mirror of
https://github.com/tradecatlabs/vibe-coding-cn.git
synced 2026-11-02 13:46:59 +00:00
4601 lines
286 KiB
JSON
4601 lines
286 KiB
JSON
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"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
|
||
"captured_at": "2026-10-07T06:03:59.363591Z",
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||
"snapshot_path": "metadata/gongfa/source-system-building-81fc7ae.txt",
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||
"snapshot_sha256": "3ef5e41bda96ba4e96f0979722c6d6c5d3838e243cf9eaf2faccc39e3672648b",
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"text_normalization": "markdown-section-trim-v1",
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"included_anchors": [
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"concept-system-building-一自顶向下先看整体再拆细节",
|
||
"concept-system-building-二自底向上先做组件再组系统",
|
||
"concept-system-building-三分而治之把复杂问题拆成小问题"
|
||
]
|
||
},
|
||
{
|
||
"id": "repo-problem-solving-81fc7ae",
|
||
"kind": "repository_document",
|
||
"path": "docs/gongfa/problem-solving.md",
|
||
"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
|
||
"captured_at": "2026-10-07T06:03:59.365887Z",
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||
"snapshot_path": "metadata/gongfa/source-problem-solving-81fc7ae.txt",
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||
"snapshot_sha256": "1a431241414583735f60426321e35573561a0280a1d716ef50ff6f8eb045aa73",
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"text_normalization": "markdown-section-trim-v1",
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||
"included_anchors": [
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"concept-problem-solving-5-搭建体系"
|
||
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||
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|
||
{
|
||
"id": "repo-recursive-self-optimizing-system-81fc7ae",
|
||
"kind": "repository_document",
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||
"path": "docs/gongfa/recursive-self-optimizing-system.md",
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||
"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
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||
"captured_at": "2026-10-07T06:03:59.367982Z",
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||
"snapshot_path": "metadata/gongfa/source-recursive-self-optimizing-system-81fc7ae.txt",
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"text_normalization": "markdown-section-trim-v1",
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"included_anchors": [
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"concept-recursive-self-optimizing-system"
|
||
]
|
||
},
|
||
{
|
||
"id": "repo-programming-dao-81fc7ae",
|
||
"kind": "repository_document",
|
||
"path": "docs/gongfa/programming-dao.md",
|
||
"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
|
||
"captured_at": "2026-10-07T06:03:59.370173Z",
|
||
"snapshot_path": "metadata/gongfa/source-programming-dao-81fc7ae.txt",
|
||
"snapshot_sha256": "d265d87953c8cae957cd41df02dcd76c91b99601703bb517661556738f73ba9e",
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"text_normalization": "markdown-section-trim-v1",
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||
"included_anchors": [
|
||
"philosophy-programming-dao-规约先于实现",
|
||
"philosophy-programming-dao-10-时间视角",
|
||
"philosophy-programming-dao-高内聚",
|
||
"philosophy-programming-dao-低耦合",
|
||
"philosophy-programming-dao-稳定接口不稳定实现",
|
||
"philosophy-programming-dao-隐藏的依赖最危险",
|
||
"philosophy-programming-dao-不变式保持世界稳定",
|
||
"philosophy-programming-dao-9-可推理性",
|
||
"philosophy-programming-dao-16-最小惊讶原则"
|
||
]
|
||
},
|
||
{
|
||
"id": "repo-software-engineering-truths-81fc7ae",
|
||
"kind": "repository_document",
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||
"path": "docs/gongfa/software-engineering-truths.md",
|
||
"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
|
||
"captured_at": "2026-10-07T09:20:36.243253Z",
|
||
"snapshot_path": "metadata/gongfa/source-software-engineering-truths-81fc7ae.txt",
|
||
"snapshot_sha256": "e08b9a728825dc175a0bc68d22e2d90cb43ef9f368cd373368ca6d456ff329f3",
|
||
"text_normalization": "markdown-section-trim-v1",
|
||
"included_anchors": [
|
||
"philosophy-software-engineering-truths-5-重复代码不一定坏错误抽象更坏",
|
||
"philosophy-software-engineering-truths-14-技术债不是罪假装没有技术债才是罪",
|
||
"philosophy-software-engineering-truths-16-性能问题要靠测量不要靠感觉",
|
||
"philosophy-software-engineering-truths-18-稳定不是没有故障而是故障可控",
|
||
"philosophy-software-engineering-truths-23-文档不是装饰品"
|
||
]
|
||
},
|
||
{
|
||
"id": "repo-dataset-first-data-service-81fc7ae",
|
||
"kind": "repository_document",
|
||
"path": "docs/gongfa/dataset-first-data-service.md",
|
||
"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
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||
"captured_at": "2026-10-07T09:20:36.245521Z",
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||
"snapshot_path": "metadata/gongfa/source-dataset-first-data-service-81fc7ae.txt",
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"snapshot_sha256": "871b29acdde5879710c512890d345d91086a369db6f9fd84a5755958011648b9",
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"text_normalization": "markdown-section-trim-v1",
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||
"included_anchors": [
|
||
"reference-engineering-practice-7-dataset-first-数据服务结构",
|
||
"reference-engineering-practice-新建数据服务流程",
|
||
"reference-engineering-practice-外部源码接入流程"
|
||
]
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||
},
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||
{
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||
"id": "repo-development-experience-81fc7ae",
|
||
"kind": "repository_document",
|
||
"path": "docs/gongfa/development-experience.md",
|
||
"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
|
||
"captured_at": "2026-10-07T09:20:36.247888Z",
|
||
"snapshot_path": "metadata/gongfa/source-development-experience-81fc7ae.txt",
|
||
"snapshot_sha256": "d1437549b3232085d678b74793c6430903ba72913679c0712cd1b9773b2994f0",
|
||
"text_normalization": "markdown-section-trim-v1",
|
||
"included_anchors": [
|
||
"reference-engineering-practice-1-变量名维护方案",
|
||
"reference-engineering-practice-33-消费端-生产端-状态变量-变换函数",
|
||
"reference-engineering-practice-34-并发concurrency",
|
||
"reference-engineering-practice-54-dry-原则不要重复"
|
||
]
|
||
},
|
||
{
|
||
"id": "repo-quality-gates-and-pitfalls-81fc7ae",
|
||
"kind": "repository_document",
|
||
"path": "docs/gongfa/quality-gates-and-pitfalls.md",
|
||
"git_revision": "81fc7ae599da7b7b0ce02ea0b069a2c88d288a4e",
|
||
"captured_at": "2026-10-07T09:20:36.250898Z",
|
||
"snapshot_path": "metadata/gongfa/source-quality-gates-and-pitfalls-81fc7ae.txt",
|
||
"snapshot_sha256": "bf0f2ba0af22ff3e806695f42c23655ab957496faf01f4148f7a0ffa85840e0a",
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||
"text_normalization": "markdown-section-trim-v1",
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||
"included_anchors": [
|
||
"reference-engineering-practice-三新手常见复杂度误区",
|
||
"reference-engineering-practice-六缓存误用"
|
||
]
|
||
}
|
||
],
|
||
"gongfa": [
|
||
{
|
||
"id": "gongfa-compositional-description",
|
||
"name": "组合描述模型",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "explanatory-content",
|
||
"content": {
|
||
"original_text": "组合描述模型，可以看成一套理解世界如何“既保持又变化”的基础框架：对象是我们能够指认和追踪的相对稳定单位，状态是对象在某一时刻或条件下的存在方式，快照是对状态的静态截取，多个快照按时间或规则排列就形成序列，而序列作为动态整体展开出来就是过程；过程之所以能从一个状态走向另一个状态，是因为背后有某种变换机制。可是一旦讨论变化，就必然遇到两个问题：它为什么仍然算“同一个”，又为什么已经变得“不同”。因此，同一用来保证追踪和识别的连续性，差异用来揭示变化、比较和意义的生成，而关系则把对象、状态、过程和差异放进更大的结构网络中，使它们真正获得意义。换句话说，这组概念不是零散术语，而是一种从静态存在走向动态生成、从孤立对象走向关系结构的认知语法：对象让世界可被指认，状态让世界可被描述，快照和序列让世界可被记录，过程和变换让世界可被解释，同一、差异和关系则让世界可被理解。",
|
||
"text_sha256": "f9f7bfd64ba4484aae25d1c51c961ed7ab2b44ce2e2276dbc5e3476faf8d4649",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收开篇关系解释框架，不收全文步骤；原文内部本体语言不替代项目固定的BFO主干。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "以对象、状态、快照、序列、过程、变换、同一、差异及关系解释变化中的连续性。",
|
||
"problem": "如何追踪变化中的对象，并描述其连续性、差异与关系。",
|
||
"mechanism": "对象与状态可被记录为快照和序列，变换使过程展开，同一/差异判定置于关系网络中。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [],
|
||
"questions": [
|
||
"它为什么仍然算“同一个”？",
|
||
"它为什么已经变得“不同”？"
|
||
],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"expected_outcomes"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-compositional-description-81fc7ae",
|
||
"heading_id": "philosophy-compositional-description-model",
|
||
"selection": "markdown-first-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-network-effects",
|
||
"name": "网络效应",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "explanatory-content",
|
||
"content": {
|
||
"original_text": "一个系统、工具、标准或平台的价值，会随着使用者、连接节点、互操作对象和生态资产的增加而上升。",
|
||
"text_sha256": "a40073db5c1a98d5392dc472ee59ccb5712f07e90f23684965bd5d4e9f64c8a6",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收核心解释关系，不收同节选型步骤；原文主张不保证每个网络或具体场景都有正效应。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "使用者、连接节点、互操作对象及生态资产增加与网络价值上升的解释关系。",
|
||
"problem": "使用者、节点、互操作对象与生态资产的增加如何关联网络价值。",
|
||
"mechanism": "更多使用者、节点、互操作对象和生态资产与系统价值上升相联系。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-thinking-models-81fc7ae",
|
||
"heading_id": "philosophy-thinking-models-网络效应",
|
||
"selection": "markdown-first-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-occams-razor",
|
||
"name": "奥卡姆剃刀",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "在能解释同一现象、满足同一验收标准的多个方案中，优先选择假设更少、结构更短、依赖更少、状态更少的方案。\n\n它不是“越简单越好”，而是：\n\n> 在不牺牲关键约束的前提下，少引入不必要实体。",
|
||
"text_sha256": "6003a6c40e6602423049b5b963a4a3f741cbe224f98e3090d85f1df76a8753b4",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收同解释/验收与关键约束下的简化准则，不收后续四步操作版；不构成删除防护的授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在相同解释、验收及关键约束下优先选择更少假设、结构、依赖和状态的方案。",
|
||
"problem": "多个满足同一目标的方案如何取舍。",
|
||
"mechanism": "在不牺牲关键约束的前提下少引入不必要实体。",
|
||
"conditions": [
|
||
"能解释同一现象、满足同一验收标准。",
|
||
"不牺牲关键约束。"
|
||
],
|
||
"limitations": [
|
||
"不是无条件的“越简单越好”。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-thinking-models-81fc7ae",
|
||
"heading_id": "philosophy-thinking-models-奥卡姆剃刀",
|
||
"selection": "markdown-prefix",
|
||
"stop_before": "适合："
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-glue-principle",
|
||
"name": "胶水原则",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "“胶水原则”是最高级别的“不重复造轮子”：能复用成熟方案就不自研底层能力，只写用于连接、编排、适配、隔离和表达业务逻辑的胶水代码。\n\n默认答案不是“我来实现”，而是：\n\n> 有没有官方能力、平台能力、事实标准、主流框架、成熟库、稳定工具、GitHub 开源仓库或内部公共能力可以直接复用？\n\n当成熟方案能以可接受的成本、风险和复杂度可靠满足需求时，它就是默认答案；自研不是默认选项，而是需要证明合理性的例外选项。",
|
||
"text_sha256": "e761c7f0a020ec6e5fd12ebfa2496fc869f7dd29a982fc849a9c0b849d8ef44f",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收成熟能力优先准则及自研例外，不收整篇拼好码交付流程。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "成熟能力能以可接受代价可靠满足需求时默认复用，自研核心须证明合理性。",
|
||
"problem": "何时复用、何时自研通用底层能力。",
|
||
"mechanism": "先查现成能力，只写必要连接、编排、适配、隔离和业务表达。",
|
||
"conditions": [
|
||
"成熟方案能以可接受的成本、风险和复杂度可靠满足需求。"
|
||
],
|
||
"limitations": [
|
||
"自研不是默认选项，但可以成为有合理依据的例外。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [
|
||
"有没有官方能力、平台能力、事实标准、主流框架、成熟库、稳定工具、GitHub开源仓库或内部公共能力可以直接复用？"
|
||
],
|
||
"missing_fields": [
|
||
"expected_outcomes"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-glue-coding-81fc7ae",
|
||
"heading_id": "concept-glue-coding-胶水原则",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-first-principles",
|
||
"name": "第一性原理",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "把问题拆到不能再依赖既有说法、行业惯例和二手结论的基础事实，再从基础事实重新推导方案。\n\n适合：\n\n- 需求被经验做法绑架时。\n- 方案复杂但没人能解释为什么必须这样时。\n- 要判断一个“默认方案”是否真的成立时。\n\n使用方式：\n\n1. 写出当前结论。\n2. 逐条追问：这个结论依赖哪些前提？\n3. 区分事实、假设、偏好、惯例。\n4. 保留不可再拆的事实约束。\n5. 从事实约束重新推导最小可行路径。\n\n在 Vibe Coding 中，它常用于防止 AI 沿着常见套路生成过度复杂方案。",
|
||
"text_sha256": "e92e76bdb2c16e50f9303929b3383074564aaa4b4728859b3deba3750790e71a",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "收录当前仓内五步操作版，不宣称覆盖第一性原理的全部历史表述。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "审查结论的前提，区分事实、假设、偏好与惯例，从事实约束重推最小路径。",
|
||
"problem": "经验惯例绑架需求，或复杂方案无法解释其必要性。",
|
||
"mechanism": "写结论→审查前提→区分事实/假设/偏好/惯例→保留事实约束→重推路径。",
|
||
"conditions": [
|
||
"需求受经验做法影响，或需要核对默认方案是否成立。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"基于事实约束的最小可行路径。"
|
||
],
|
||
"questions": [
|
||
"这个结论依赖哪些前提？"
|
||
],
|
||
"missing_fields": [
|
||
"limitations"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-thinking-models-81fc7ae",
|
||
"heading_id": "philosophy-thinking-models-第一性原理",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-thought-experiment",
|
||
"name": "思想实验",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "在现实执行之前，先构造一个简化但关键约束完整的假想场景，用来检验概念、规则、边界和后果。\n\n适合：\n\n- 方案还没写代码，但要判断是否会崩。\n- 现实试错成本高。\n- 需要测试一个原则在极端情况下是否仍成立。\n\n使用方式：\n\n1. 设定一个最小场景。\n2. 保留关键约束，删除无关细节。\n3. 推演正常路径、边界路径、极端路径。\n4. 看结论是否自洽，是否出现反例。\n\n示例问题：\n\n- 如果用户完全零基础，这份教程还能不能走通？\n- 如果 AI 输出错了，门禁能不能挡住？\n- 如果某个外部仓库不可用，系统是否还能替换？",
|
||
"text_sha256": "b71449ec04a516c2352599fcc0c4a514c6175731f5ea087b3245eade73e16ded",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "收当前独立模型节；适用场景不代表充分成立条件，原文未明示的失效边界留空。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在现实执行之前，先构造一个简化但关键约束完整的假想场景，用来检验概念、规则、边界和后果。",
|
||
"problem": "如何在现实执行前检验概念、规则、边界和后果，尤其在试错成本高时。",
|
||
"mechanism": "设定最小场景→保留关键约束、去掉无关细节→推演正常/边界/极端路径→检查自洽性与反例。",
|
||
"conditions": [
|
||
"方案还没写代码，但要判断是否会崩。",
|
||
"现实试错成本高。",
|
||
"需要测试一个原则在极端情况下是否仍成立。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"最小假想场景及正常、边界、极端路径的推演。",
|
||
"自洽性判断及发现的反例。"
|
||
],
|
||
"questions": [
|
||
"如果用户完全零基础，这份教程还能不能走通？",
|
||
"如果AI输出错了，门禁能不能挡住？",
|
||
"如果某个外部仓库不可用，系统是否还能替换？"
|
||
],
|
||
"missing_fields": [
|
||
"limitations"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-thinking-models-81fc7ae",
|
||
"heading_id": "philosophy-thinking-models-思想实验",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-reverse-thinking",
|
||
"name": "逆向思维",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "从失败、反例、风险和终局倒推当前行动，先问“怎样一定会失败”，再反推避免失败的约束。\n\n适合：\n\n- 做质量门禁。\n- 做架构风险分析。\n- 判断一个计划是否只是看起来完整。\n\n使用方式：\n\n1. 写出最坏结果。\n2. 列出导致最坏结果的路径。\n3. 找出其中可被提前检测或阻断的环节。\n4. 把阻断点转成测试、CI、脚本、schema、清单或人工复核。\n\n在 AI 协作中，逆向思维尤其重要：不要只问“AI 怎么完成任务”，还要问“AI 会怎样糊弄、幻觉、漏测、误删、过度实现”。",
|
||
"text_sha256": "00d4217a7a7a70c57e118f23008da53af03a16daf7c4dddd9134571b4e8fdf23",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "收当前独立模型节；适用场景不代表充分成立条件，原文未明示的失效边界留空。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "从失败、反例、风险和终局倒推当前行动，先问“怎样一定会失败”，再反推避免失败的约束。",
|
||
"problem": "如何从失败、反例、风险与最坏终局倒推出当前可检测、可阻断的约束。",
|
||
"mechanism": "写出最坏结果→列出失败路径→识别提前检测/阻断环节→将阻断点固化为测试、CI、脚本、schema、清单或人工复核。",
|
||
"conditions": [
|
||
"做质量门禁。",
|
||
"做架构风险分析。",
|
||
"判断一个计划是否只是看起来完整。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"最坏结果及导致失败的路径清单。",
|
||
"提前检测或阻断点及对应测试、脚本、清单或复核。"
|
||
],
|
||
"questions": [
|
||
"怎样一定会失败？",
|
||
"AI会怎样糊弄、幻觉、漏测、误删、过度实现？"
|
||
],
|
||
"missing_fields": [
|
||
"limitations"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-thinking-models-81fc7ae",
|
||
"heading_id": "philosophy-thinking-models-逆向思维",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-multi-order-thinking",
|
||
"name": "多阶思维",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "多阶思维继承二阶思维，但不止停在“行动之后会发生什么”，而是继续追踪后续反应、反馈、反身性和系统性连锁。\n\n二阶思维关注：\n\n> 我的行动会带来什么后果？\n\n多阶思维继续追问：\n\n> 后果会改变参与者行为吗？\n> 行为改变后会反过来改变系统吗？\n> 系统改变后，原来的策略还成立吗？\n\n适合：\n\n- 平台规则、社区治理、开源协作、SEO/GEO、激励机制。\n- 任何会让参与者根据结果调整行为的系统。\n\n使用方式：\n\n1. 一阶：行动本身会产生什么直接结果。\n2. 二阶：直接结果会触发什么间接后果。\n3. 三阶：参与者看到后果后会如何改变行为。\n4. 反身性：行为改变会如何反过来改变系统条件。\n5. 收敛：原策略是否需要调整、加门禁或保留回滚路径。\n\n在 GEO 中，多阶思维意味着：不是只写关键词，而是让内容被 AI 引用后继续强化项目定位、用户行为和外部分发路径。",
|
||
"text_sha256": "490b2421a035dcefafffa690516bc6100d3d2ffb4633b6febab87e2ed7f01186",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "收当前独立模型节；适用场景不代表充分成立条件，原文未明示的失效边界留空。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "多阶思维继承二阶思维，但不止停在“行动之后会发生什么”，而是继续追踪后续反应、反馈、反身性和系统性连锁。",
|
||
"problem": "行动后果、参与者行为、反馈与反身性如何继续改变系统及原策略。",
|
||
"mechanism": "分析直接结果→追踪间接后果→判断参与者行为变化→分析反身性反馈→调整策略、门禁或回滚路径。",
|
||
"conditions": [
|
||
"平台规则、社区治理、开源协作、SEO/GEO、激励机制。",
|
||
"任何会让参与者根据结果调整行为的系统。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"直接、间接、行为变化和反身性影响的多阶分析。",
|
||
"策略调整、门禁或回滚路径判断。"
|
||
],
|
||
"questions": [
|
||
"我的行动会带来什么后果？",
|
||
"后果会改变参与者行为吗？",
|
||
"行为改变后会反过来改变系统吗？",
|
||
"系统改变后，原来的策略还成立吗？"
|
||
],
|
||
"missing_fields": [
|
||
"limitations"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-thinking-models-81fc7ae",
|
||
"heading_id": "philosophy-thinking-models-多阶思维",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-state-space-thinking",
|
||
"name": "状态空间思维模型",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "状态空间思维模型把“状态、变化、序列、决策树、多元宇宙”整合在一起，用来分析一个系统从当前状态可能走向哪些未来状态。\n\n它关注的不是单一路径，而是：\n\n> 当前在哪个状态？\n> 可以采取哪些动作？\n> 每个动作会把系统推向哪些状态？\n> 哪些路径可逆，哪些路径不可逆？\n> 哪些未来状态更稳定、更可验证、更可回滚？\n\n核心元素：\n\n- 当前状态：系统此刻的配置、资源、约束和风险。\n- 动作集合：现在可以执行的操作。\n- 状态转移：动作如何改变状态。\n- 决策树：不同动作展开出的路径分支。\n- 多元宇宙：所有可能路径形成的未来状态集合。\n- 序列：实际被选择并发生的一条路径。\n- 收敛条件：哪些状态算成功、失败或需要回滚。\n\n使用方式：\n\n1. 描述当前状态，不急着下结论。\n2. 列出可行动作，而不是只看默认动作。\n3. 为每个动作写出可能的后续状态。\n4. 标记不可逆动作、高风险动作和可回滚动作。\n5. 选择能保留最多未来选择权、同时最接近目标的路径。\n6. 用检查点、测试、提交、备份和 CI 把路径变得可回退。\n\n在工程实践中，状态空间思维能防止“一步走死”：重要操作前先建立检查点，优先走可验证、可回滚、可分阶段收敛的路径。",
|
||
"text_sha256": "cafcf77b67302d17563e03a6e0017dc3cf48e3432af5174079f21b93fa3dc2ba",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "收当前独立模型节；适用场景不代表充分成立条件，原文未明示的失效边界留空。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "状态空间思维模型把“状态、变化、序列、决策树、多元宇宙”整合在一起，用来分析一个系统从当前状态可能走向哪些未来状态。",
|
||
"problem": "系统从当前状态可沿哪些动作进入哪些未来状态，哪些路径可逆或可回滚。",
|
||
"mechanism": "描述当前状态→列出动作→展开后续状态→标记不可逆/高风险/可回滚路径→选择保留选择权且接近目标的路径→建立检查点与回退手段。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"当前状态、可行动作与可能后续状态的描述。",
|
||
"不可逆、高风险、可回滚路径标记及检查点选择。"
|
||
],
|
||
"questions": [
|
||
"当前在哪个状态？",
|
||
"可以采取哪些动作？",
|
||
"每个动作会把系统推向哪些状态？",
|
||
"哪些路径可逆，哪些路径不可逆？",
|
||
"哪些未来状态更稳定、更可验证、更可回滚？"
|
||
],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-thinking-models-81fc7ae",
|
||
"heading_id": "philosophy-thinking-models-状态空间思维模型",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-phenomenological-reduction",
|
||
"name": "现象学还原（悬置假设）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：需求含糊、模型脑补、Bug难复现时，先把\"解释/偏好\"清零，回到可观察事实与可复现结构。\n\n**落地动作**：\n\n- 先写四件套：现象(实际) / 意图(期望) / 情境(环境约束) / 边界(明确不做)\n- 输出最小可复现体 MRE：最小输入 + 最小脚本 + 复现步骤 + 预期vs实际\n- 把抽象词降维：快/稳/好用 → 指标&验收用例\n\n**Python工具**：`pytest`（MRE脚本）、日志、最小数据样例\n\n**提示词**：\n\n```text\n先做现象学还原：不要推测原因。输出：现象/意图/情境/边界/未确定项/MRE；然后再给最小修复与测试。\n```",
|
||
"text_sha256": "95e68a224625233d5a3acb6f97974ead0c576737ba427264d919c84505739c17",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：需求含糊、模型脑补、Bug难复现时，先把\"解释/偏好\"清零，回到可观察事实与可复现结构。",
|
||
"problem": "需求含糊、模型脑补、Bug难复现时，先把\"解释/偏好\"清零，回到可观察事实与可复现结构。",
|
||
"mechanism": "- 先写四件套：现象(实际) / 意图(期望) / 情境(环境约束) / 边界(明确不做)\n- 输出最小可复现体 MRE：最小输入 + 最小脚本 + 复现步骤 + 预期vs实际\n- 把抽象词降维：快/稳/好用 → 指标&验收用例",
|
||
"conditions": [
|
||
"适用情境：需求含糊、模型脑补或Bug难复现。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"现象、意图、情境与边界记录。",
|
||
"最小可复现体（MRE）及预期/实际对照。",
|
||
"抽象需求对应的指标与验收用例。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-1-现象学还原悬置假设",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-dialectical-iteration",
|
||
"name": "正反合（三段迭代）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：把一次性\"写到完美\"替换为可控三轮：快速可用 → 反例打脸 → 收敛为工程版本。\n\n**落地动作**：\n\n- **正**：只做 main path，让它跑通\n- **反**：列失败模式（边界/空值/并发/权限/超时/性能），用测试与基准逼出反例\n- **合**：重构接口/收敛依赖/补文档与回归，形成下一轮稳定起点\n\n**Python工具**：`pytest` + `hypothesis` + `ruff/black` + profiling/benchmark\n\n**提示词**：\n\n```text\n按正反合输出：1)最小可运行实现 2)反例与失败模式+测试 3)综合后的重构方案与最终代码。\n```",
|
||
"text_sha256": "3c2de763fc155c41569ab7f11acabe5bfeb9b0817a73ab1e494ad2cee1099d07",
|
||
"claim_status": "source_statement",
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||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：把一次性\"写到完美\"替换为可控三轮：快速可用 → 反例打脸 → 收敛为工程版本。",
|
||
"problem": "把一次性\"写到完美\"替换为可控三轮：快速可用 → 反例打脸 → 收敛为工程版本。",
|
||
"mechanism": "- **正**：只做 main path，让它跑通\n- **反**：列失败模式（边界/空值/并发/权限/超时/性能），用测试与基准逼出反例\n- **合**：重构接口/收敛依赖/补文档与回归，形成下一轮稳定起点",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"可运行主路径的最小实现。",
|
||
"失败模式、反例测试与基准。",
|
||
"收敛接口、依赖、文档与回归后的稳定起点。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-2-正反合三段迭代",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-falsification",
|
||
"name": "可证伪主义（波普尔）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：把\"看起来对\"变成\"暂时无法证伪\"；显著降低隐藏 bug。\n\n**落地动作**：\n\n- 每个关键断言都要配一个能让它失败的测试（边界/随机/反例）\n- 优先性质测试而非只写示例测试\n\n**Python工具**：`hypothesis`（性质/模糊）、`pytest`\n\n**提示词**：\n\n```text\n为该实现列出 5 个可证伪点，并为每个点写一个最小测试（优先 Hypothesis 性质测试）。\n```",
|
||
"text_sha256": "411481762ecb2c3ec1537e56314e0d16f154c58484bc3514981842437dbf2937",
|
||
"claim_status": "source_statement",
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||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：把\"看起来对\"变成\"暂时无法证伪\"；显著降低隐藏 bug。",
|
||
"problem": "把\"看起来对\"变成\"暂时无法证伪\"；显著降低隐藏 bug。",
|
||
"mechanism": "- 每个关键断言都要配一个能让它失败的测试（边界/随机/反例）\n- 优先性质测试而非只写示例测试",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"关键断言对应的可失败测试，优先性质测试。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-3-可证伪主义波普尔",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-lightweight-formal-methods",
|
||
"name": "形式化方法（轻量形式化）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：减少非法状态、约束模型输出、让行为可检查可累积。\n\n**落地动作**：\n\n- **先规格**：类型 + schema + 不变式 + 错误集合（异常或 error object）+ 复杂度约束（可选）\n- **再检查器**：类型检查 + 运行时校验 + 断言/契约 + 性质测试\n- **最后实现**：逐条映射规格（谁保证哪条约束）\n\n**Python工具**：\n\n- `typing`（Literal/NewType/Protocol/TypedDict/Annotated）\n- `pyright/mypy`\n- `pydantic/msgspec`（输入输出校验）\n- `assert` / `icontract` / `deal`\n- `pytest` + `hypothesis`\n\n**提示词**：\n\n```text\n先输出形式化规格（类型/schema/不变式/错误语义），再给至少 3 条 Hypothesis 性质测试，最后写实现并逐条说明满足关系。\n```",
|
||
"text_sha256": "bee84e77c89b64a2aefb1f6fff50c9c181028905364860b295356f1f4d47c13c",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：减少非法状态、约束模型输出、让行为可检查可累积。",
|
||
"problem": "减少非法状态、约束模型输出、让行为可检查可累积。",
|
||
"mechanism": "- **先规格**：类型 + schema + 不变式 + 错误集合（异常或 error object）+ 复杂度约束（可选）\n- **再检查器**：类型检查 + 运行时校验 + 断言/契约 + 性质测试\n- **最后实现**：逐条映射规格（谁保证哪条约束）",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"类型、schema、不变式与错误语义规格。",
|
||
"类型/运行时校验、契约和性质测试。",
|
||
"实现与规格的满足关系说明。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-4-形式化方法轻量形式化",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-toolbox-occams-razor",
|
||
"name": "奥卡姆剃刀（工具箱操作版）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：避免模型引入不必要框架/抽象；提升可维护性与迭代速度。\n\n**落地动作**：\n\n- 要求两套方案：常规版 vs 简化版；以测试为准删复杂度\n- 优先标准库、减少依赖、减少可变状态、减少层级\n\n**Python工具**：`ruff`（复杂度/风格）、依赖审计（requirements最小化）\n\n**提示词**：\n\n```text\n在满足全部测试与验收的前提下，把实现复杂度删掉 30%：减少依赖、状态和抽象层，并解释删减理由。\n```",
|
||
"text_sha256": "cb2ea2e0c4a4e675b0995da687724011dac1959d566ad7a1c6f1d9c218914a0a",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：避免模型引入不必要框架/抽象；提升可维护性与迭代速度。",
|
||
"problem": "避免模型引入不必要框架/抽象；提升可维护性与迭代速度。",
|
||
"mechanism": "- 要求两套方案：常规版 vs 简化版；以测试为准删复杂度\n- 优先标准库、减少依赖、减少可变状态、减少层级",
|
||
"conditions": [
|
||
"简化必须满足全部测试与验收；以测试为准删复杂度。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"常规与简化方案的对照。",
|
||
"保持测试与验收约束的简化实现及删减理由。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-5-奥卡姆剃刀最小复杂度",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-pragmatic-optimization",
|
||
"name": "实用主义（以指标为准）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：避免\"优化方向漂移\"；每轮明确一个可量化目标。\n\n**落地动作**：\n\n- 先定义成功指标（P95延迟/错误率/成本/内存/可维护性）\n- 每轮只优化一个指标；其余保持不退化（用基准/回归锁住）\n\n**Python工具**：`pytest-benchmark` 或简单计时；日志与指标；回归测试\n\n**提示词**：\n\n```text\n把需求转成指标与验收阈值，并给出测量方法；本轮只优化 X 指标，保证其它指标不退化。\n```",
|
||
"text_sha256": "4d363457999e0d2f1e2773e0f49e98fe2b15147c91b35cdd069d57e82dd1dfcf",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：避免\"优化方向漂移\"；每轮明确一个可量化目标。",
|
||
"problem": "避免\"优化方向漂移\"；每轮明确一个可量化目标。",
|
||
"mechanism": "- 先定义成功指标（P95延迟/错误率/成本/内存/可维护性）\n- 每轮只优化一个指标；其余保持不退化（用基准/回归锁住）",
|
||
"conditions": [
|
||
"先定义成功指标、验收阈值与测量方法；每轮优化一个指标，其他指标以基准/回归约束不退化。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"成功指标、验收阈值与测量方法。",
|
||
"本轮指定指标的优化及其他指标不退化的回归约束。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-6-实用主义以指标为准",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-systems-boundary-feedback",
|
||
"name": "系统论/整体论（边界与反馈回路）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：复杂系统容易在耦合点失控；缩短反馈回路提效最大。\n\n**落地动作**：\n\n- 先画数据流/依赖边界：I/O 放边缘，核心逻辑保持纯函数\n- 优先解耦高耦合点；把慢依赖换成桩/模拟以加速测试\n\n**Python工具**：依赖注入（轻量）、`pytest fixtures`、纯函数设计\n\n**提示词**：\n\n```text\n画出数据流与依赖边界，指出最高耦合点与最短反馈回路改造方案；给出可测试的纯函数核心与 I/O 适配层。\n```\n\n**扩展阅读**：\n\n- [控制论与科学方法论](#控制论与科学方法论) - 用“可能性空间/反馈/信息/黑箱/可证伪”解释从试错到收敛的机制",
|
||
"text_sha256": "72cbec4e59f98ee44b9777113ed7aa97646441f30f1be47f61f72bdca56f64c0",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：复杂系统容易在耦合点失控；缩短反馈回路提效最大。",
|
||
"problem": "复杂系统容易在耦合点失控；缩短反馈回路提效最大。",
|
||
"mechanism": "- 先画数据流/依赖边界：I/O 放边缘，核心逻辑保持纯函数\n- 优先解耦高耦合点；把慢依赖换成桩/模拟以加速测试",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"数据流、依赖边界与高耦合点分析。",
|
||
"可测试纯函数核心及I/O适配方案。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-7-系统论整体论边界与反馈回路",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-hermeneutic-clarification",
|
||
"name": "诠释学（语境澄清）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：需求文本有歧义，模型与人对同一词理解不同。\n\n**落地动作**：\n\n- 先复述需求 + 歧义清单 + 默认选择（必须显式）\n- 默认选择写入 docstring/README/类型定义\n\n**Python工具**：docstring、类型与 schema 固化默认\n\n**提示词**：\n\n```text\n先复述需求并列出所有歧义点；对每个歧义给默认策略与理由；确认后再写实现与测试。\n```",
|
||
"text_sha256": "f2706f772cea6476b19dd708843b3fbc9ef2d2ba56798137ddb37aba91dd0ef7",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：需求文本有歧义，模型与人对同一词理解不同。",
|
||
"problem": "需求文本有歧义，模型与人对同一词理解不同。",
|
||
"mechanism": "- 先复述需求 + 歧义清单 + 默认选择（必须显式）\n- 默认选择写入 docstring/README/类型定义",
|
||
"conditions": [
|
||
"适用情境：需求文本有歧义，模型与人对同一词理解不同。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"需求复述、歧义清单及显式默认选择。",
|
||
"进入文档、类型或schema的默认策略。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-8-诠释学语境澄清",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-steelmanning",
|
||
"name": "\"钢人化\"原则（最强版本理解）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：减少无效争论/误解；让重构建议更贴近原意图。\n\n**落地动作**：\n\n- 先把现有方案表达成最强版本（目标、约束、权衡）\n- 再提出改进（保留其优势，指出代价）\n\n**Python工具**：PR描述结构化（优点/风险/替代方案）\n\n**提示词**：\n\n```text\n先钢人化现有实现：列出它的最佳解释与优点；再给改进方案并明确代价与风险。\n```",
|
||
"text_sha256": "fdb81961c528388e4abff074033b3b8259cc3cd3ccf95f6a63afdf493a88ef5e",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：减少无效争论/误解；让重构建议更贴近原意图。",
|
||
"problem": "减少无效争论/误解；让重构建议更贴近原意图。",
|
||
"mechanism": "- 先把现有方案表达成最强版本（目标、约束、权衡）\n- 再提出改进（保留其优势，指出代价）",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"现有方案的最强解释及优点。",
|
||
"保留优势、说明代价和风险的改进方案。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-9-钢人化原则最强版本理解",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-reversible-decisions",
|
||
"name": "决策论/机会成本（可逆优先）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：避免过早做不可逆技术决策（换框架/改数据模型）。\n\n**落地动作**：\n\n- 标注决策：可逆 vs 不可逆；优先做可逆高价值项\n- 先写接口+测试桩+适配层，延后绑定外部系统\n\n**Python工具**：抽象边界、adapter、in-memory 实现\n\n**提示词**：\n\n```text\n把方案拆成可逆/不可逆决策；先给可逆路径的 MVP，实现通过测试；不可逆部分只给接口与占位实现。\n```",
|
||
"text_sha256": "144eeadd97fcb638796da5943cd2396920d750ecbf0d1d0fc031f15563cd9f8b",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：避免过早做不可逆技术决策（换框架/改数据模型）。",
|
||
"problem": "避免过早做不可逆技术决策（换框架/改数据模型）。",
|
||
"mechanism": "- 标注决策：可逆 vs 不可逆；优先做可逆高价值项\n- 先写接口+测试桩+适配层，延后绑定外部系统",
|
||
"conditions": [
|
||
"适用情境：面临换框架、改数据模型等不可逆技术决策。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"可逆/不可逆决策清单。",
|
||
"可逆高价值路径的接口、测试桩、适配层和MVP。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-10-决策论机会成本可逆优先",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-counterfactual-testing",
|
||
"name": "反事实推理（Counterfactuals）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：系统性覆盖异常路径，降低线上事故。\n\n**落地动作**：\n\n- 问\"如果 X 不成立会怎样\"：超时、乱序、重复、空值、弱网、权限缺失、时钟漂移\n- 把反事实转成测试矩阵与降级策略\n\n**Python工具**：`pytest` 参数化、`hypothesis` 生成器、超时与重试控制\n\n**提示词**：\n\n```text\n列出 15 个反事实场景并按风险排序；为 Top5 写测试与降级/错误语义。\n```",
|
||
"text_sha256": "e57e942bf45b08954531e3b0be0e11580237d5017f773e5c024b7d1b4b03dce0",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：系统性覆盖异常路径，降低线上事故。",
|
||
"problem": "系统性覆盖异常路径，降低线上事故。",
|
||
"mechanism": "- 问\"如果 X 不成立会怎样\"：超时、乱序、重复、空值、弱网、权限缺失、时钟漂移\n- 把反事实转成测试矩阵与降级策略",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"异常条件的反事实场景矩阵。",
|
||
"风险对应的测试、降级与错误语义。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-11-反事实推理counterfactuals",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-abductive-debugging",
|
||
"name": "溯因排错法（仓内操作版）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：debug/性能退化时，比穷举更快定位\"最可能原因\"。\n\n**落地动作**：\n\n- 列候选原因 → 为每个原因写最便宜的区分性实验（日志点/开关/最小基准）\n- 用证据淘汰而不是凭感觉改代码\n\n**Python工具**：结构化日志、trace、最小 benchmark、feature flag\n\n**提示词**：\n\n```text\n给出候选原因列表，并为每个原因提供一个最低成本、最高区分度的验证实验与预期观察。\n```",
|
||
"text_sha256": "a9b821e8da4740b609d4252e935dbd7859e9393663e922c5a0ed6904be03682b",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：debug/性能退化时，比穷举更快定位\"最可能原因\"。",
|
||
"problem": "debug/性能退化时，比穷举更快定位\"最可能原因\"。",
|
||
"mechanism": "- 列候选原因 → 为每个原因写最便宜的区分性实验（日志点/开关/最小基准）\n- 用证据淘汰而不是凭感觉改代码",
|
||
"conditions": [
|
||
"适用情境：debug或性能退化的原因排查。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"候选原因及低成本、高区分度的实验与预期观察。",
|
||
"用证据淘汰原因的排查结果。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-12-溯因推理abduction最佳解释",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-bayesian-hypothesis-updating",
|
||
"name": "贝叶斯式信念更新（与溯因配合）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：在不确定下理性分配排查时间。\n\n**落地动作**：\n\n- 给假设先验（高/中/低）→ 实验后更新后验排序\n- 只对后验最高的 1-2 个假设投入修改成本\n\n**Python工具**：同 12；加一张\"假设-证据\"表\n\n**提示词**：\n\n```text\n按先验排序原因；给最信息增益实验；根据可能结果更新排序并给下一步。\n```",
|
||
"text_sha256": "f0fc67cb532e476080cdfbf0d830108ec39418fbf35328f45a65de2d0596a37e",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：在不确定下理性分配排查时间。",
|
||
"problem": "在不确定下理性分配排查时间。",
|
||
"mechanism": "- 给假设先验（高/中/低）→ 实验后更新后验排序\n- 只对后验最高的 1-2 个假设投入修改成本",
|
||
"conditions": [
|
||
"适用情境：在不确定条件下分配排查时间。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"假设先验、证据与后验排序记录。",
|
||
"按更新排序选择的下一步实验或修改方向。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-13-贝叶斯式信念更新与溯因配合",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-reflective-equilibrium",
|
||
"name": "反思平衡（Reflective equilibrium）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：当用例、原则、约束冲突时收敛规范（尤其 API 语义、错误处理、兼容性）。\n\n**落地动作**：\n\n- 三层对齐：具体用例 ↔ 一般原则 ↔ 系统约束\n- 用测试固化：回归用例（具体判断）+ 性质测试（原则）\n\n**Python工具**：`pytest` + `hypothesis`；规范文档（错误模型/幂等语义）\n\n**提示词**：\n\n```text\n列出用例/原则/约束三集，指出冲突点；给两轮调整方案，每轮说明要改哪些用例、原则或实现以达成一致。\n```",
|
||
"text_sha256": "f0fce3eba9822fb208e58243272994309129d7fc01d3b605fcc16247403474ef",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：当用例、原则、约束冲突时收敛规范（尤其 API 语义、错误处理、兼容性）。",
|
||
"problem": "当用例、原则、约束冲突时收敛规范（尤其 API 语义、错误处理、兼容性）。",
|
||
"mechanism": "- 三层对齐：具体用例 ↔ 一般原则 ↔ 系统约束\n- 用测试固化：回归用例（具体判断）+ 性质测试（原则）",
|
||
"conditions": [
|
||
"适用情境：用例、原则与约束冲突，需要收敛规范，尤其API语义、错误处理与兼容性。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"用例、原则、约束三集及冲突点。",
|
||
"调整方案与回归/性质测试。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-14-反思平衡reflective-equilibrium",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-concept-engineering",
|
||
"name": "概念分析 / 概念工程",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：防止术语漂移导致返工；把领域概念固化进代码。\n\n**落地动作**：\n\n- 概念表：术语/定义/边界/不变量/转换关系\n- 概念工程：用 Enum/Literal/NewType/dataclass(frozen) 与 schema 固化边界；禁止混用\n\n**Python工具**：`Enum`、`Literal`、`NewType`、`pydantic` 校验\n\n**提示词**：\n\n```text\n先产出概念表；再映射成 Python 类型与 schema；给 5 个应被拒绝的反例输入，并写对应测试。\n```",
|
||
"text_sha256": "b6ecc05eea18dcbd23d204a4169b41fc6f2518523ed112fbe24ae6a827c0f71a",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：防止术语漂移导致返工；把领域概念固化进代码。",
|
||
"problem": "防止术语漂移导致返工；把领域概念固化进代码。",
|
||
"mechanism": "- 概念表：术语/定义/边界/不变量/转换关系\n- 概念工程：用 Enum/Literal/NewType/dataclass(frozen) 与 schema 固化边界；禁止混用",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"术语、定义、边界、不变量和转换关系概念表。",
|
||
"对应的类型与schema，以及应拒绝的反例测试。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-15-概念分析-概念工程",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-methodological-doubt",
|
||
"name": "方法论怀疑（笛卡尔式）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：把不可靠前提当事实是 vibe coding 常见事故源。\n\n**落地动作**：\n\n- 对关键前提标注：是否可验证\n- 不可验证 → 必须加运行时校验/超时/重试/降级；并写会失败的测试\n\n**Python工具**：`assert`/校验器、超时、重试、容错分支测试\n\n**提示词**：\n\n```text\n列出该方案依赖的所有前提，并标注可验证性；对不可验证前提添加防线（校验/超时/降级）与对应测试。\n```",
|
||
"text_sha256": "4c202f4edc18569af1f7e45b3626b6d0cf7100c29903d1829ed23098021a402f",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：把不可靠前提当事实是 vibe coding 常见事故源。",
|
||
"problem": "把不可靠前提当事实是 vibe coding 常见事故源。",
|
||
"mechanism": "- 对关键前提标注：是否可验证\n- 不可验证 → 必须加运行时校验/超时/重试/降级；并写会失败的测试",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"关键前提及可验证性清单。",
|
||
"不可验证前提对应的防线和失败测试。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-16-方法论怀疑笛卡尔式",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-evidence-triangulation",
|
||
"name": "视角三角测量（Triangulation）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：减少单一证据的误判；提升结论可靠性。\n\n**落地动作**：\n\n- 同一结论至少两种证据：单测/性质测试 + 日志/指标；或差分测试 + fuzz\n\n**Python工具**：`pytest`/`hypothesis` + metrics/logging；差分对照\n\n**提示词**：\n\n```text\n对关键行为给出至少两种独立验证方式，并说明各自盲区与如何互补。\n```",
|
||
"text_sha256": "366b519324e28a0d9507b34c7338bcf82f584f5d1804ba6f9e6bb2e2fe1b47b3",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：减少单一证据的误判；提升结论可靠性。",
|
||
"problem": "减少单一证据的误判；提升结论可靠性。",
|
||
"mechanism": "- 同一结论至少两种证据：单测/性质测试 + 日志/指标；或差分测试 + fuzz",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"至少两种独立验证方式、各自盲区及互补说明。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-17-视角三角测量triangulation",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-mechanistic-explanation",
|
||
"name": "机制解释（Mechanistic explanation）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：把\"能跑\"变成\"可解释可维护\"；降低未来修改风险。\n\n**落地动作**：\n\n- 要求输出数据流：输入 → 中间状态 → 输出\n- 对中间状态写不变式/断言；把解释与代码结构对齐\n\n**Python工具**：`assert`、类型收窄、分层函数、docstring\n\n**提示词**：\n\n```text\n给出机制解释：数据在系统中如何流动；列出每个中间状态的不变式，并在代码中用断言或类型保证。\n```",
|
||
"text_sha256": "0c8a0cb4a0ce1623aa9937e7b4e3c0588fcbe902cdef2ef123ce2a0842d1bb5e",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：把\"能跑\"变成\"可解释可维护\"；降低未来修改风险。",
|
||
"problem": "把\"能跑\"变成\"可解释可维护\"；降低未来修改风险。",
|
||
"mechanism": "- 要求输出数据流：输入 → 中间状态 → 输出\n- 对中间状态写不变式/断言；把解释与代码结构对齐",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"输入、中间状态、输出的数据流机制说明。",
|
||
"中间状态的不变量及断言或类型保证。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-18-机制解释mechanistic-explanation",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-failure-mode-analysis",
|
||
"name": "错误认识论（Error epistemology）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：系统化\"我们会如何错\"，比事后补洞更省。\n\n**落地动作**：\n\n- 先做失败模式清单（空值/乱序/重复/并发/权限/超时/编码/浮点等）\n- 每类至少一个测试；明确错误语义（raise / error object / log+metric）\n\n**Python工具**：`pytest` 参数化 + `hypothesis`；统一 error 模型\n\n**提示词**：\n\n```text\n生成失败模式清单并按风险排序；为 Top N 写测试；统一错误模型并给出示例响应/异常层级。\n```",
|
||
"text_sha256": "9245e35da05e68b72a9a0519b46919e4ef10c263b29e40f549d9c8421b9a1cbc",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：系统化\"我们会如何错\"，比事后补洞更省。",
|
||
"problem": "系统化\"我们会如何错\"，比事后补洞更省。",
|
||
"mechanism": "- 先做失败模式清单（空值/乱序/重复/并发/权限/超时/编码/浮点等）\n- 每类至少一个测试；明确错误语义（raise / error object / log+metric）",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"按风险排序的失败模式清单及对应测试。",
|
||
"统一错误模型与示例响应/异常层级。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-19-错误认识论error-epistemology",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-experimental-design",
|
||
"name": "实验哲学（x-phi）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：交互与默认策略别靠直觉，用数据决定。\n\n**落地动作**：\n\n- 把争议点改成可测实验（A/B 默认值、错误文案、重试策略）\n- 指标：误用率、重试率、成功率、工单率、完成时间\n\n**Python工具**：埋点/日志、简单 A/B 分组、配置开关\n\n**提示词**：\n\n```text\n把该设计争议转成实验：分组、指标、样本、持续时间、判定阈值；给出埋点字段与分析方法。\n```",
|
||
"text_sha256": "f06a372c255f5363d8555a0e30999ea6c6c0904d432dcaca20491b168af856e1",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：交互与默认策略别靠直觉，用数据决定。",
|
||
"problem": "交互与默认策略别靠直觉，用数据决定。",
|
||
"mechanism": "- 把争议点改成可测实验（A/B 默认值、错误文案、重试策略）\n- 指标：误用率、重试率、成功率、工单率、完成时间",
|
||
"conditions": [
|
||
"适用情境：交互与默认策略存在设计争议，可将争议改成可测实验。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"实验分组、指标、样本、持续时间与判定阈值方案。",
|
||
"埋点字段与分析方法。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-20-实验哲学x-phi",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-computational-modeling",
|
||
"name": "计算哲学（Computational philosophy）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：复杂状态与规则用\"可运行模型/仿真/搜索\"代替纯讨论。\n\n**落地动作**：\n\n- reference 实现（慢但清晰）作为 oracle\n- optimized 实现（快/工程化）用差分测试锁死行为\n- 用仿真/生成器自动探索边界\n\n**Python工具**：`hypothesis`、差分测试、状态机测试（Hypothesis stateful）\n\n**提示词**：\n\n```text\n先写 reference（清晰）+ optimized（高效）；写差分测试与状态机/性质测试自动找反例并修复。\n```",
|
||
"text_sha256": "12d5d8e70ce2fa678f55802b543965ed174d2004b2838629da420da40a1808e8",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：复杂状态与规则用\"可运行模型/仿真/搜索\"代替纯讨论。",
|
||
"problem": "复杂状态与规则用\"可运行模型/仿真/搜索\"代替纯讨论。",
|
||
"mechanism": "- reference 实现（慢但清晰）作为 oracle\n- optimized 实现（快/工程化）用差分测试锁死行为\n- 用仿真/生成器自动探索边界",
|
||
"conditions": [
|
||
"适用情境：复杂状态与规则需要用可运行模型、仿真或搜索探索。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"清晰的reference实现与优化实现。",
|
||
"差分、仿真、生成器或状态机测试的反例。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-21-计算哲学computational-philosophy",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-naturalized-epistemology",
|
||
"name": "自然化认识论（Naturalized epistemology）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：承认人类/模型都有系统性偏误，用流程与工具把偏误外包给检查器。\n\n**落地动作**：\n\n- 默认自动化：lint+format+类型检查+测试\n- 高风险路径：必须性质测试/模糊测试/运行时校验\n- 结论至少双证据（测试+指标）\n\n**Python工具**：`ruff`/`black`/`pyright`/`pytest`/`hypothesis`/`pydantic`\n\n**提示词**：\n\n```text\n列出该任务最常见的误判点，并为每个误判点给一个自动化防线（检查器/测试/断言/埋点）。\n```",
|
||
"text_sha256": "75abcbc85937aaa5a14a2d735f9e0f9b208d596cc2bf934eb98cd34139400dc4",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：承认人类/模型都有系统性偏误，用流程与工具把偏误外包给检查器。",
|
||
"problem": "承认人类/模型都有系统性偏误，用流程与工具把偏误外包给检查器。",
|
||
"mechanism": "- 默认自动化：lint+format+类型检查+测试\n- 高风险路径：必须性质测试/模糊测试/运行时校验\n- 结论至少双证据（测试+指标）",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"常见误判点及自动化防线。",
|
||
"高风险路径的性质/模糊/运行时校验。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-22-自然化认识论naturalized-epistemology",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-bayesian-epistemology",
|
||
"name": "贝叶斯认识论（Bayesian epistemology）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**用途**：在多个方案/原因间理性分配注意力与试错预算。\n\n**落地动作**：\n\n- 先验 → 实验 → 后验 → 下一步；把排查变成序列决策问题\n\n**Python工具**：同 12/13；记录表\n\n**提示词**：\n\n```text\n用贝叶斯式流程组织排查：先验排序、信息增益最高的实验、更新后的行动计划。\n```",
|
||
"text_sha256": "253b8d36452ce18562c9c33c42482690217e86eaec25e88b6d9f9d931d2ce5f5",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收编号方法节的用途、操作、工具建议与提示词；数字建议和工具名称不自动成为倍率、唯一依赖或授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "原文用途：在多个方案/原因间理性分配注意力与试错预算。",
|
||
"problem": "在多个方案/原因间理性分配注意力与试错预算。",
|
||
"mechanism": "- 先验 → 实验 → 后验 → 下一步；把排查变成序列决策问题",
|
||
"conditions": [
|
||
"适用情境：需要在多个方案或原因间分配注意力与试错预算。"
|
||
],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"先验排序、信息增益实验、后验与后续行动计划。"
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||
],
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||
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||
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||
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||
},
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||
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"source_id": "repo-methodology-toolbox-81fc7ae",
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"heading_id": "philosophy-methodology-toolbox-23-贝叶斯认识论bayesian-epistemology",
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||
"selection": "markdown-section",
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||
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|
||
}
|
||
},
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||
{
|
||
"id": "gongfa-fixed-point-convergence",
|
||
"name": "固定点收敛系统",
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||
"aliases": [],
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||
"content_version": "r1",
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||
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||
"entry_status": "registered",
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||
"ontology_type": "method-specification",
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||
"content": {
|
||
"original_text": "#vibecoding方法论 ，固定点收敛系统可界定为自主智能体的分层闭环控制框架，用于将意图转化为可执行、可验证且受变更约束的目标状态，并持续缩小现实与目标的偏差。其机制遵循“观察→差距识别→行动→验证→修正”，行动失效则升级策略，策略失效则复审目标，目标不可确定时回退需求或交由人工裁决。该框架依赖目标可识别、状态可观测、验证准则一致及策略空间中存在有效控制路径；缺乏稳定性或充分反馈时，“收敛”只能作为架构期望。方法上形成“需求澄清→目标冻结→计划→执行→验证→更新→升级”的闭环，并与需求工程、反馈控制、自治计算、监督控制及运行时保障相衔接。其核心问题是：动态环境下，如何以分层约束获得对目标趋近过程的可验证控制。",
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"text_sha256": "228dc7de506eceac46c6f19d9c679c39e43f581991c9c3caae93d021d3d0823a",
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"claim_status": "source_statement",
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"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
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||
"provenance": "editorial_summary",
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||
"summary": "以分层闭环控制，将意图转成可执行、可验证的目标状态，并按失效层级调整行动、策略或目标。",
|
||
"problem": "动态环境下，如何对趋近目标的过程实施可验证控制。",
|
||
"mechanism": "观察→差距识别→行动→验证→修正；行动失效则升级策略，策略失效则复审目标，目标不可确定则回退需求或交由人工裁决。",
|
||
"conditions": [
|
||
"目标可识别、状态可观测、验证准则一致，策略空间中存在有效控制路径。"
|
||
],
|
||
"limitations": [
|
||
"缺乏稳定性或充分反馈时，“收敛”只能作为架构期望。"
|
||
],
|
||
"expected_outcomes": [
|
||
"可执行、可验证且受变更约束的目标状态，以及现实与目标偏差的持续缩小。"
|
||
],
|
||
"questions": [
|
||
"其核心问题是：动态环境下，如何以分层约束获得对目标趋近过程的可验证控制。"
|
||
],
|
||
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||
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||
"source_ref": {
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||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
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||
"heading_id": "固定点收敛系统",
|
||
"selection": "html-paragraph",
|
||
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|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-recursive-ai-governance",
|
||
"name": "递归式 AI 治理",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
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||
"entry_status": "registered",
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"ontology_type": "dao-content",
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"content": {
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||
"original_text": "#vibecoding方法论 ，“递归式AI治理”：AI既承担问题求解，也承担对下一层模型或智能体的生成、选择、审查与修正，以减少人工介入并形成自主闭环。其机制可概括为“实例→假设→选择→推演→检验→归因→更新”，通过跨层反馈持续扩大可自动化的接口。该范式依赖目标、约束与评价信号具有足够可识别性，否则易出现欠决定性、代理目标偏移、误差递归放大与外推失效。因而需以验证层、终止条件、异常回滚和人类最终裁决构成治理闭环。它与元学习、自动机器学习、多智能体系统、控制论及委托—代理理论相连。其核心问题是：复杂认知过程可被递归自动化到何种层级，以及价值判断应在何处保留人类主权。",
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"text_sha256": "98759af3b5fce57802e630260ff2c3337953f1dd5e850ade13bd365f0e2e2ac1",
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"claim_status": "source_statement",
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"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
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||
"provenance": "editorial_summary",
|
||
"summary": "AI既求解问题，也生成、选择、审查与修正下一层模型或智能体，并通过跨层反馈扩大可自动化接口。",
|
||
"problem": "复杂认知过程可以递归自动化到什么层级，价值判断应在哪里保留人类主权。",
|
||
"mechanism": "实例→假设→选择→推演→检验→归因→更新；以验证层、终止条件、异常回滚和人类最终裁决构成治理闭环。",
|
||
"conditions": [
|
||
"目标、约束与评价信号具有足够可识别性。"
|
||
],
|
||
"limitations": [
|
||
"否则易出现欠决定性、代理目标偏移、误差递归放大与外推失效。"
|
||
],
|
||
"expected_outcomes": [
|
||
"减少人工介入并形成自主闭环，通过跨层反馈持续扩大可自动化的接口。"
|
||
],
|
||
"questions": [
|
||
"其核心问题是：复杂认知过程可被递归自动化到何种层级，以及价值判断应在何处保留人类主权。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "递归式-ai-治理",
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||
"selection": "html-paragraph",
|
||
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|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-glue-coding",
|
||
"name": "拼好码",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "dao-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，“拼好码”是一种复用主导的软件工程范式，用于在既有能力空间中完成需求匹配、方案选择与业务编排，以奖励搜索、组装能力，并且惩罚自研、强调以低自研成本获得可验证、可替换、可回滚的系统。其机制为“需求→候选能力→选择→适配→编排→检验→更新”：成熟组件承担通用复杂度，薄胶水表达领域差异，测试与CI提供经验检验。其成立依赖方案可识别、维护稳定、接口可隔离；若供应链不可观测、依赖锁定或关键约束超出现有能力，复用可能转化为系统性风险。方法上需以偏离说明、替换路径和回滚机制闭环，并据运行结果更新方案。该范式关联模块化、组件复用、软件供应链与AI辅助开发。其核心问题是：如何在既有能力构成的假设空间中，以合理归纳偏置平衡复用效率、业务差异与长期演化。",
|
||
"text_sha256": "7ee9dc827a5bceca480aa7d2b969cfd49a097b4791acb970c7e47ede82393c9c",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在既有能力空间中匹配需求、选择方案并编排业务，用低自研成本获得可验证、可替换、可回滚的系统。",
|
||
"problem": "如何平衡复用效率、业务差异与长期演化。",
|
||
"mechanism": "需求→候选能力→选择→适配→编排→检验→更新；成熟组件承担通用复杂度，薄胶水表达领域差异，测试与CI提供经验检验。",
|
||
"conditions": [
|
||
"方案可识别、维护稳定、接口可隔离。"
|
||
],
|
||
"limitations": [
|
||
"供应链不可观测、依赖锁定或关键约束超出现有能力时，复用可能转化为系统性风险。"
|
||
],
|
||
"expected_outcomes": [
|
||
"需求匹配、方案选择与业务编排，以及可验证、可替换、可回滚的系统。"
|
||
],
|
||
"questions": [
|
||
"其核心问题是：如何在既有能力构成的假设空间中，以合理归纳偏置平衡复用效率、业务差异与长期演化。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "拼好码",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-attractor-model",
|
||
"name": "吸引子",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "dao-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，“吸引子”在当前语境可定义为持续软件演化的目标状态模型，用于将“项目变好”形式化为可比较、可更新的优化方向。其表征由自然语言目标、性能/复杂度/风险等可观测变量与硬约束构成。Agent 的运行链条为：变更实例→状态估计→距离比较→方向选择→结果检验→规则更新；多个吸引子叠加形成约束代码演化的动态势场。该机制依赖目标可识别性、指标对真实质量的有效映射及多目标冲突的稳定裁决，否则会出现欠决定性、指标投机与局部最优。闭环需以经验反馈持续校准距离函数、权重和约束。该框架连接多目标优化、控制理论、强化学习与软件演化，核心问题是：如何把开放式工程目标转化为可度量、可检验、可更新且长期方向一致的生成性约束系统。",
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"claim_status": "source_statement",
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"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "把“项目变好”表示为由目标、可观测变量与硬约束组成的可比较、可更新的软件演化方向。",
|
||
"problem": "如何把开放式工程目标转为可度量、可检验、可更新且长期方向一致的约束系统。",
|
||
"mechanism": "变更实例→状态估计→距离比较→方向选择→结果检验→规则更新；经验反馈持续校准距离函数、权重和约束。",
|
||
"conditions": [
|
||
"目标可识别，指标对真实质量具有有效映射，多目标冲突有稳定裁决。"
|
||
],
|
||
"limitations": [
|
||
"否则会出现欠决定性、指标投机与局部最优。"
|
||
],
|
||
"expected_outcomes": [
|
||
"将“项目变好”形式化为可比较、可更新的优化方向"
|
||
],
|
||
"questions": [
|
||
"该框架连接多目标优化、控制理论、强化学习与软件演化，核心问题是：如何把开放式工程目标转化为可度量、可检验、可更新且长期方向一致的生成性约束系统。"
|
||
],
|
||
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|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "吸引子",
|
||
"selection": "html-paragraph",
|
||
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|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-finite-sample-generative-inference",
|
||
"name": "有限样本生成推断",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，有限样本生成推断是一种在受限假设空间中，由少量观测识别潜在生成规律的方法。其过程包括：以有限实例形成经验约束，构造多个候选假设，依据归纳偏置、复杂度与解释能力进行模型选择，再通过演绎生成新的实例或预测，并利用新增证据进行判别、修正与更新。由于有限样本通常存在欠决定性，同一组事实可以对应多种规律，因此推断结果始终依赖假设空间与选择准则。其基本结构可概括为：实例—假设—选择—生成—检验—更新。其核心研究问题是：有限信息在何种约束条件下能够支持对潜在无限结构的可靠推断。",
|
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"text_sha256": "d4102794df1e8c4e645ccbbf0c41cca46b46ac9770d81290e9315045b7df2b90",
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||
"claim_status": "source_statement",
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"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "有限样本生成推断是一种在受限假设空间中，由少量观测识别潜在生成规律的方法。",
|
||
"problem": "有限信息在什么约束下能够支持对潜在无限结构的可靠推断。",
|
||
"mechanism": "有限实例形成经验约束→构造候选假设→按归纳偏置、复杂度与解释能力选择→演绎新实例/预测→用新增证据判别、修正与更新。",
|
||
"conditions": [
|
||
"推断位于受限假设空间内，并依赖假设空间与选择准则。"
|
||
],
|
||
"limitations": [
|
||
"有限样本通常存在欠决定性，同一组事实可对应多种规律。"
|
||
],
|
||
"expected_outcomes": [
|
||
"候选生成规律及通过演绎得到的新实例或预测，随后由新增证据判别、修正和更新。"
|
||
],
|
||
"questions": [
|
||
"其核心研究问题是：有限信息在何种约束条件下能够支持对潜在无限结构的可靠推断。"
|
||
],
|
||
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|
||
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|
||
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|
||
"source_ref": {
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||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
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||
"heading_id": "有限样本生成推断",
|
||
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||
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||
}
|
||
},
|
||
{
|
||
"id": "gongfa-ai-native",
|
||
"name": "AI-native",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
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||
"entry_status": "registered",
|
||
"ontology_type": "dao-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，AI-native可界定为一种以机器代理权扩张为方向的系统设计范式，旨在判断哪些原由人承担的认知、决策与执行环节可转交AI。其机制遵循“任务→能力抽象→权限委派→自主推演→结果检验→反馈更新”，并以人工介入频率、决策依赖度和AI自主完成度衡量原生化程度。该范式依赖模型能力、结果可验证性、过程可审计性与责任边界；在目标欠定义、异常难识别、责任不可转移或外推风险较高时，减少人工介入可能放大系统性错误。AI-native由此与自动化理论、人机协同、智能代理和治理研究相连，其方法核心是动态配置人机控制权，并保留回退与更新机制。更一般地，它追问机器自主性上升后，行动权、控制权与最终责任如何重新分配。",
|
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|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "判断哪些人的认知、决策与执行环节可转交AI，并动态配置人机控制权、回退与更新机制。",
|
||
"problem": "哪些由人承担的认知、决策与执行可转交AI，以及人机行动权、控制权和责任如何配置。",
|
||
"mechanism": "任务→能力抽象→权限委派→自主推演→结果检验→反馈更新",
|
||
"conditions": [
|
||
"依赖模型能力、结果可验证性、过程可审计性与责任边界。"
|
||
],
|
||
"limitations": [
|
||
"目标欠定义、异常难识别、责任不可转移或外推风险较高时，减少人工介入可能放大系统性错误。"
|
||
],
|
||
"expected_outcomes": [
|
||
"判断哪些原由人承担的认知、决策与执行环节可转交AI"
|
||
],
|
||
"questions": [
|
||
"更一般地，它追问机器自主性上升后，行动权、控制权与最终责任如何重新分配。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "ai-native",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-representation-flattening",
|
||
"name": "拍扁",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "explanatory-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，“拍扁”可界定为一种表征化过程：将连续、关系密集且高维的现实对象映射为机器可计算的有限结构，其核心问题是如何构造足以支持推理与行动的表示空间。表格、JSON、向量、图、树或序列对应不同的归纳偏置，它们通过选择性保留结构而限定模型的可见信息、假设空间与可执行计算。其机制可概括为“现实实例→表征生成→候选结构→模型选择→推演计算→经验检验→表示更新”。该过程受信息损失、可识别性、欠决定性及分布外外推能力约束，表征选择失当会使后续计算产生系统性偏差。现代AI的重要进展在于将固定表示进一步转化为可随任务与上下文调整的表示学习，并持续减少依赖人工设计和转换的接口。该范式连接表示学习、统计学习、信息论与科学建模，其更一般的问题是：有限计算系统应如何压缩现实，同时保留对预测、推理与干预真正必要的结构。",
|
||
"text_sha256": "a562c18f987697cbaa45303b40362db1664212ddaae45c59b488fedadf9b7bfa",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "将连续、关系密集且高维的对象转换为机器可计算的有限表示，选择性保留任务所需结构。",
|
||
"problem": "有限计算系统如何压缩现实，同时保留预测、推理与干预所需结构。",
|
||
"mechanism": "现实实例→表征生成→候选结构→模型选择→推演计算→经验检验→表示更新；不同表示形成不同归纳偏置与可见信息范围。",
|
||
"conditions": [],
|
||
"limitations": [
|
||
"该过程受信息损失、可识别性、欠决定性及分布外外推能力约束，表征选择失当会使后续计算产生系统性偏差。"
|
||
],
|
||
"expected_outcomes": [
|
||
"选择性保留任务相关结构的机器可计算表示，如表格、JSON、向量、图、树或序列。"
|
||
],
|
||
"questions": [
|
||
"该范式连接表示学习、统计学习、信息论与科学建模，其更一般的问题是：有限计算系统应如何压缩现实，同时保留对预测、推理与干预真正必要的结构。"
|
||
],
|
||
"missing_fields": [
|
||
"conditions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "拍扁",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-symbolic-space-validated-knowledge",
|
||
"name": "符号序列空间与有效知识子集",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "dao-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，将理论、方法与经验抽象为符号序列空间Σ*中的对象，并把经经验检验支持者定义为有效知识子集K；目标是学习验证诱导的有效性函数，以可计算近似压缩实验搜索空间。其机制形成“实例→假设→选择→演绎→检验→更新”闭环：语言模型生成候选，代理系统结合逻辑检查、形式验证、数据、仿真与人类反馈估计有效性，并将搜索集中于高可信区域。其成立依赖验证信号的可识别性、评价独立性与分布稳定性，并受欠决定性、分布外外推、代理偏差和反馈自证约束。该思路连接统计学习、贝叶斯更新、主动学习、形式化方法与自动化科学发现。核心问题是：经验验证能否被编译为可计算判别机制，以及计算在何种边界内可替代部分实证发现。",
|
||
"text_sha256": "f10f287a30297c1c8b418fa019d9c28d31e590c8460f1d245a8620c6879c57c8",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在符号序列空间中生成候选，由经验检验估计有效性，以可计算近似压缩实验搜索空间。",
|
||
"problem": "经验验证能否成为可计算判别机制，计算可以在什么边界内替代部分实证发现。",
|
||
"mechanism": "实例→假设→选择→演绎→检验→更新；模型生成候选，代理结合逻辑、形式验证、数据、仿真与人类反馈估计有效性。",
|
||
"conditions": [
|
||
"验证信号可识别、评价独立、分布稳定。"
|
||
],
|
||
"limitations": [
|
||
"受欠决定性、分布外外推、代理偏差与反馈自证约束。"
|
||
],
|
||
"expected_outcomes": [
|
||
"验证诱导的有效性函数的可计算近似，以及对实验搜索空间的压缩。"
|
||
],
|
||
"questions": [
|
||
"核心问题是：经验验证能否被编译为可计算判别机制，以及计算在何种边界内可替代部分实证发现。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "符号序列空间与有效知识子集",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-compositional-model",
|
||
"name": "合成模型",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "explanatory-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，合成模型是一种以层级组合与涌现为核心的生成性框架，用于解释复杂形态如何由低层级单元及其相互作用逐层构成。其基本机制可表述为：基础单元→组合规则→结构形成→跨层约束→宏观涌现→经验检验→模型更新；各层既具有相对独立的有效规律，又受上下层耦合、反馈与边界条件限制。该框架的关键风险在于跨尺度映射可能存在可识别性不足与欠决定性，同一宏观结构往往对应多种微观生成机制，因此外推必须依赖可检验预测与模型选择。它与复杂系统、统计物理、多尺度建模及涌现论紧密相关；弦、量子几何、全息原理与涌现时空等更深层假设仍属开放研究。其核心问题在于：有限的微观规则如何经组合、变异与反馈生成具有新规律的高层级结构。",
|
||
"text_sha256": "be9c3afcf2ae8c122b8bfa31abfa0a44d9255cf3a3e1ce09300b357366dc0b47",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "用层级组合、相互作用、跨层约束与涌现解释复杂形态如何逐层构成。",
|
||
"problem": "有限微观规则如何经组合、变异与反馈生成具有新规律的高层级结构。",
|
||
"mechanism": "基础单元→组合规则→结构形成→跨层约束→宏观涌现→经验检验→模型更新；各层规律受到上下层耦合、反馈与边界条件限制。",
|
||
"conditions": [
|
||
"外推依赖可检验预测与模型选择。"
|
||
],
|
||
"limitations": [
|
||
"跨尺度映射可能欠识别、欠决定；同一宏观结构可对应多种微观机制。",
|
||
"弦、量子几何、全息原理与涌现时空等更深层假设仍属开放研究。"
|
||
],
|
||
"expected_outcomes": [
|
||
"解释复杂形态如何由低层级单元及其相互作用逐层构成"
|
||
],
|
||
"questions": [
|
||
"其核心问题在于：有限的微观规则如何经组合、变异与反馈生成具有新规律的高层级结构。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "合成模型",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-solution-space",
|
||
"name": "解空间",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "dao-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，解空间是由问题的全部候选解及其组织关系构成的形式集合，用于界定答案可能存在的范围。其运作可表述为：问题表征→约束编码→候选生成→可行性筛选→目标评价→搜索更新，其中可行域、目标函数与搜索策略共同决定解的可达性与优劣结构。该框架依赖问题边界、约束和评价准则的可形式化；若存在不可识别性、欠决定性、组合爆炸或目标错设，搜索结果与外推能力将受限。方法上，通过定义解集、构造约束与目标、执行搜索、经验检验并更新机制形成闭环。它连接运筹优化、算法设计、约束满足与状态空间搜索；相较问题空间和状态空间，解空间聚焦候选答案及其结构。其核心问题是：有限表征与计算资源下，哪些解能够被定义、识别并有效发现。",
|
||
"text_sha256": "59bad9ce3bd22586e23e256a39a684aefaa4d38940d65fbcbfbb6c1f51b52c8e",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "用候选解及其组织关系界定答案的可能范围，以约束、目标评价和搜索更新探索可行解。",
|
||
"problem": "有限表征与计算资源下，哪些解可被定义、识别并有效发现。",
|
||
"mechanism": "问题表征→约束编码→候选生成→可行性筛选→目标评价→搜索更新；可行域、目标函数与搜索策略共同限定可达性与优劣结构。",
|
||
"conditions": [
|
||
"问题边界、约束和评价准则可形式化。"
|
||
],
|
||
"limitations": [
|
||
"不可识别性、欠决定性、组合爆炸或目标错设会限制搜索与外推。"
|
||
],
|
||
"expected_outcomes": [
|
||
"界定答案可能存在的范围"
|
||
],
|
||
"questions": [
|
||
"其核心问题是：有限表征与计算资源下，哪些解能够被定义、识别并有效发现。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "解空间",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-proximate-causal-mechanism",
|
||
"name": "近端因果机制",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，近端因果机制是指因果链中与目标结果直接相邻并实际触发其发生的生成机制，用于解释结果在何种局部条件下被即时产生。其分析路径可概括为“结果界定→近邻原因识别→机制推演→经验检验→因果更新”：先区分上游背景、传导环节与直接作用项，再借助干预、时序和反事实证据检验末端机制。该方法依赖因果结构具有足够可识别性，并要求关键中介未被遗漏；若存在共同原因、反馈回路或多重充分路径，近端解释可能面临欠决定性，也难以直接外推至更远层级。其方法论与机制解释、因果图模型、过程追踪及实验因果推断相衔接。更一般地，它处理因果解释中的层级选择问题：在复杂生成过程中，何种机制对特定结果具有最高解释相关性。",
|
||
"text_sha256": "77f74cfde9c59c0b5ac6f410db6e5422ea673a5ac6efad12b976d100b2ffb18a",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "区分上游背景、传导环节与直接作用项，用干预、时序和反事实证据检验结果末端的生成机制。",
|
||
"problem": "复杂生成过程中，什么层级的机制对特定结果最有解释相关性。",
|
||
"mechanism": "结果界定→近邻原因识别→机制推演→经验检验→因果更新",
|
||
"conditions": [
|
||
"因果结构具有足够可识别性，关键中介未被遗漏。"
|
||
],
|
||
"limitations": [
|
||
"存在共同原因、反馈回路或多重充分路径时，近端解释可能欠决定，也难直接外推至更远层级。"
|
||
],
|
||
"expected_outcomes": [
|
||
"解释结果在何种局部条件下被即时产生"
|
||
],
|
||
"questions": [
|
||
"更一般地，它处理因果解释中的层级选择问题：在复杂生成过程中，何种机制对特定结果具有最高解释相关性。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "近端因果机制",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-purpose-directed-action",
|
||
"name": "目的导向的行动选择",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，目的导向的行动选择机制，其核心问题是在既定需求与约束下，从可能实现路径构成的假设空间中选择具有较高工具效能的方案。其运行链条表现为：需求识别→目标形式化→候选路径生成→约束筛选→方案推演→经验检验→结果更新。路径进入候选空间仅意味着满足基本可行性，其相对优越性仍依赖评价函数、资源约束、环境状态及对目标的表征方式；当目标含混、评价尺度冲突或信息不足时，方案选择将面临欠决定性、可识别性不足与外推失效等风险。因此，编程实践需要通过反馈不断修正目标、模型与搜索策略，形成“生成—选择—执行—检验—更新”的闭环。该框架与理性行动理论、决策理论、搜索与优化、工程设计及模型选择问题相联，其更一般的问题是：有限认知主体如何在开放的可能性空间中建立可辩护的选择准则，并使行动有效性与目标结构保持一致。",
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"text_sha256": "31a074c8b562f62f6527bda26073f006be041f9c69d95c290961340001250133",
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||
"claim_status": "source_statement",
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"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在需求与约束下生成、筛选并检验候选行动路径，用反馈修正目标、模型与搜索策略。",
|
||
"problem": "有限认知主体如何建立可辩护的选择准则，使行动有效性与目标结构一致。",
|
||
"mechanism": "需求识别→目标形式化→候选路径生成→约束筛选→方案推演→经验检验→结果更新。",
|
||
"conditions": [
|
||
"路径进入候选空间需满足基本可行性；优越性依赖评价函数、资源约束、环境状态及目标表征。"
|
||
],
|
||
"limitations": [
|
||
"目标含混、评价尺度冲突或信息不足时，选择可能欠决定、欠识别或外推失效。"
|
||
],
|
||
"expected_outcomes": [
|
||
"经约束筛选、评价和经验检验的候选行动路径及后续更新。"
|
||
],
|
||
"questions": [
|
||
"该框架与理性行动理论、决策理论、搜索与优化、工程设计及模型选择问题相联，其更一般的问题是：有限认知主体如何在开放的可能性空间中建立可辩护的选择准则，并使行动有效性与目标结构保持一致。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "目的导向的行动选择",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-predictive-processing-update",
|
||
"name": "预测加工与模型更新",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "explanatory-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，基于预测加工与模型更新的认知生成框架，用于解释主体如何在不完备信息下形成并修正现实表征。其机制可概括为“经验→假设→选择→推演→检验→更新”：既有经验限定假设空间并形成归纳偏置，感知与行动提供新证据，系统依据预测误差调整内部生成模型。该过程依赖输入、反馈与证据判准具有相对稳定性；若这些条件被持续操控，则可能引发可识别性不足、证据欠决定及外推偏差。因而，认知塑形的分析应覆盖信息输入、反馈结构、证据权重与更新规则的闭环。该框架与预测编码、贝叶斯认知、主动推断及科学哲学中的理论负载问题相衔接。其核心问题是：对证据生成机制的控制，会在多大程度上约束主体可接受的现实模型。",
|
||
"text_sha256": "03289192c126eb2b371a7a815ae751228257b23d34ac004c19c31ade87610e21",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在不完备信息下，以经验、假设与新证据形成现实表征，并用预测误差修正内部生成模型。",
|
||
"problem": "控制证据生成机制，会在多大程度上约束主体可接受的现实模型。",
|
||
"mechanism": "经验→假设→选择→推演→检验→更新；感知与行动提供证据，依据预测误差调整内部生成模型。",
|
||
"conditions": [
|
||
"输入、反馈与证据判准具有相对稳定性。"
|
||
],
|
||
"limitations": [
|
||
"这些条件被持续操控时，可能引发可识别性不足、证据欠决定及外推偏差。"
|
||
],
|
||
"expected_outcomes": [
|
||
"解释主体如何在不完备信息下形成并修正现实表征"
|
||
],
|
||
"questions": [
|
||
"其核心问题是：对证据生成机制的控制，会在多大程度上约束主体可接受的现实模型。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "预测加工与模型更新",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-constrained-state-transformation",
|
||
"name": "受约束的状态变换",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "dao-content",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，可将跨学科的问题求解抽象为一种受约束的状态变换理论：给定对象的当前状态与目标状态，在规则、资源和风险构成的约束集内，搜索并选择具有较高效用的可行变换路径。其生成机制可表述为“状态表征→变换假设→组合搜索→模型选择→结果推演→经验检验→反馈更新”，其中评价函数决定路径优先级，反馈机制则持续修正假设空间与归纳偏置。该框架成立依赖于状态、变换与目标具有足够的可识别性；若因果结构欠决定、约束不可观测或环境发生分布漂移，外推与优化均可能失效。由此形成“理解—生成—选择—检验—更新”的方法闭环，并与控制论、运筹学、决策理论、科学方法论及人工智能中的规划问题相连接。其更一般的理论核心，是研究世界中哪些状态转移具有可能性、可控性与价值，以及有限认知主体如何发现并改进这些转移路径。",
|
||
"text_sha256": "4f25d260b533bbd958bd2a29b0115d735cf79cdabc2e8e22f57ff14153f0613a",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在规则、资源与风险约束内搜索可行状态转移路径，以评价函数排序，并用经验反馈更新假设空间。",
|
||
"problem": "哪些状态转移具有可能性、可控性与价值，有限认知主体如何发现并改进这些路径。",
|
||
"mechanism": "状态表征→变换假设→组合搜索→模型选择→结果推演→经验检验→反馈更新",
|
||
"conditions": [
|
||
"状态、变换与目标具有足够可识别性。"
|
||
],
|
||
"limitations": [
|
||
"因果结构欠决定、约束不可观测或环境分布漂移时，外推与优化可能失效。"
|
||
],
|
||
"expected_outcomes": [
|
||
"约束集内的可行变换路径及经检验、反馈更新的选择。"
|
||
],
|
||
"questions": [
|
||
"其更一般的理论核心，是研究世界中哪些状态转移具有可能性、可控性与价值，以及有限认知主体如何发现并改进这些转移路径。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "受约束的状态变换",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-minimal-prior-agent-assisted-solving",
|
||
"name": "最小先验条件下的代理增强型任务求解",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，“最小先验条件下的代理增强型任务求解”：其目标是在陌生领域中，以明确的完成条件、约束与验收标准收缩假设空间，并借助高权限智能代理降低知识获取与执行成本。其运作链条为“任务定义→外部知识检索→能力复用→模型选择→关键假设PoC→实现→验证与确认→经验更新”，其中通过标准、基准、参考架构与成熟组件引入归纳偏置，再以需求覆盖、成熟度、成本、风险和集成难度进行多准则选择。该方法依赖外部知识源质量、工具可调用性、接口可集成性及关键假设的可识别性；在信息欠决定、外推失效或权限扩大时，可能放大系统性误差与操作风险。其闭环依靠Verification、Validation及选型、实验、Runbook与失败记录持续更新。该范式与系统工程、证据驱动决策、软件复用、贝叶斯式更新及人机协同相连，核心问题是：在有限认知与有限成本下，如何设计可校验、可复用、可更新的任务求解机制。",
|
||
"text_sha256": "0cdfa62012abcb3b56875094c294032b92b8f4e9482e77f8bccca44507a9de6e",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在陌生领域固定完成条件、约束与验收标准，以外部知识、成熟能力、PoC与代理降低求解成本。",
|
||
"problem": "有限认知与成本下，如何设计可校验、可复用、可更新的任务求解机制。",
|
||
"mechanism": "任务定义→外部知识检索→能力复用→模型选择→关键假设PoC→实现→验证与确认→经验更新",
|
||
"conditions": [
|
||
"外部知识源质量、工具可调用性、接口可集成性及关键假设的可识别性。"
|
||
],
|
||
"limitations": [
|
||
"信息欠决定、外推失效或权限扩大时，可能放大系统性误差与操作风险。"
|
||
],
|
||
"expected_outcomes": [
|
||
"需求覆盖下的任务求解方案、关键假设PoC、实现与验证确认；选型、实验、Runbook及失败记录用于更新。"
|
||
],
|
||
"questions": [
|
||
"该范式与系统工程、证据驱动决策、软件复用、贝叶斯式更新及人机协同相连，核心问题是：在有限认知与有限成本下，如何设计可校验、可复用、可更新的任务求解机制。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "最小先验条件下的代理增强型任务求解",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-projection-representation",
|
||
"name": "Projection 表示转换",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，广义 Projection 可视为任务约束下的表示转换：将复杂对象映射到更可观察、可计算的表示空间，并尽量保留目标所需结构。其机制可概括为“对象→任务结构→表示选择→推演→检验→更新”：任务规定信息保留准则，表示决定哪些关系被显化、哪些运算被简化。该框架成立依赖于原对象具有可提取结构、映射保存任务相关信息、表示空间运算与原关系可靠对应，且信息损失与转换成本可控；否则可能产生结构失真、欠识别、过度压缩或外推失效。方法闭环包括表示构造、检验、误差诊断与更新，并与线性代数、信号处理、降维、表示学习和信息论相连。其核心问题是：如何在可计算性、信息充分性与结构忠实度之间建立任务相关的最优权衡。",
|
||
"text_sha256": "7ad6e7c29d231787585474c9bad2a64e85a3489c91a776880498a097c397c999",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "在任务约束下选择更可观察、可计算的表示，保留任务相关信息和结构，并检验损失与转换成本。",
|
||
"problem": "如何在可计算性、信息充分性与结构忠实度之间形成任务相关权衡。",
|
||
"mechanism": "对象→任务结构→表示选择→推演→检验→更新",
|
||
"conditions": [
|
||
"对象有可提取结构；映射保存任务相关信息；表示运算与原关系可靠对应；信息损失与转换成本可控。"
|
||
],
|
||
"limitations": [
|
||
"否则可能结构失真、欠识别、过度压缩或外推失效。"
|
||
],
|
||
"expected_outcomes": [
|
||
"尽量保留任务相关结构的可观察、可计算表示。"
|
||
],
|
||
"questions": [
|
||
"其核心问题是：如何在可计算性、信息充分性与结构忠实度之间建立任务相关的最优权衡。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "projection-表示转换",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-state-classification",
|
||
"name": "状态分类工具",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "#vibecoding方法论 ，状态分类工具是一种将系统演化过程离散化为始态、暂态与终态的分析框架，用于刻画对象在时间序列中的结构位置及状态转移条件。其运作机制可概括为“状态识别→类型归属→转移推演→结果判定→经验检验→分类更新”：始态界定分析起点，暂态描述尚未稳定的中间过程，终态表示在既定规则下达到的稳定、吸收或判定状态。该方法依赖状态边界可定义、转移规则具有相对稳定性及观测信息足以支持状态识别；若存在路径依赖、状态重叠、不可观测变量或开放系统扰动，则可能产生可识别性不足与结果欠决定性。该框架与有限状态机、马尔可夫过程、动态系统及过程分析相连接，其核心问题是：如何以有限状态空间表征连续演化，并建立可检验、可更新且具外推能力的状态转移模型。",
|
||
"text_sha256": "22960352424cb9e6eaf21f75b78332cfc0685c732dc6f175b579d5400f60773d",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只取标题下的方法段落；页面根节点和宏观表述不构成本体继承、权限授予或品级证据。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "将系统演化表示为始态、暂态与终态，分析时间序列位置、状态转移条件和结果判定。",
|
||
"problem": "如何用有限状态空间表征连续演化，形成可检验、可更新且具外推能力的转移模型。",
|
||
"mechanism": "状态识别→类型归属→转移推演→结果判定→经验检验→分类更新",
|
||
"conditions": [
|
||
"状态边界可定义，转移规则相对稳定，观测信息足以支持状态识别。"
|
||
],
|
||
"limitations": [
|
||
"路径依赖、状态重叠、不可观测变量或开放系统扰动，可能导致欠识别与结果欠决定。"
|
||
],
|
||
"expected_outcomes": [
|
||
"刻画对象在时间序列中的结构位置及状态转移条件"
|
||
],
|
||
"questions": [
|
||
"该框架与有限状态机、马尔可夫过程、动态系统及过程分析相连接，其核心问题是：如何以有限状态空间表征连续演化，并建立可检验、可更新且具外推能力的状态转移模型。"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "tradecatlabs-vibe-coding-methodology-page",
|
||
"heading_id": "状态分类工具",
|
||
"selection": "html-paragraph",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-top-down-system-design",
|
||
"name": "自顶向下系统设计",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**自顶向下**的核心思路是：先明确系统整体要做什么，再逐层拆分成子系统、模块、类、函数，最后落实到具体代码实现。\n\n比如要开发一个在线购物系统，采用自顶向下的方法时，通常会先问：\n\n这个系统的总体目标是什么？\n它需要支持哪些核心业务？\n整体架构应该如何划分？\n\n然后再逐步拆解：\n\n在线购物系统\n→ 用户模块、商品模块、购物车模块、订单模块、支付模块、物流模块\n→ 订单模块\n→ 创建订单、取消订单、查询订单、订单状态流转\n→ 创建订单函数\n→ 参数校验、库存检查、价格计算、订单保存、消息通知\n\n这种方法的优势是**全局结构清晰**。系统从一开始就有比较明确的架构边界，模块之间的关系也更容易统一规划。对于需求比较明确、规模较大的系统，例如银行核心系统、企业 ERP 系统、政务平台、基础设施平台等，自顶向下非常常见。\n\n它的缺点是，如果一开始对需求理解不准确，高层设计可能会出现偏差，后续细节实现时就会频繁返工。因此，自顶向下适合需求相对清楚、业务边界比较稳定的场景。",
|
||
"text_sha256": "9fa4b5861d87e9d221244db001058a619151e8582388cf39eb2165a6dce2361c",
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||
"claim_status": "source_statement",
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||
"selection_note": "只收自顶向下的完整限定节，不收后面两种方法或综合选型。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "从整体目标与核心业务开始，逐层拆成子系统、模块、类、函数与代码。",
|
||
"problem": "如何在需求相对清楚的系统中先确定整体结构与边界。",
|
||
"mechanism": "明确系统目标/核心业务/整体架构→拆子系统与模块→细化功能、类和函数→实现代码。",
|
||
"conditions": [
|
||
"需求相对清楚、业务边界比较稳定；原文举银行核心、ERP、政务与基础设施等大型系统。"
|
||
],
|
||
"limitations": [
|
||
"开始时理解需求不准确会使高层设计偏离，后续细节实现频繁返工。"
|
||
],
|
||
"expected_outcomes": [
|
||
"全局结构、架构边界与模块关系的规划，以及向具体代码的逐层落实。"
|
||
],
|
||
"questions": [
|
||
"系统的总体目标是什么？",
|
||
"需要支持哪些核心业务？",
|
||
"整体架构应该如何划分？"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-system-building-81fc7ae",
|
||
"heading_id": "concept-system-building-一自顶向下先看整体再拆细节",
|
||
"selection": "markdown-section",
|
||
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|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-bottom-up-system-building",
|
||
"name": "自底向上系统构建",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**自底向上**的核心思路是：先从基础能力、底层组件、工具模块开始建设，再逐步组合成更大的功能和完整系统。\n\n比如还是开发在线购物系统，采用自底向上的方法时，可能会先实现：\n\n日志组件\n配置管理组件\n数据库访问组件\n缓存组件\n权限校验组件\n消息队列封装\n通用异常处理模块\n支付 SDK 封装\n\n当这些基础组件逐渐稳定后，再用它们组合出商品服务、订单服务、支付服务等业务模块，最终形成完整系统。\n\n这种方法的优势是**复用性强、基础能力扎实**。团队可以不断沉淀通用模块，后续开发新功能时就不需要重复造轮子。对于已有技术平台、组件库、框架体系的团队来说，自底向上很自然。\n\n它也适合需求还在演化的项目。因为业务目标可能一开始并不完全清楚，但团队可以先建设确定性较高的底层能力，等需求逐渐明确后再组合成业务系统。\n\n它的风险是，如果只关注底层组件而缺乏整体目标，可能会出现“组件很多，但系统拼不起来”的问题。也就是说，自底向上容易造成局部能力很强，但整体架构不够统一。",
|
||
"text_sha256": "80e8562bebf39d2f10874ad98440e739abab28ee7718311fcb335fe559826665",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收自底向上的完整限定节，组件示例不是唯一实现依赖。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "先建设确定的基础能力与组件，再将稳定组件组合为业务功能和完整系统。",
|
||
"problem": "如何利用既有技术平台或确定性较高的基础能力支撑演化中的业务。",
|
||
"mechanism": "建设基础能力/组件/工具模块→组件逐渐稳定→组合业务模块→形成完整系统。",
|
||
"conditions": [
|
||
"适合已有技术平台、组件库或框架体系的团队，也适合业务需求仍在演化的项目。"
|
||
],
|
||
"limitations": [
|
||
"只关注底层而缺乏整体目标，可能出现“组件很多，但系统拼不起来”，整体架构不统一。"
|
||
],
|
||
"expected_outcomes": [
|
||
"可复用基础组件、由组件组合的业务模块与完整系统。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-system-building-81fc7ae",
|
||
"heading_id": "concept-system-building-二自底向上先做组件再组系统",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-divide-and-conquer",
|
||
"name": "分而治之",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "**分而治之**的核心思想是：面对复杂问题时，不直接一次性解决整体，而是把它拆成若干相对独立、规模更小的问题，分别解决后再组合起来。\n\n它更像是一种通用的问题处理原则，不只是软件工程中的系统构建方法，也广泛存在于算法设计、项目管理、组织协作中。\n\n比如开发一个推荐系统，可以把问题拆成：\n\n数据采集\n用户画像\n商品画像\n召回算法\n排序算法\n特征工程\n模型训练\n在线推理\n效果评估\n\n每个部分都可以由不同团队或不同模块独立推进，最后再集成为完整的推荐系统。\n\n分而治之的优点是**降低复杂度**。一个大问题往往难以直接理解和实现，但拆成多个小问题后，每个小问题的目标更清晰、测试更容易、维护成本也更低。\n\n不过，分而治之的关键在于“如何拆”。如果拆分边界不合理，就会导致模块之间耦合严重、接口混乱、集成困难。好的拆分应该尽量做到高内聚、低耦合：每个模块内部职责集中，模块之间通过清晰接口协作。",
|
||
"text_sha256": "93a23c4213f381f7de16ad98ac7aeeb3aa58d6870a97aefca35fbfe04fc01b26",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收分而治之的完整限定节；不把任务拆分次序改成类型继承。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "把复杂整体拆成相对独立的小问题，分别求解，再集成为完整结果。",
|
||
"problem": "如何将难以理解、实现和测试的复杂问题分解为可管理单元。",
|
||
"mechanism": "按问题边界拆分→分别解决子问题→通过清晰接口集成。",
|
||
"conditions": [
|
||
"拆分尽量高内聚、低耦合，模块职责集中，通过清晰接口协作。"
|
||
],
|
||
"limitations": [
|
||
"边界不合理会造成耦合严重、接口混乱与集成困难。"
|
||
],
|
||
"expected_outcomes": [
|
||
"更清晰、易测试与维护的子问题，以及集成后的完整系统。"
|
||
],
|
||
"questions": [
|
||
"如何拆分问题边界？"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-system-building-81fc7ae",
|
||
"heading_id": "concept-system-building-三分而治之把复杂问题拆成小问题",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-problem-definition-framework",
|
||
"name": "问题界定与求解闭环（仓内体系版）",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "你这套框架可以搭成一个完整的问题求解系统：\n\n<a id=\"concept-problem-solving-一问题从哪里来\"></a>\n#### 一、问题从哪里来？\n\n问题来自：\n\n目标状态 ≠ 当前状态\n\n只要“想要的结果”和“现实情况”之间存在差距，问题就出现了。\n\n例如：\n\n* 想学会英语，但现在听不懂\n* 想提高业绩，但当前成交率低\n* 想管理团队，但成员执行不稳定\n* 想做出产品，但用户需求不清晰\n\n这些表面上是不同问题，本质都是状态差距。\n\n<a id=\"concept-problem-solving-二问题如何被定义\"></a>\n#### 二、问题如何被定义？\n\n定义问题需要四件事：\n\n1. 目标：要达到什么？\n2. 现状：现在是什么？\n3. 差距：缺什么、卡在哪？\n4. 标准：怎样算解决？\n\n如果这四件事不清楚，后面所有努力都可能是在“解错题”。\n\n<a id=\"concept-problem-solving-三问题如何被求解\"></a>\n#### 三、问题如何被求解？\n\n求解问题的核心结构是：\n\n目标 × 约束 × 对象 → 路径\n\n也就是说，方案不是凭空来的，而是由这三个因素决定的。\n\n<a id=\"concept-problem-solving-1-目标决定方向\"></a>\n##### 1. 目标决定方向\n\n目标不同，解法不同。\n\n例如：\n\n* 目标是“先能用”，就用最简单可行方案\n* 目标是“做到最优”，就需要更复杂的比较和优化\n* 目标是“短期见效”，就优先处理关键瓶颈\n* 目标是“长期稳定”，就要建设系统和机制\n\n<a id=\"concept-problem-solving-2-约束决定边界\"></a>\n##### 2. 约束决定边界\n\n约束告诉你什么不能忽略。\n\n常见约束包括：\n\n* 时间\n* 资源\n* 成本\n* 风险\n* 规则\n* 能力上限\n* 外部环境\n\n没有约束的方案，往往只是空想。\n\n<a id=\"concept-problem-solving-3-对象决定方法\"></a>\n##### 3. 对象决定方法\n\n对象不同，处理方式不同。\n\n如果对象是信息，重点是筛选、辨别、整理。\n如果对象是人，重点是动机、沟通、协作。\n如果对象是系统，重点是结构、流程、反馈。\n如果对象是资源，重点是配置、优先级、效率。\n如果对象是环境，重点是适应、利用、改变条件。\n\n<a id=\"concept-problem-solving-四求解如何收敛\"></a>\n#### 四、求解如何收敛？\n\n求解不是一次完成，而是靠反馈收敛：\n\n执行 → 结果 → 对比目标 → 发现偏差 → 修正路径 → 再执行\n\n这就是迭代。\n\n所以完整链条是：\n\n当前状态 → 目标状态 → 状态差距 → 问题定义 → 目标/约束/对象 → 求解路径 → 执行校正 → 结果验证 → 反馈迭代 → 目标达成",
|
||
"text_sha256": "5f98b23774d0fd73e31f8db76ed410d74f062dde92ab7df18e7670ab1a05c7b2",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收“5.搭建体系”及其下位解释，排除新手提示词和后续应用案例；差距式是源文概念简记，不是通用数值减法。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "用目标、现状、差距与标准界定问题，在目标/约束/对象下选择路径，再用反馈修正。",
|
||
"problem": "如何避免解错问题，并让求解过程通过结果反馈趋近目标。",
|
||
"mechanism": "定义目标/现状/差距/标准→按目标、约束和对象构造路径→执行→比较结果与目标→修正→再执行。",
|
||
"conditions": [
|
||
"问题定义的目标、现状、差距和标准须清楚；路径需考虑时间、资源、成本、风险、规则、能力上限与环境。"
|
||
],
|
||
"limitations": [
|
||
"四项定义不清，努力可能是在解错题；没有约束的方案往往只是空想。"
|
||
],
|
||
"expected_outcomes": [
|
||
"明确的问题定义、受约束的求解路径及经反馈修正的结果。"
|
||
],
|
||
"questions": [
|
||
"要达到什么？",
|
||
"现在是什么？",
|
||
"缺什么、卡在哪？",
|
||
"怎样算解决？"
|
||
],
|
||
"missing_fields": []
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-problem-solving-81fc7ae",
|
||
"heading_id": "concept-problem-solving-5-搭建体系",
|
||
"selection": "markdown-section",
|
||
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|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-methodology-delivery-loop",
|
||
"name": "方法论工具箱总体作业流",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "建议默认流程：\n\n1. **现象卡片**（现象/意图/情境/边界）→ 清零脑补\n2. **规格化**（类型+schema+错误语义+不变式）→ 可机器检查\n3. **检查器**（单测+性质测试+lint+类型检查+关键断言）→ 可证伪\n4. **最小实现**（main path）→ 快速跑通\n5. **反例驱动**（Hypothesis/边界/差分/基准）→ 找到失败模式\n6. **收敛重构**（删复杂度、固化概念、稳定接口、补文档）→ 可维护",
|
||
"text_sha256": "6f224bc1df90eee7f2d857e492b3465a1d04801e579b7f8f20e592758c1ca5c6",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收六步总体作业流，不合并下方23个独立方法或工具配置。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "用现象卡片、规格、检查器、最小实现、反例与收敛重构组织工程交付。",
|
||
"problem": "如何将探索组织为可检查、可证伪和可维护的工程产出。",
|
||
"mechanism": "现象卡片→规格化→检查器→最小实现→反例驱动→收敛重构。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"现象/意图/情境/边界卡片、规格与检查器、最小实现、失败模式及维护后的稳定版本。"
|
||
],
|
||
"questions": [],
|
||
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|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-methodology-toolbox-81fc7ae",
|
||
"heading_id": "philosophy-methodology-toolbox-总体作业流",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
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|
||
},
|
||
{
|
||
"id": "gongfa-cybernetic-feedback-method",
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"name": "控制论与科学方法论（仓内工程解释版）",
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"original_text": "控制论视角下，工程实践不是一次性生成，而是通过信息、反馈和约束持续收缩可能性空间。\n\n核心要点：\n\n1. 控制的逻辑起点是被控对象存在多种可能状态；控制的本质是通过选择手段，让可能性空间朝目标状态收缩。\n2. 改造世界的实践，本质是在多维可能性空间中选择物质、条件和时机，最终把低概率组合实现为确定结果。\n3. 控制能力可以理解为控制前后可能性空间大小之比；任何工具或方法都有能力上限。\n4. 负反馈通过比较现状与目标的差距并采取行动缩小差距，把有限的单次控制能力累积放大。\n5. 正反馈会自我增强，使状态持续偏离初始平衡点，可能导致增长、演化、崩溃或恶性循环。\n6. 信息是对系统不确定性的减少；控制要实现，必须先获得足够信息。\n7. 控制与信息是一体两面：信息改变认知状态，控制改变现实状态。\n8. 组织是一种结构状态，组织化程度越高，结构中包含的信息量越高。\n9. 复杂系统的因果关系常常是概率因果、反馈环和因果网络，而不是单一线性链条。\n10. 有效分析必须建立“相对孤立系统”，把无限问题切成有限问题。\n11. 拥有内部反馈回路的系统会趋向稳态结构，用动态平衡抵御外部随机干扰。\n12. 系统演化通常是旧稳态被破坏，经过不稳定过渡，落入新稳态。\n13. 自组织系统会在没有外部指令时，由内部相互作用从无序涌现出宏观有序。\n14. 质变可以是渐变，也可以是飞跃；关键不在变化速度，而在中间状态是否稳定。\n15. 黑箱认识依赖输入控制与输出观察，理论就是对黑箱内部结构的模型。\n16. 认识过程是“实践 -> 理论 -> 实践”的负反馈循环，用模型预测与现实输出的差异修正模型。\n17. 认识反馈能否收敛，取决于理论是否可证伪、反馈速度是否足够快、反馈幅度是否不过度、实践结果是否能可靠判别理论真伪。\n18. 科学规律的本质是变量之间的约束关系；掌握规律，就是把不可控随机变量转成可预测、可控制的确定结果。\n\n用于 AI 编程时，这套方法可以压缩成一句话：\n\n> 先把目标写成可检验状态，再用测试、日志、反馈、约束和迭代，把模型输出从可能性空间中逐步收缩到可验收结果。",
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"profile": {
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||
"summary": "以信息、约束和反馈在可能性空间中选择行动，用模型预测与现实输出的差异修正模型。",
|
||
"problem": "如何用可检验目标与反馈把一次性生成改成持续求解和工程验收。",
|
||
"mechanism": "源文以“实践→理论→实践”描述负反馈：比较模型预测与现实输出修正模型，并限定相对孤立系统；AI编程用测试、日志、反馈、约束与迭代检验。",
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"conditions": [
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"获得足够信息并建立有限问题边界；源文认为认识反馈的收敛取决于可证伪性、反馈速度/幅度及结果的可靠判别。"
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||
],
|
||
"limitations": [
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||
"工具与方法有能力上限；正反馈可能形成增长、崩溃或恶性循环；复杂因果可能是概率关系、反馈环或网络。"
|
||
],
|
||
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|
||
"可检验目标状态、模型修订与可验收结果。"
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"source_id": "repo-methodology-toolbox-81fc7ae",
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{
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||
"id": "gongfa-recursive-generator-optimization",
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||
"name": "递归生成器自优化（仓内形式模型）",
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||
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||
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"original_text": "> 递归自优化生成系统的形式化模型。\n\n<a id=\"concept-recursive-self-optimizing-system-摘要\"></a>\n### 摘要\n\n本文研究一类递归自优化生成系统。它们的目标不是直接生成最优输出，而是通过迭代式自我修改，构建一种稳定的生成能力。系统先生成产物，再根据理想化目标优化这些产物，并使用优化后的产物更新自身的生成机制。本文把这一过程形式化为生成器空间上的自映射，识别其不动点结构，并用代数与 λ 演算表达这种自指动力学。分析表明，这类系统天然体现了一种由不动点语义支配的自举式元生成过程。\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-1-引言\"></a>\n### 1. 引言\n\n自动化提示词工程、元学习和自改进 AI 系统的近期进展表明，系统关注点正在从优化单个输出，转向优化产生输出的机制。在这类系统中，计算对象不再是一个解，而是一个**解的生成器**。\n\n本文形式化描述一种递归自优化框架：生成器产生产物，优化算子根据理想化目标改进产物，元生成器再使用优化结果更新生成器自身。重复执行这一闭环，会得到一个生成器序列；该序列可能收敛到一种稳定且自洽的生成能力。\n\n本文的贡献是给出一个紧凑的形式模型，用来捕捉这种行为，并说明该系统可以自然地用不动点与自指计算来解释。\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-2-形式模型\"></a>\n### 2. 形式模型\n\n令 \\(\\mathcal{I}\\) 表示意图空间，\\(\\mathcal{P}\\) 表示提示词、程序或技能的空间。定义生成器空间：\n\n$$\n\\mathcal{G} \\subseteq \\mathcal{P}^{\\mathcal{I}},\n$$\n\n其中每个生成器 \\(G \\in \\mathcal{G}\\) 都是一个函数：\n\n$$\nG : \\mathcal{I} \\to \\mathcal{P}.\n$$\n\n令 \\(\\Omega\\) 表示理想目标或评估准则的抽象表示。定义：\n\n$$\nO : \\mathcal{P} \\times \\Omega \\to \\mathcal{P},\n$$\n\n作为优化算子；再定义：\n\n$$\nM : \\mathcal{G} \\times \\mathcal{P} \\to \\mathcal{G},\n$$\n\n作为元生成算子，用优化后的产物更新生成器。\n\n给定初始意图 \\(I \\in \\mathcal{I}\\)，系统按以下方式演化：\n\n$$\nP = G(I),\n$$\n\n$$\nP^{*} = O(P, \\Omega),\n$$\n\n$$\nG' = M(G, P^{*}).\n$$\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-3-递归更新算子\"></a>\n### 3. 递归更新算子\n\n上述过程在生成器空间上诱导出一个自映射：\n\n$$\n\\Phi : \\mathcal{G} \\to \\mathcal{G},\n$$\n\n定义为：\n\n$$\n\\Phi(G) = M\\big(G, O(G(I), \\Omega)\\big).\n$$\n\n对 \\(\\Phi\\) 进行迭代，会得到序列 \\(\\{G_n\\}_{n \\ge 0}\\)，满足：\n\n$$\nG_{n+1} = \\Phi(G_n).\n$$\n\n系统的目标不是某个具体的 \\(P^{*}\\)，而是生成器序列 \\(\\{G_n\\}\\) 的收敛行为。\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-4-不动点语义\"></a>\n### 4. 不动点语义\n\n**稳定生成能力**可以定义为 \\(\\Phi\\) 的一个不动点：\n\n$$\nG^{*} \\in \\mathcal{G}, \\quad \\Phi(G^{*}) = G^{*}.\n$$\n\n这样的生成器在“生成 -> 优化 -> 更新”的自身闭环下保持不变。当 \\(\\Phi\\) 满足适当的连续性或压缩性条件时，\\(G^{*}\\) 可以通过迭代极限获得：\n\n$$\nG^{*} = \\lim_{n \\to \\infty} \\Phi^{n}(G_0).\n$$\n\n这个不动点表示一个自洽的生成器：它的输出已经编码了自身改进所需的准则。\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-5-代数与-λ-演算表示\"></a>\n### 5. 代数与 λ 演算表示\n\n这个递归结构可以用无类型 λ 演算表达。令 \\(I\\) 与 \\(\\Omega\\) 为常量项，令 \\(G\\)、\\(O\\)、\\(M\\) 为 λ 项。定义单步更新泛函：\n\n$$\n\\text{STEP} \\equiv \\lambda G.\\ (M\\ G)\\big((O\\ (G\\ I))\\ \\Omega\\big).\n$$\n\n引入不动点组合子：\n\n$$\nY \\equiv \\lambda f.(\\lambda x.f(x\\ x))(\\lambda x.f(x\\ x)).\n$$\n\n稳定生成器可以表示为：\n\n$$\nG^{*} \\equiv Y\\ \\text{STEP},\n$$\n\n并满足：\n\n$$\nG^{*} = \\text{STEP}\\ G^{*}.\n$$\n\n这个表示明确揭示了系统的自指性质：生成器被定义为一个泛函的不动点，而这个泛函会使用生成器自身的输出来变换生成器。\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-6-讨论\"></a>\n### 6. 讨论\n\n上述形式化说明，递归自优化天然导向不动点结构，而不是终端输出。生成器既是计算主体，也是计算对象；改进发生在生成器空间中的收敛过程里，而不是单个输出空间中的一次性优化里。\n\n这类系统与关于自指、递归和自举计算的经典结果一致，并为自改进 AI 架构与自动化元提示词系统提供了一种原则性基础。\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-7-结论\"></a>\n### 7. 结论\n\n本文提出了递归自优化生成系统的形式模型，并通过自映射、不动点和 λ 演算递归刻画其行为。分析表明，稳定的生成能力对应于元生成算子的不动点，这为自改进生成机制提供了一个简洁的理论基础。\n\n---\n\n<a id=\"concept-recursive-self-optimizing-system-附录高层次概念释义\"></a>\n### 附录：高层次概念释义\n\n这篇论文的核心思想，可以通俗理解为一个能够**自我完善**的 AI 系统。其递归本质可以拆成以下步骤。\n\n<a id=\"concept-recursive-self-optimizing-system-1-定义核心角色\"></a>\n#### 1. 定义核心角色\n\n- **α-提示词（生成器）**：一个“母体”提示词，唯一职责是**生成**其他提示词或技能。\n- **Ω-提示词（优化器）**：另一个“母体”提示词，唯一职责是**优化**其他提示词或技能。\n\n<a id=\"concept-recursive-self-optimizing-system-2-描述递归生命周期\"></a>\n#### 2. 描述递归生命周期\n\n1. **创生（Bootstrap）**\n 用 AI 生成 `α-提示词` 和 `Ω-提示词` 的初始版本 `v1`。\n\n2. **自省与进化（Self-Correction & Evolution）**\n 用 `Ω-提示词 v1` 去**优化** `α-提示词 v1`，得到更强的 `α-提示词 v2`。\n\n3. **创造（Generation）**\n 用**进化后的** `α-提示词 v2` 生成所需的目标提示词和技能。\n\n4. **循环与飞跃（Recursive Loop）**\n 将新生成的、更强大的产物，甚至包括新版本的 `Ω-提示词`，反馈给系统，再次用于优化 `α-提示词`，从而启动下一轮进化。\n\n<a id=\"concept-recursive-self-optimizing-system-3-终极目标\"></a>\n#### 3. 终极目标\n\n通过这个持续运行的**递归优化循环**，系统在每次迭代中都完成一次**自我超越**，不断逼近我们设定的**理想状态**。",
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"selection_note": "完整保留该文及附录；形式模型和源文“更强/收敛”主张不等于实测改进，不替代BFO主树，不从连续性单独推导一般收敛保证。",
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||
"profile": {
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||
"provenance": "editorial_summary",
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||
"summary": "生成器先产生产物，优化器改进产物，元生成器再据此更新生成机制；讨论生成器序列的不动点。",
|
||
"problem": "如何把单个输出的优化转向生成机制自身的迭代更新。",
|
||
"mechanism": "P=G(I)→P*=O(P,Ω)→G′=M(G,P*)；反复迭代生成器空间上的自映射Φ。",
|
||
"conditions": [
|
||
"源文只在满足适当连续性或压缩性条件时讨论以迭代极限获得不动点。"
|
||
],
|
||
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|
||
"源文称生成器序列“可能收敛”，形式表示和自指结构本身不是实际改进或工程收敛的证明。"
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||
],
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||
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||
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"content": {
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"original_text": "- 行为先于结构\n- 结构先于代码\n- 程序是规约的影子",
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||
"summary": "先明确行为，再确定结构，最后落实代码。",
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||
"problem": "实现前如何安排规约、结构与代码的先后。",
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||
"mechanism": "行为先于结构，结构先于代码；源文将程序称为规约的影子。",
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||
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||
},
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||
{
|
||
"id": "gongfa-state-ownership-questions",
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||
"name": "状态时间与责任追踪",
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||
"content": {
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||
"original_text": "- 程序不是空间结构，而是时间上的结构\n- 每段逻辑都是随时间展开的事件\n- 设计要回答三个问题：\n 1. 状态由谁持有？\n 2. 状态何时变化？\n 3. 谁触发变化？",
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"selection_note": "只收“10.时间视角”及三个设计问题；这是源文工程认知工具，不改变BFO中实体/过程/状态的定义。",
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||
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||
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||
"summary": "从时间展开角度检查状态由谁持有、何时变化、由谁触发。",
|
||
"problem": "设计中如何明确状态持有者、变化时机与触发者。",
|
||
"mechanism": "依次询问状态归属、变化时间和触发来源。",
|
||
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|
||
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||
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|
||
"questions": [
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||
"状态由谁持有？",
|
||
"状态何时变化？",
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||
"谁触发变化？"
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||
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||
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||
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|
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||
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||
{
|
||
"id": "gongfa-high-cohesion",
|
||
"name": "高内聚",
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||
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||
"content": {
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||
"original_text": "- 相关的靠近\n- 不相关的隔离\n- 单一职责是内聚的核心",
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|
||
"provenance": "editorial_summary",
|
||
"summary": "让相关内容靠近、不相关内容隔离，以单一职责作为内聚核心。",
|
||
"problem": "如何按相关性与职责组织代码。",
|
||
"mechanism": "相关的靠近，不相关的隔离；单一职责是内聚核心。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-programming-dao-81fc7ae",
|
||
"heading_id": "philosophy-programming-dao-高内聚",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-low-coupling",
|
||
"name": "低耦合",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "- 模块如行星：可预测，却不束缚\n- 依赖越少，生命越长\n- 不耦合，才自由",
|
||
"text_sha256": "5f50ecf5fa02490a514fe64352ce2ca2fd8ee669977022d3b1101f42b0be8642",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "模块之间应可预测而不互相束缚，优先减少依赖。",
|
||
"problem": "模块之间如何保持较少束缚。",
|
||
"mechanism": "以依赖更少作为模块关系的取舍方向。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"原文把依赖减少与更长生命及自由相联系；这是方向性主张。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-programming-dao-81fc7ae",
|
||
"heading_id": "philosophy-programming-dao-低耦合",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-stable-interface-contract",
|
||
"name": "稳定接口与可变实现",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "- API 是契约\n- 实现是细节\n- 不破坏契约，就是负责",
|
||
"text_sha256": "5d2157fe1081ddbed9c5dbd380ee318068412a02d8e4ae354102d7f9c9d2b71d",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "API 是契约，实现是细节，变化不应破坏契约。",
|
||
"problem": "实现变化时如何对接口契约负责。",
|
||
"mechanism": "区分契约与实现细节，保持契约不被破坏。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-programming-dao-81fc7ae",
|
||
"heading_id": "philosophy-programming-dao-稳定接口不稳定实现",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-explicit-dependencies",
|
||
"name": "显式依赖优先",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "- 显式 > 隐式\n- 透明 > 优雅\n- 隐式依赖是腐败的起点",
|
||
"text_sha256": "81c50c27c950240229ca928a1eaf9fd24dd5ddc18ef64bc42c11017363837b3a",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "依赖表达优先显式和透明，警惕隐藏依赖。",
|
||
"problem": "如何取舍依赖关系的表达方式。",
|
||
"mechanism": "显式优于隐式，透明优于优雅。",
|
||
"conditions": [],
|
||
"limitations": [
|
||
"原文警示隐式依赖是腐败的起点；未给普适证明。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-programming-dao-81fc7ae",
|
||
"heading_id": "philosophy-programming-dao-隐藏的依赖最危险",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-invariant-constraints",
|
||
"name": "不变式与明确约束",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "- 不变式是程序的物理法则\n- 明确约束 = 创造秩序",
|
||
"text_sha256": "5c820280d17a1f5cfb2cb303d03afaac36c6546a27d334ab2fde85378a3a976d",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "把不变式视为程序的法则，通过明确约束建立秩序。",
|
||
"problem": "如何以明确约束建立程序秩序。",
|
||
"mechanism": "强调不变式及明确约束。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"原文将明确约束与创造秩序相联系。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-programming-dao-81fc7ae",
|
||
"heading_id": "philosophy-programming-dao-不变式保持世界稳定",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-reasonable-code",
|
||
"name": "可推理性优先",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "- 可预测性比性能更重要\n- 程序应能被人脑推理\n- 变量少、分支浅、状态明、逻辑平\n- 可推理性 = 可维护性",
|
||
"text_sha256": "02556cfc972942c7104dd64050fc1c927723fc8cd4beea0a80111a21f4e7cc83",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "强调人可推理的程序，以少变量、浅分支、明状态和平逻辑支持可理解性。",
|
||
"problem": "程序怎样更容易被人脑推理。",
|
||
"mechanism": "优先可预测性；变量少、分支浅、状态明、逻辑平。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"原文把可推理性与可维护性相等同；这是来源主张。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-programming-dao-81fc7ae",
|
||
"heading_id": "philosophy-programming-dao-9-可推理性",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-least-surprise",
|
||
"name": "最小惊讶原则",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "- 好代码应像常识一样运作\n- 不惊讶，就是最好的用户体验\n- 可预测性 = 信任",
|
||
"text_sha256": "35c11147118b90685768b6dbd0bb5e437e8680d171a8d5cc45a3a2a8f383d18b",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "代码行为应符合常识并减少惊讶，强调可预测性。",
|
||
"problem": "怎样让代码行为不使使用者惊讶。",
|
||
"mechanism": "以符合常识和可预测性作为取舍方向。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"原文将不惊讶与用户体验、可预测性与信任相联系。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-programming-dao-81fc7ae",
|
||
"heading_id": "philosophy-programming-dao-16-最小惊讶原则",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-validated-abstraction",
|
||
"name": "真实使用验证抽象",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "抽象是有代价的。\n\n好的抽象减少复杂性，坏的抽象制造复杂性。没有被真实使用验证过的抽象，往往只是提前制造复杂度。\n\n过早抽象，很多时候比适度重复更糟。",
|
||
"text_sha256": "9c35a2abb7c73d0d6ef2a6a0ad58897246a69aab7a8c44331a34b2a1be6863ee",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "抽象需要承担成本，错误抽象可能比适度重复更糟，应由真实使用检验。",
|
||
"problem": "如何取舍重复代码与抽象。",
|
||
"mechanism": "区分减少复杂性的好抽象和制造复杂性的坏抽象，警惕未被真实使用验证的抽象。",
|
||
"conditions": [],
|
||
"limitations": [
|
||
"抽象有代价，过早抽象可能比适度重复更糟。"
|
||
],
|
||
"expected_outcomes": [
|
||
"好的抽象减少复杂性。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-software-engineering-truths-81fc7ae",
|
||
"heading_id": "philosophy-software-engineering-truths-5-重复代码不一定坏错误抽象更坏",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-technical-debt-ledger",
|
||
"name": "技术债显式登记",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "为了赶进度牺牲质量，有时是现实选择。\n\n但技术债必须被看见、被记录、被评估。债可以借，但要知道借了多少，利息是什么，什么时候还。\n\n“以后再重构”通常意味着“以后也不会重构”。",
|
||
"text_sha256": "53099acdfb332ed247425ff5707c1b29130918accb29fdeb2fd1342545224d44",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "技术债可以借，但须被看见、记录、评估并知道数量、利息和偿还时间。",
|
||
"problem": "如何对现实交付折衷及后续偿还负责。",
|
||
"mechanism": "显式记录并评估技术债，说明借了多少、利息是什么、什么时候还。",
|
||
"conditions": [
|
||
"为了赶进度牺牲质量有时是现实选择。"
|
||
],
|
||
"limitations": [
|
||
"只有“以后再重构”的承诺通常不会落实。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-software-engineering-truths-81fc7ae",
|
||
"heading_id": "philosophy-software-engineering-truths-14-技术债不是罪假装没有技术债才是罪",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-performance-by-measurement",
|
||
"name": "性能定位以测量为准",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "过早优化是万恶之源。\n\n先让它跑起来，再让它正确，最后才是让它快。\n\n大多数性能问题不是靠猜出来的，而是靠监控、profiling、压测和真实数据定位出来的。",
|
||
"text_sha256": "77c5408834e12ba77ca1cd8a92f0f254cb9a5481d5908d50055edd75de2770fd",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "性能问题应靠监控、profiling、压测和真实数据定位，而不是凭感觉。",
|
||
"problem": "性能问题如何定位。",
|
||
"mechanism": "用监控、profiling、压测及真实数据定位；原文还提出先跑起来、再正确、最后快的次序。",
|
||
"conditions": [],
|
||
"limitations": [
|
||
"原文警示过早优化，不能只靠猜测定位多数性能问题。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-software-engineering-truths-81fc7ae",
|
||
"heading_id": "philosophy-software-engineering-truths-16-性能问题要靠测量不要靠感觉",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-controllable-failure",
|
||
"name": "故障可控与恢复优先",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "稳定系统靠的不是永远不出问题，而是出问题时能够被发现、被定位、被隔离、被恢复。\n\n日志、监控、告警、追踪、降级、限流、回滚，都是系统稳定性的一部分。\n\n日志不是给程序看的，是给凌晨三点处理事故的人看的。",
|
||
"text_sha256": "c84538242086e542ab36b740bbf33388e676b0ed1bbd17c06848b023eec722d6",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "稳定不是永不出错，而是故障能够被发现、定位、隔离和恢复。",
|
||
"problem": "有故障时系统怎样保持可控。",
|
||
"mechanism": "围绕发现、定位、隔离、恢复组织稳定性能力，日志、监控、告警、追踪、降级、限流、回滚是其中部分。",
|
||
"conditions": [],
|
||
"limitations": [
|
||
"稳定不依赖永远不出问题。"
|
||
],
|
||
"expected_outcomes": [
|
||
"出问题时能够被发现、被定位、被隔离、被恢复。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-software-engineering-truths-81fc7ae",
|
||
"heading_id": "philosophy-software-engineering-truths-18-稳定不是没有故障而是故障可控",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-decision-rationale-documentation",
|
||
"name": "文档记录设计理由",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "文档不是为了证明你写过什么，而是为了让别人少猜。\n\n尤其是记录“为什么这么做”的文档，通常比解释“代码做了什么”更有价值。\n\n代码能说明系统当前怎么运行，但文档能解释当时为什么做出这个选择。",
|
||
"text_sha256": "fb91b1b518e5156f658a2ef9dfd3a6e09a98a69606c7b48f98d056dd7e085306",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "文档不是装饰，尤其应解释当时为什么这样选择。",
|
||
"problem": "怎样减少他人对设计背景的猜测。",
|
||
"mechanism": "文档解释选择理由，代码说明系统当前怎样运行，二者承担不同说明功能。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"让别人少猜，解释当时为什么做出选择。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-software-engineering-truths-81fc7ae",
|
||
"heading_id": "philosophy-software-engineering-truths-23-文档不是装饰品",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-dataset-first-architecture",
|
||
"name": "Dataset First 数据服务范式",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "适合长期运行、补数、巡检、血缘、质量治理的数据产品服务。\n\n判断规则：\n\n> 如果服务的核心交付物是“稳定数据集”，而不是页面、接口或一次性脚本，就优先使用 Dataset First。\n\n<a id=\"reference-engineering-practice-一句话\"></a>\n##### 一句话\n\n以 dataset 为边界，以 schema/data contract 为先，以 runtime/registry/config 为共享控制面，以 collect/backfill/repair/validate 为实现单元。\n\n<a id=\"reference-engineering-practice-适合\"></a>\n##### 适合\n\n- 行情事实采集服务。\n- 另类事件采集服务。\n- 周期轮询快照服务。\n- 原子事件流 + 时间桶聚合并存的数据服务。\n- 需要长期运行、补数、巡检、血缘、质量治理的数据服务。\n\n<a id=\"reference-engineering-practice-不适合直接照抄\"></a>\n##### 不适合直接照抄\n\n- 纯 API 网关。\n- 纯 Web 应用。\n- 纯交易执行服务。\n- 一次性脚本工具。\n- 不产出稳定 dataset 的临时任务。\n\n<a id=\"reference-engineering-practice-核心原则\"></a>\n##### 核心原则\n\n1. Dataset First：顶层先按 dataset 划分，而不是按 `collector/parser/writer/task` 划分。\n2. Contract First：先定义目标落表、字段语义、主键、时间列、分区策略和刷新粒度。\n3. Layered Modeling：原子层、聚合层、事件流、时间桶、运行状态分开建模。\n4. Shared Control Plane：`config.py`、`registry.py`、`service_entry.py`、`runtime/*` 统一收口。\n5. Legacy Is Explicit：迁移期 legacy 壳只能兼容转发，新逻辑不得回流旧路径。",
|
||
"text_sha256": "45bb5f2e9244ad307d77b85aea7a182e2c153fb56c6fbba253e025c8abe61282",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收目录模板之前的判断规则、适用/不适用范围及核心原则；不收目录图或后续构建/接入流程。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "稳定数据集服务优先按 dataset 分边界，先定契约、区分数据层、共享控制面并显式隔离 legacy。",
|
||
"problem": "核心交付物为稳定数据集的服务怎样组织。",
|
||
"mechanism": "顶层按 dataset 划分，先定义落表、字段语义、主键、时间与分区/刷新策略；数据层分开，控制面统一，legacy 只兼容转发。",
|
||
"conditions": [
|
||
"核心交付物是稳定数据集，而不是页面、接口或一次性脚本。",
|
||
"适用于事实/事件采集、周期快照、事件流与时间桶并存，以及长期补数、巡检、血缘和质量治理的数据服务。"
|
||
],
|
||
"limitations": [
|
||
"纯 API 网关、纯 Web 应用、纯交易执行服务、一次性脚本及不产出稳定 dataset 的临时任务不适合直接照抄。",
|
||
"迁移期 legacy 壳只能兼容转发，新逻辑不得回流旧路径。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-dataset-first-data-service-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-7-dataset-first-数据服务结构",
|
||
"selection": "markdown-prefix",
|
||
"stop_before": "<a id=\"reference-engineering-practice-标准目录\"></a>"
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-dataset-service-construction",
|
||
"name": "新建数据服务作业流",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "1. 先定 dataset 清单：哪些 active、哪些 backfill_only、哪些 reserved。\n2. 先写 contract：字段、主键、时间列、分区策略、资源 ID、schema version。\n3. 再建 registry、config、service_entry、runtime。\n4. 逐个实现 dataset：`contract -> writer -> collect -> backfill -> validate -> repair`。\n5. 最后补 README、AGENTS、verify/CI、资源目录、血缘映射、smoke。",
|
||
"text_sha256": "baf1bafb0ba2902b325e23c3b23b87152af259ade01b0d4ec07ea9e9169646cc",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "先定数据集清单与契约，再建控制面，逐个实现数据集，最后补文档、验证和血缘。",
|
||
"problem": "怎样新建数据服务。",
|
||
"mechanism": "清单 -> contract -> registry/config/service_entry/runtime -> contract、writer、collect、backfill、validate、repair -> README、AGENTS、verify/CI、资源目录、血缘、smoke。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-dataset-first-data-service-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-新建数据服务流程",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-external-source-dataset-adaptation",
|
||
"name": "外部源码数据集化接入",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "1. 先盘点外部源码实际产出的数据对象，不先搬代码。\n2. 把原项目脚本反向映射为 dataset。\n3. 将 API client、auth、rate limiter、storage client、retry/backoff 抽到 `common/`、`runtime/` 或 `writers/`。\n4. 将 legacy 壳显式隔离，只允许兼容转发，不允许承载新逻辑。",
|
||
"text_sha256": "235adcd7c283cc1ab19d2f54f49ad677c2a7d9edf3a43d26dadc792c44cf5620",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "先按真实数据对象反向整理外部脚本，再抽出公共能力并隔离 legacy。",
|
||
"problem": "怎样把外部源码接入数据服务结构。",
|
||
"mechanism": "盘点实际数据对象，不先搬代码；脚本映射为 dataset；client/auth/限流/storage/retry-backoff 抽至公共层、runtime 或 writer；legacy 只做兼容转发。",
|
||
"conditions": [],
|
||
"limitations": [
|
||
"legacy 壳不能承载新逻辑，只允许兼容转发。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-dataset-first-data-service-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-外部源码接入流程",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-symbol-name-index-maintenance",
|
||
"name": "变量名索引维护",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "<a id=\"reference-engineering-practice-11-新建变量名大全文件\"></a>\n#### 1.1 新建“变量名大全文件”\n\n建立一个统一的变量索引文件，用于 AI 以及团队整体维护。\n\n<a id=\"reference-engineering-practice-文件内容包括格式示例\"></a>\n##### 文件内容包括（格式示例）：\n\n| 变量名 | 变量注释（描述） | 出现位置（文件路径） | 出现频率（统计） |\n| -------- | -------- | -------------------- | -------- |\n| user_age | 用户年龄 | /src/user/profile.js | 12 |\n\n<a id=\"reference-engineering-practice-目的\"></a>\n##### 目的\n\n* 统一变量命名\n* 方便全局搜索\n* AI 或人工可统一管理、重构\n* 降低命名冲突和语义不清晰带来的风险",
|
||
"text_sha256": "d29e54373844c7f3ae0f9e1f93dd7f08869228cdafc85bbc04f08119af0e1963",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "建立统一变量索引，列出名称、说明、位置和出现频率供团队与 AI 维护。",
|
||
"problem": "变量命名如何统一登记和维护。",
|
||
"mechanism": "创建变量名大全文件，按变量名、描述、文件路径和出现频率组织条目。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"统一命名、方便全局搜索与统一管理/重构，降低命名冲突和语义不清风险。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-development-experience-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-1-变量名维护方案",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-input-state-transform-separation",
|
||
"name": "输入输出与状态变换分离",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "method-specification",
|
||
"content": {
|
||
"original_text": "系统行为应明确划分：\n\n| 概念 | 说明 |\n| ------ | -------------- |\n| 消费端 | 接收外部数据或依赖输入的地方 |\n| 生产端 | 生成数据、输出结果的地方 |\n| 状态（变量） | 存储当前系统信息的变量 |\n| 变换（函数） | 处理状态、改变数据的逻辑 |\n\n明确区分 **输入 → 处理 → 输出**，并独立管理每个环节。",
|
||
"text_sha256": "5ecabc148817ed14ae49cafac8bfda07428578a9c95dd4262971b8bb3ee9a7cb",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "区分消费端、生产端、状态和变换，独立管理输入处理输出环节。",
|
||
"problem": "系统行为怎样按输入、输出、状态和处理逻辑划分。",
|
||
"mechanism": "消费端接收外部数据，生产端生成结果，状态存储系统信息，变换处理状态；输入、处理、输出独立管理。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-development-experience-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-33-消费端-生产端-状态变量-变换函数",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-shared-resource-concurrency",
|
||
"name": "共享资源与并发安全约束",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "* 清晰区分共享资源\n* 避免数据竞争\n* 必要时加锁或使用线程安全结构\n* 区分“并发处理”和“异步处理”的差异",
|
||
"text_sha256": "80b42a60c078ff6669a80f0f3c8ea724cb6c769eba351e8cc9089251766ab30a",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "并发设计应识别共享资源、避免竞争、必要时使用同步，并区分并发与异步。",
|
||
"problem": "共享资源怎样避免数据竞争。",
|
||
"mechanism": "清晰区分共享资源，必要时加锁或使用线程安全结构，区分并发处理与异步处理。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [
|
||
"避免数据竞争。"
|
||
],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-development-experience-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-34-并发concurrency",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-dry-reuse",
|
||
"name": "DRY 逻辑复用准则",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "用函数、类、模块复用逻辑，不要复制粘贴。",
|
||
"text_sha256": "11cd67ed2c3cb02be26a68472a22711530f4a9900aa6dfb84024df080fb2bf1f",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "用函数、类、模块复用逻辑，而不是复制粘贴。",
|
||
"problem": "重复逻辑怎样取舍。",
|
||
"mechanism": "使用函数、类、模块复用逻辑。",
|
||
"conditions": [],
|
||
"limitations": [],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"limitations",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-development-experience-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-54-dry-原则不要重复",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-input-scale-complexity-constraints",
|
||
"name": "输入规模与复杂度约束",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "```text\n禁止不知道输入规模就选择算法\n禁止不估算最坏情况复杂度\n禁止只看一层循环而忽略循环内部函数的复杂度\n禁止忽略 in、index、count、remove 在 list 上是线性复杂度\n禁止忽略切片会复制数据\n禁止忽略 sorted 是 O(n log n)\n禁止忽略 dict / set 平均 O(1) 但最坏情况和内存成本仍需考虑\n禁止忽略递归深度和重复子问题\n禁止把两层循环一概视为不可接受，而不分析 n 的实际大小\n禁止把单层循环一概视为高性能，而不分析循环体成本\n禁止把 O(n) 算法写成多次 O(n) 串联却不评估常数和内存\n禁止在性能关键路径里嵌套调用隐藏全表扫描的 helper 函数\n禁止封装 helper 后忘记其内部复杂度\n```",
|
||
"text_sha256": "908cc7665d599efa46196df220fdf07a9b68a2d73bc400739c02c7dfb56458e7",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "选择算法前检查输入规模、最坏复杂度、容器操作、复制及被调用函数成本。",
|
||
"problem": "怎样避免只看循环表面而误判复杂度。",
|
||
"mechanism": "要求考虑输入规模、循环体/helper 成本、list 线性操作、切片复制、排序、dict/set 最坏成本、递归重复子问题，以及常数和内存。",
|
||
"conditions": [],
|
||
"limitations": [
|
||
"两层循环不必然不可接受，单层循环不必然高性能。",
|
||
"dict/set 的平均 O(1) 不免除最坏情况和内存成本审查。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"conditions",
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-quality-gates-and-pitfalls-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-三新手常见复杂度误区",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
},
|
||
{
|
||
"id": "gongfa-cache-lifecycle-isolation",
|
||
"name": "缓存生命周期与隔离约束",
|
||
"aliases": [],
|
||
"content_version": "r1",
|
||
"predecessor_version": null,
|
||
"entry_status": "registered",
|
||
"ontology_type": "normative-allocation-content",
|
||
"content": {
|
||
"original_text": "```text\n禁止无脑给函数加 lru_cache\n禁止缓存带副作用函数\n禁止缓存依赖外部状态但 key 不包含外部状态版本的信息\n禁止缓存会频繁变化的数据\n禁止缓存大型返回值却不限制 maxsize\n禁止缓存用户级敏感数据却不做隔离\n禁止用全局 dict 当永久缓存\n禁止没有缓存失效策略\n禁止没有缓存命中率观测\n禁止缓存 key 设计过细导致几乎不命中\n禁止缓存 key 设计过粗导致返回错误结果\n禁止在多进程环境误以为本地缓存共享\n禁止在分布式环境误以为单机缓存全局一致\n禁止为了避免计算而引入更高的内存和一致性成本\n```",
|
||
"text_sha256": "3ca37523c3c65d4ed5db914528885cb9224ede6067c91e3a6e857585ebf28005",
|
||
"claim_status": "source_statement",
|
||
"selection_note": "只收此独立限定节，不借同篇其他内容补字段；来源要求不成为本次执行或发布授权。",
|
||
"profile": {
|
||
"provenance": "editorial_summary",
|
||
"summary": "缓存必须审查对象、状态版本、容量、隔离、失效和观测，不假定本地缓存自动共享或全局一致。",
|
||
"problem": "怎样避免缓存的正确性、隔离和成本误用。",
|
||
"mechanism": "约束副作用与外部状态缓存，要求合理 key、容量上限、敏感数据隔离、失效策略及命中率观测；核对多进程和分布式语义。",
|
||
"conditions": [
|
||
"原文针对带外部状态、用户级敏感数据、大型返回值及多进程/分布式缓存场景提出相应约束。"
|
||
],
|
||
"limitations": [
|
||
"不应缓存带副作用函数或会频繁变化的数据。",
|
||
"单机或本地缓存不自动全局一致或进程共享。",
|
||
"避免计算不能以更高的内存和一致性成本为代价。"
|
||
],
|
||
"expected_outcomes": [],
|
||
"questions": [],
|
||
"missing_fields": [
|
||
"expected_outcomes",
|
||
"questions"
|
||
]
|
||
}
|
||
},
|
||
"source_ref": {
|
||
"source_id": "repo-quality-gates-and-pitfalls-81fc7ae",
|
||
"heading_id": "reference-engineering-practice-六缓存误用",
|
||
"selection": "markdown-section",
|
||
"stop_before": null
|
||
}
|
||
}
|
||
],
|
||
"assessments": [],
|
||
"grade_proposals": [
|
||
{
|
||
"id": "gongfa-author-initial-20261007",
|
||
"vocabulary_id": "gongfa-grade-vocabulary-v1",
|
||
"policy_id": "gongfa-grading-policy",
|
||
"policy_version": "0.1.0",
|
||
"status": "provisional",
|
||
"basis": "expert_judgment",
|
||
"assessor": "AI编者人工初评；非独立评审者",
|
||
"assessed_at": "2026-10-07T07:47:52.872364Z",
|
||
"application_context": "在中文软件工程知识库中选择并应用功法：以各条task_family中的判断、设计、排错或受控交付为目标，先要求正确性、安全、权限和可验收性，再考虑效果、可靠性与成本；所需观测、技能和验证能力需另行满足。任务集、环境、预算与阈值尚未实验冻结。",
|
||
"baseline_assumption": "假定对照采用胜任的现行需求、设计、实现和相关验证流程，在相同目标、信息、环境及预算下比较；不故意选择不测试、忽略安全或能力不足的弱基线。若基线已完整采用同一方法，则不得据初评推定额外增益。此处是前瞻假设，不是已运行基线。",
|
||
"limits": [
|
||
"本批次是基于限定原文的人工内容判断与预计结果带，不是实验分数、实际效果评级或独立语义批准。",
|
||
"类型不决定档位。按0.1.0的黄/玄/地/天方向预估限定任务效果、合理基线增益、困难/迁移表现和能力缺口；未借标题、字数、步骤数、名气、抽象性或形式化程度打分。",
|
||
"初/中/高表示本阶预计结果带而非置信度、次数或等距倍率；区间尚未校准，不能跨任务、跨类型直接排名。",
|
||
"confidence只是作者对前瞻判断的主观把握标签，不是准确率、概率或实测可靠性；所有效果条件都还需要受控验证。",
|
||
"拼好码按2026-10-11用户指令修订为天阶高级暂定；组块理论关联仅作结构类比和机制假设，不构成效能证据；其余条目意见及未校准范围保持不变。",
|
||
"每条falsifier指出可推翻或下调初评的条件，不是已经执行的实验或失败记录。内容版本变更不自动继承本批意见；正式评级仍另追加assessments。"
|
||
],
|
||
"supersedes": null,
|
||
"items": [
|
||
{
|
||
"gongfa_id": "gongfa-compositional-description",
|
||
"content_version": "r1",
|
||
"content_sha256": "f9f7bfd64ba4484aae25d1c51c961ed7ab2b44ce2e2276dbc5e3476faf8d4649",
|
||
"ontology_type": "explanatory-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "复杂系统的对象、状态和关系辨析",
|
||
"classification_reason": "限定开篇解释实体、过程、关系与状态的组合结构，不是行动规程。",
|
||
"reason": "预计能减少把载体、内容、状态变化混为一谈的建模错误；尚不足以单独突破既有设计方法的能力边界。",
|
||
"confidence": "medium",
|
||
"falsifier": "若在保留同一性和变化的案例中仍产生类别混淆，或基线已有同等表达能力，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-network-effects",
|
||
"content_version": "r1",
|
||
"content_sha256": "a40073db5c1a98d5392dc472ee59ccb5712f07e90f23684965bd5d4e9f64c8a6",
|
||
"ontology_type": "explanatory-content",
|
||
"grade_id": "huang-advanced",
|
||
"task_family": "产品或工具生态的网络效应识别",
|
||
"classification_reason": "限定原文只解释使用者增加与价值变化的关系，不包含后文选型步骤。",
|
||
"reason": "预计能提示分析规模与价值的关联，辅助常规判断；不能仅凭这条关系推出增长收益或优于合理选型基线。",
|
||
"confidence": "low",
|
||
"falsifier": "若忽略负外部性、拥塞或平台情境而误判价值，或不能区分相关性和机制，初评不成立。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-occams-razor",
|
||
"content_version": "r1",
|
||
"content_sha256": "6003a6c40e6602423049b5b963a4a3f741cbe224f98e3090d85f1df76a8753b4",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "di-initial",
|
||
"task_family": "等效方案的复杂度取舍",
|
||
"classification_reason": "在解释或验收等效时要求减少不必要对象，是有约束的选择准则。",
|
||
"reason": "预计在多方案和遗留设计取舍中减少无价值复杂度，同时保留关键约束；困难情境的增益仍须检验。",
|
||
"confidence": "medium",
|
||
"falsifier": "若删减破坏正确性、安全或必要表达能力，或简化仅把成本转嫁调用者，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-glue-principle",
|
||
"content_version": "r1",
|
||
"content_sha256": "e761c7f0a020ec6e5fd12ebfa2496fc869f7dd29a982fc849a9c0b849d8ef44f",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "技术选型与成熟能力复用",
|
||
"classification_reason": "规定成熟能力优先以及自研例外、偏离说明和替换边界，主内容是工程准则。",
|
||
"reason": "预计在陌生技术和维护约束较强的任务中减少自研错误与长期所有权成本，但复用必须满足关键约束。",
|
||
"confidence": "medium",
|
||
"falsifier": "若依赖无法维护、集成成本超过收益、供应链风险失控，或关键约束不满足，初评应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-first-principles",
|
||
"content_version": "r1",
|
||
"content_sha256": "e92e76bdb2c16e50f9303929b3383074564aaa4b4728859b3deba3750790e71a",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-advanced",
|
||
"task_family": "错误前提下的问题重构与方案重推",
|
||
"classification_reason": "明确从前提列举、归类、验证、重推到小实验的施行路径。",
|
||
"reason": "预计在需求含错误假设的困难或迁移任务中纠正问题定义并重建可行方案；不是通用最优解保证。",
|
||
"confidence": "medium",
|
||
"falsifier": "若不能识别错误前提，重推方案违反事实或预算，或成熟基线已经同样处理这些前提，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-thought-experiment",
|
||
"content_version": "r1",
|
||
"content_sha256": "b71449ec04a516c2352599fcc0c4a514c6175731f5ea087b3245eade73e16ded",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "高成本试验前的方案边界检查",
|
||
"classification_reason": "规定暂时放开现实限制、推演边界、恢复约束并落到实验，属于方法蓝图。",
|
||
"reason": "预计能在真实试错前排除部分矛盾和边界缺陷；不能用设想替代现实验证或保证困难任务达标。",
|
||
"confidence": "medium",
|
||
"falsifier": "若推演依赖错误世界模型、忽略关键现实约束，或预言经受控实验否定，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-reverse-thinking",
|
||
"content_version": "r1",
|
||
"content_sha256": "00d4217a7a7a70c57e118f23008da53af03a16daf7c4dddd9134571b4e8fdf23",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "方案失败条件与禁区识别",
|
||
"classification_reason": "从反面目标列举失败、提炼约束并反向验证，是明确的分析操作。",
|
||
"reason": "预计较只顺向寻找成功路径更容易暴露已知失败条件；对未知失败和远距离迁移仍有限。",
|
||
"confidence": "medium",
|
||
"falsifier": "若反面清单遗漏主要风险、无法转成可检验约束，或约束本身阻断允许方案，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-multi-order-thinking",
|
||
"content_version": "r1",
|
||
"content_sha256": "490b2421a035dcefafffa690516bc6100d3d2ffb4633b6febab87e2ed7f01186",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "决策后续影响与反馈审查",
|
||
"classification_reason": "要求沿一阶、二阶及更后影响推演，并核验重要假设。",
|
||
"reason": "预计在有反馈与延迟的决策中减少只看即时收益的错误；长链影响仍有模型和观测不确定性。",
|
||
"confidence": "medium",
|
||
"falsifier": "若后果链主要来自猜测、忽略共同原因或反馈方向，或延误超过决策预算，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-state-space-thinking",
|
||
"content_version": "r1",
|
||
"content_sha256": "cafcf77b67302d17563e03a6e0017dc3cf48e3432af5174079f21b93fa3dc2ba",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "状态空间中的目标、路径和约束建模",
|
||
"classification_reason": "包含状态描述、目标确定、约束、路径比较与验证，规定如何求解。",
|
||
"reason": "预计在多路径和多约束任务中帮助排除不可达方案并保留可验证路径；状态不可识别时不能保证效果。",
|
||
"confidence": "medium",
|
||
"falsifier": "若关键状态或约束遗漏、状态空间不可控爆炸，或路径无法实际验证，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-phenomenological-reduction",
|
||
"content_version": "r1",
|
||
"content_sha256": "95e68a224625233d5a3acb6f97974ead0c576737ba427264d919c84505739c17",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "排错与需求中的观察假设分离",
|
||
"classification_reason": "给出悬置解释、记录可观测现象并逐层恢复假设的步骤。",
|
||
"reason": "预计能减少把推测当事实造成的排错偏航；没有独立观测时不保证找到原因。",
|
||
"confidence": "medium",
|
||
"falsifier": "若观察本身不可靠、假设被隐含带回，或区分后仍不能指导下一次判别，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-dialectical-iteration",
|
||
"content_version": "r1",
|
||
"content_sha256": "3c2de763fc155c41569ab7f11acabe5bfeb9b0817a73ab1e494ad2cee1099d07",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "冲突方案的比较和小步综合",
|
||
"classification_reason": "按正题、反例、综合方案和验证组织迭代，不是单纯关系解释。",
|
||
"reason": "预计能改善单一方案的盲区与折衷表达；综合方案仍可能不满足硬约束。",
|
||
"confidence": "medium",
|
||
"falsifier": "若综合只是平均冲突而未解决约束，或无法用独立验证区分方案质量，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-falsification",
|
||
"content_version": "r1",
|
||
"content_sha256": "411481762ecb2c3ec1537e56314e0d16f154c58484bc3514981842437dbf2937",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "假设和方案的反例验证",
|
||
"classification_reason": "要求事先提出可推翻条件、设计反例并按结果修正假设。",
|
||
"reason": "预计在错误高风险及迁移任务中更早淘汰不成立主张，保留反例敏感性；有限通过不证明普适。",
|
||
"confidence": "medium",
|
||
"falsifier": "若反例不能区分真假假设、测试不敏感，或失败后通过改目标保住原结论，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-lightweight-formal-methods",
|
||
"content_version": "r1",
|
||
"content_sha256": "bee84e77c89b64a2aefb1f6fff50c9c181028905364860b295356f1f4d47c13c",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-advanced",
|
||
"task_family": "协议、不变量与边界条件检查",
|
||
"classification_reason": "把约束写成契约、状态和不变量，再用工具及反例检查，主内容是操作方法。",
|
||
"reason": "预计在复杂协议与边界案例中发现常规示例测试遗漏的不一致；结论受模型和工具保证范围限制。",
|
||
"confidence": "medium",
|
||
"falsifier": "若形式模型遗漏用户关键性质、验证方向错误，或工具通过却实际契约违约，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-toolbox-occams-razor",
|
||
"content_version": "r1",
|
||
"content_sha256": "cb2ea2e0c4a4e675b0995da687724011dac1959d566ad7a1c6f1d9c218914a0a",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "冗余模块和解释的等效简化",
|
||
"classification_reason": "工具箱节给出候选简化、约束保留与反证操作，区别于原准则条目。",
|
||
"reason": "预计能稳定减少可证明冗余并保持行为；不能把所有复杂度或防护都视为冗余。",
|
||
"confidence": "medium",
|
||
"falsifier": "若简化导致行为、权限或恢复能力回归，或维护成本只是转移，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-pragmatic-optimization",
|
||
"content_version": "r1",
|
||
"content_sha256": "4d363457999e0d2f1e2773e0f49e98fe2b15147c91b35cdd069d57e82dd1dfcf",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "以实际用户指标检验工程方案",
|
||
"classification_reason": "提出目标指标、小实验、比较和反馈更新的实际流程。",
|
||
"reason": "预计能减少纯风格或理论偏好的无效优化；指标偏差和局部收益可能抵消增益。",
|
||
"confidence": "medium",
|
||
"falsifier": "若指标与用户结果脱节、收益无法重现，或正确性和安全被效率抵消，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-systems-boundary-feedback",
|
||
"content_version": "r1",
|
||
"content_sha256": "72cbec4e59f98ee44b9777113ed7aa97646441f30f1be47f61f72bdca56f64c0",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "跨模块依赖、边界和反馈诊断",
|
||
"classification_reason": "规定画系统边界、追踪依赖及反馈并做局部干预的分析方法。",
|
||
"reason": "预计在跨组件困难问题中暴露局部修补遗漏的反馈与边界，避免把系统缺陷误判为单点问题。",
|
||
"confidence": "medium",
|
||
"falsifier": "若边界排除了真实因果链、干预影响不可分辨，或只画关系图却无法检验解释，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-hermeneutic-clarification",
|
||
"content_version": "r1",
|
||
"content_sha256": "f2706f772cea6476b19dd708843b3fbc9ef2d2ba56798137ddb37aba91dd0ef7",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "需求措辞与使用语境澄清",
|
||
"classification_reason": "按语境、术语、例子、反例和回读核对要求，属于澄清操作。",
|
||
"reason": "预计能减少同词异义和用户意图误读，改善常规需求质量；不能代替事实或验收确认。",
|
||
"confidence": "medium",
|
||
"falsifier": "若解释仍与用户具体情境不符、关键反例被忽略，或双方确认无法落到验收，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-steelmanning",
|
||
"content_version": "r1",
|
||
"content_sha256": "fdb81961c528388e4abff074033b3b8259cc3cd3ccf95f6a63afdf493a88ef5e",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-initial",
|
||
"task_family": "对立方案的准确理解与比较",
|
||
"classification_reason": "要求重建对方最强合理论证并核对后再反驳，是讨论方法。",
|
||
"reason": "预计能减少稻草人式误判；单独使用不能证明方案质量或解决工程约束。",
|
||
"confidence": "medium",
|
||
"falsifier": "若重建内容并非对方观点、替对方补造事实，或最强论证仍不能映射到目标，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-reversible-decisions",
|
||
"content_version": "r1",
|
||
"content_sha256": "144eeadd97fcb638796da5943cd2396920d750ecbf0d1d0fc031f15563cd9f8b",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "不确定条件下的可回退方案选择",
|
||
"classification_reason": "以可逆性、机会成本、试点与回退安排形成决策步骤。",
|
||
"reason": "预计能降低错误决策的恢复成本并加快有界学习；不可逆边界仍须独立保护。",
|
||
"confidence": "medium",
|
||
"falsifier": "若回滚无法执行、数据副作用不能恢复，或可逆方案的持续成本超过收益，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-counterfactual-testing",
|
||
"content_version": "r1",
|
||
"content_sha256": "e57e942bf45b08954531e3b0be0e11580237d5017f773e5c024b7d1b4b03dce0",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "因果解释与验证器的反事实检查",
|
||
"classification_reason": "规定改变关键前提、保留其他条件并检查结论是否敏感。",
|
||
"reason": "预计能在困难排错与迁移验证中暴露自证、混杂和不敏感测试；反事实必须可比较。",
|
||
"confidence": "medium",
|
||
"falsifier": "若改变多个混杂因素、预期值复写实现，或测试对已知缺陷仍不敏感，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-abductive-debugging",
|
||
"content_version": "r1",
|
||
"content_sha256": "a9b821e8da4740b609d4252e935dbd7859e9393663e922c5a0ed6904be03682b",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "多候选原因的故障诊断",
|
||
"classification_reason": "限定仓内步骤提出候选解释、最小区分实验并按证据淘汰。",
|
||
"reason": "预计在多原因且成本受限的故障中减少盲改和无信息重试，较单假设调试更易保留替代解释。",
|
||
"confidence": "medium",
|
||
"falsifier": "若候选集遗漏真实原因、实验无法区分假设，或观察失败却不更新解释，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-bayesian-hypothesis-updating",
|
||
"content_version": "r1",
|
||
"content_sha256": "f0fc67cb532e476080cdfbf0d830108ec39418fbf35328f45a65de2d0596a37e",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "随证据更新的故障假设排序",
|
||
"classification_reason": "与溯因配合规定先验、证据更新及下一次区分实验的安排。",
|
||
"reason": "预计能减少新证据下的固执排序；证据相关性和错误先验会限制可靠性。",
|
||
"confidence": "medium",
|
||
"falsifier": "若重复证据被当成独立支持、基率方向弄反，或更新不改变下一次有效判别，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-reflective-equilibrium",
|
||
"content_version": "r1",
|
||
"content_sha256": "f0fce3eba9822fb208e58243272994309129d7fc01d3b605fcc16247403474ef",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "规则、案例与判断之间的校准",
|
||
"classification_reason": "在规则、具体判断及反例之间反复调整并核对一致性。",
|
||
"reason": "预计能改善局部规则和案例的冲突；共同偏差可能形成一致但错误的平衡。",
|
||
"confidence": "medium",
|
||
"falsifier": "若案例与规则共享错误来源，或只调整规则迁就已有结论而没有独立检验，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-concept-engineering",
|
||
"content_version": "r1",
|
||
"content_sha256": "b6ecc05eea18dcbd23d204a4169b41fc6f2518523ed112fbe24ae6a827c0f71a",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "API、需求和领域术语的边界设计",
|
||
"classification_reason": "要求列定义、例子、反例和边界，修正概念并核对使用。",
|
||
"reason": "预计能减少接口与沟通中的歧义和错误外推；新定义须保留消费者可观察契约。",
|
||
"confidence": "medium",
|
||
"falsifier": "若边界不支持真实用例、定义循环，或统一术语反而抹去必要差异，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-methodological-doubt",
|
||
"content_version": "r1",
|
||
"content_sha256": "4c202f4edc18569af1f7e45b3626b6d0cf7100c29903d1829ed23098021a402f",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-initial",
|
||
"task_family": "高风险前提的证据审查",
|
||
"classification_reason": "按信息来源、可信前提与最小验证重建判断，是施行方法。",
|
||
"reason": "预计能促使暴露未经核实的前提；过度怀疑会耗尽预算或延迟允许决策。",
|
||
"confidence": "medium",
|
||
"falsifier": "若无法按风险收敛审查、可信事实被无理由反复推翻，或验证成本超过收益，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-evidence-triangulation",
|
||
"content_version": "r1",
|
||
"content_sha256": "366b519324e28a0d9507b34c7338bcf82f584f5d1804ba6f9e6bb2e2fe1b47b3",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "多来源解释的独立交叉核对",
|
||
"classification_reason": "规定从不同视角取证、比较冲突并查共同上游。",
|
||
"reason": "预计能发现单一来源的偏差并提高判断可靠性；通道数量不等于证据独立性。",
|
||
"confidence": "medium",
|
||
"falsifier": "若多个通道复用同一错误源、冲突被多数票掩盖，或新增来源不提供区分信息，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-mechanistic-explanation",
|
||
"content_version": "r1",
|
||
"content_sha256": "0c8a0cb4a0ce1623aa9937e7b4e3c0588fcbe902cdef2ef123ce2a0842d1bb5e",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "从现象到生成机制的解释检验",
|
||
"classification_reason": "要求建立因果生成链、可观测中介和干预预测并检验。",
|
||
"reason": "预计能在跨层故障中提升解释的可检验性，减少只凭相关性定位；机制仍可能欠决定。",
|
||
"confidence": "medium",
|
||
"falsifier": "若机制无法产生可区分预测、关键中介遗漏，或干预不支持所提因果链，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-failure-mode-analysis",
|
||
"content_version": "r1",
|
||
"content_sha256": "9245e35da05e68b72a9a0519b46919e4ef10c263b29e40f549d9c8421b9a1cbc",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "高风险方案的失败模式与防护分析",
|
||
"classification_reason": "逐项分析可能错误、传播、检测和恢复办法，形成工程方法。",
|
||
"reason": "预计在困难场景中提高对部分失败和恢复路径的覆盖；清单本身不证明防护有效。",
|
||
"confidence": "medium",
|
||
"falsifier": "若主要故障模式仍遗漏、错误检测不敏感，或恢复措施在受控故障中不能执行，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-experimental-design",
|
||
"content_version": "r1",
|
||
"content_sha256": "f06a372c255f5363d8555a0e30999ea6c6c0904d432dcaca20491b168af856e1",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "可区分假设的工程实验设计",
|
||
"classification_reason": "要求将主张转成变量、对照和可核验结果，属于实验施行规约。",
|
||
"reason": "预计能在多因素问题中减少混杂和错误归因，支持困难假设的淘汰；外推范围仍受样本限制。",
|
||
"confidence": "medium",
|
||
"falsifier": "若对照不可比、变量控制失败、oracle不独立，或实验不足以区分候选假设，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-computational-modeling",
|
||
"content_version": "r1",
|
||
"content_sha256": "12d5d8e70ce2fa678f55802b543965ed174d2004b2838629da420da40a1808e8",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "复杂机制的模拟与现实对照",
|
||
"classification_reason": "规定构造可计算模型、运行仿真并对照现实和边界。",
|
||
"reason": "预计能在现实试验昂贵的困难任务中暴露机制边界；模拟收益不能替代真实场景验收。",
|
||
"confidence": "medium",
|
||
"falsifier": "若模型省略主导因素、参数拟合泄漏，或预测在未参与建模案例中失效，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-naturalized-epistemology",
|
||
"content_version": "r1",
|
||
"content_sha256": "75abcbc85937aaa5a14a2d735f9e0f9b208d596cc2bf934eb98cd34139400dc4",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "huang-advanced",
|
||
"task_family": "认知判断与实际行为证据的对照",
|
||
"classification_reason": "工具箱操作版要求将认识问题接到行为观测与机制研究，不只是学派介绍。",
|
||
"reason": "预计能让常规认知主张更贴近可观察行为；该限定内容尚未给出足以预期稳定基线增益的测量合同。",
|
||
"confidence": "low",
|
||
"falsifier": "若行为指标无法测量所讨论性质、相关性被当成机制，或认知解释不能指导检验，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-bayesian-epistemology",
|
||
"content_version": "r1",
|
||
"content_sha256": "253b8d36452ce18562c9c33c42482690217e86eaec25e88b6d9f9d931d2ce5f5",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "不确定判断的先验与证据更新",
|
||
"classification_reason": "将主张、先验、证据和更新组织成可反思的判断步骤。",
|
||
"reason": "预计能改善不确定信息下的判断纪律；先验、似然或基率错误会直接传播到结论。",
|
||
"confidence": "medium",
|
||
"falsifier": "若没有可靠基率却补造概率、重复数据被多次计权，或更新方向错误，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-fixed-point-convergence",
|
||
"content_version": "r1",
|
||
"content_sha256": "228dc7de506eceac46c6f19d9c679c39e43f581991c9c3caae93d021d3d0823a",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "自治任务执行的分层验证与升级",
|
||
"classification_reason": "限定内容规定观察、行动、验证、策略升级及需求回退，主内容是闭环控制方法。",
|
||
"reason": "预计在动态或执行反复失效的任务中更好地区分行动、策略和目标问题，保留人工退出；不预言数学收敛。",
|
||
"confidence": "low",
|
||
"falsifier": "若目标不可识别、验证信号不可靠、升级持续振荡或无法退出，初评应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-recursive-ai-governance",
|
||
"content_version": "r1",
|
||
"content_sha256": "98759af3b5fce57802e630260ff2c3337953f1dd5e850ade13bd365f0e2e2ac1",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "xuan-initial",
|
||
"task_family": "多层代理的生成、审查和授权治理",
|
||
"classification_reason": "同时解释递归自动化、规定验证终止与人类裁决，并给出跨层迭代框架。",
|
||
"reason": "预计能提示跨层治理必需的制约与回退；递归层级增加并不自动产生可靠性或能力突破。",
|
||
"confidence": "low",
|
||
"falsifier": "若误差在跨层反馈中放大、审查与生成同源自证，或人类无法有效裁决退出，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-glue-coding",
|
||
"content_version": "r1",
|
||
"content_sha256": "7ee9dc827a5bceca480aa7d2b969cfd49a097b4791acb970c7e47ede82393c9c",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "tian-advanced",
|
||
"task_family": "复用主导的软件系统组装与演化",
|
||
"classification_reason": "条目把复用价值准则、工程机制及需求到验证的流程作为完整范式表达。",
|
||
"reason": "用户要求将拼好码提升至天阶上品，按项目词表映射为天阶高级（tian-advanced）。与组块理论的关联作为结构类比：把成熟能力组织为可识别、可复用、可组合的单元，再由拼好码完成需求匹配、边界适配和编排，可能使复杂问题的处理转向更高层次的系统组合与业务表达。该机制假设支持用户指定的最高档暂定判断，但并非受控任务结果，尚未证明相对固定合理基线突破了明确能力缺口。",
|
||
"confidence": "low",
|
||
"falsifier": "若预先固定的组块化软件组合任务中，相比合理基线未达到预定正确性、可靠性、成本与可维护性阈值，或组件边界/接口错配、供应链与依赖风险抵消收益，则应下调；若组块化并未带来可重复的高层组合优势，该机制假设亦被推翻。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-attractor-model",
|
||
"content_version": "r1",
|
||
"content_sha256": "526fd4f3ac368afa02c3fff6600aec366b903f3966caf3abd62e236f6ecc11e4",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "xuan-initial",
|
||
"task_family": "持续代码演化的目标与约束设计",
|
||
"classification_reason": "完整条目结合目标状态解释、多目标约束和距离检验更新流程。",
|
||
"reason": "预计能把模糊改进方向转为可比较目标；指标、权重和冲突裁决未可靠映射时只能是规划建议。",
|
||
"confidence": "low",
|
||
"falsifier": "若距离指标下降但真实质量变差、发生指标投机，或冲突不能稳定裁决，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-finite-sample-generative-inference",
|
||
"content_version": "r1",
|
||
"content_sha256": "d4102794df1e8c4e645ccbbf0c41cca46b46ac9770d81290e9315045b7df2b90",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "少量样本下的多假设生成与判别",
|
||
"classification_reason": "给出实例、多个假设、选择、生成、检验和更新的施行路径。",
|
||
"reason": "预计能比从少量样本直接认定唯一规律更好地保留替代假设；有限样本仍不足以消除欠决定性。",
|
||
"confidence": "low",
|
||
"falsifier": "若假设空间排除真实规律、归纳偏置压倒新证据，或新增预测无法区分候选，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-ai-native",
|
||
"content_version": "r1",
|
||
"content_sha256": "643c85507e90ed0759d3e199f7824c074621b0733743dcac611de387c65a1958",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "人机任务、权限和责任配置",
|
||
"classification_reason": "同时提出原生化概念、控制权与责任准则，并规定委派、验证和更新路径。",
|
||
"reason": "预计能使代理委派与可验证能力匹配，减少把自动化程度等同效果；高权限不能抵消责任边界。",
|
||
"confidence": "low",
|
||
"falsifier": "若异常不可识别、结果不可审计、责任无法承担，或减少人工介入放大错误，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-representation-flattening",
|
||
"content_version": "r1",
|
||
"content_sha256": "a562c18f987697cbaa45303b40362db1664212ddaae45c59b488fedadf9b7bfa",
|
||
"ontology_type": "explanatory-content",
|
||
"grade_id": "xuan-initial",
|
||
"task_family": "机器可计算表示的信息损失分析",
|
||
"classification_reason": "解释现实到表格、图、序列等有限表示的关系、归纳偏置和损失边界。",
|
||
"reason": "预计能让表示选择更关注任务必要信息，而非默认使用一种格式；尚不能凭框架保证映射正确。",
|
||
"confidence": "low",
|
||
"falsifier": "若压缩丢失任务关键关系、归纳偏置造成系统性误判，或无法检测表示失真，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-symbolic-space-validated-knowledge",
|
||
"content_version": "r1",
|
||
"content_sha256": "f10f287a30297c1c8b418fa019d9c28d31e590c8460f1d245a8620c6879c57c8",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "huang-intermediate",
|
||
"task_family": "候选知识生成与有效性筛选设计",
|
||
"classification_reason": "把符号空间和有效知识的解释、验证独立性要求及代理筛选闭环一起表达。",
|
||
"reason": "预计能提示区分生成候选与经验支持知识；尚不能把验证诱导的集合直接变成可靠可计算判别器。",
|
||
"confidence": "low",
|
||
"falsifier": "若筛选反馈自证、逻辑通过被当成经验成立，或实际有效性无法独立判别，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-compositional-model",
|
||
"content_version": "r1",
|
||
"content_sha256": "be9c3afcf2ae8c122b8bfa31abfa0a44d9255cf3a3e1ce09300b357366dc0b47",
|
||
"ontology_type": "explanatory-content",
|
||
"grade_id": "huang-advanced",
|
||
"task_family": "复杂系统的层级组合与涌现解释",
|
||
"classification_reason": "限定内容解释单元、组合、层级耦合和涌现关系，不给出唯一工程实现。",
|
||
"reason": "预计能帮助常规跨层描述避免把宏观性质简单等同部件性质；深层物理假设不能据此作为工程事实。",
|
||
"confidence": "low",
|
||
"falsifier": "若跨尺度预测无法检验、不同微观机制被误认唯一，或开放物理假设被当成已定规律，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-solution-space",
|
||
"content_version": "r1",
|
||
"content_sha256": "59bad9ce3bd22586e23e256a39a684aefaa4d38940d65fbcbfbb6c1f51b52c8e",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "约束求解与候选方案搜索",
|
||
"classification_reason": "既定义候选解及组织关系，也给出约束、筛选、搜索和检验的完整求解框架。",
|
||
"reason": "预计能改进候选完整性和可行性筛选；搜索空间规模、目标错设和表征不足仍限制效果。",
|
||
"confidence": "low",
|
||
"falsifier": "若可行域遗漏真实解、搜索不可承受，或优化了错误目标导致用户任务不达标，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-proximate-causal-mechanism",
|
||
"content_version": "r1",
|
||
"content_sha256": "77f74cfde9c59c0b5ac6f410db6e5422ea673a5ac6efad12b976d100b2ffb18a",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "故障末端因果机制的定位与检验",
|
||
"classification_reason": "明确区分背景、中介和直接作用项，再借干预、时序和反事实检验末端机制。",
|
||
"reason": "预计在复杂因果链的局部诊断中减少把上游背景误当直接触发项；不能把近端发现直接推广到全链。",
|
||
"confidence": "low",
|
||
"falsifier": "若共同原因、关键中介或反馈未控制，或局部干预不支持所提触发机制，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-purpose-directed-action",
|
||
"content_version": "r1",
|
||
"content_sha256": "31a074c8b562f62f6527bda26073f006be041f9c69d95c290961340001250133",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "目标、约束与候选路径的多准则选择",
|
||
"classification_reason": "按需求、目标形式化、候选生成、筛选、推演、检验与更新施行。",
|
||
"reason": "预计能减少选到了可行却不符合目标的方案；效用尺度冲突和欠定义目标仍须显式裁决。",
|
||
"confidence": "low",
|
||
"falsifier": "若评价函数未对应需求、多指标被暗中平均，或选中路径不满足硬约束，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-predictive-processing-update",
|
||
"content_version": "r1",
|
||
"content_sha256": "03289192c126eb2b371a7a815ae751228257b23d34ac004c19c31ade87610e21",
|
||
"ontology_type": "explanatory-content",
|
||
"grade_id": "huang-advanced",
|
||
"task_family": "认知模型与证据生成机制的解释",
|
||
"classification_reason": "解释经验、归纳偏置、预测误差和内部模型更新的关系，不是一次训练执行。",
|
||
"reason": "预计能提示审查输入和反馈如何影响可接受模型；不能仅凭认知框架保证更准确的工程判断。",
|
||
"confidence": "low",
|
||
"falsifier": "若输入与反馈被同源操控而未识别，或该解释无法产生可区分预测，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-constrained-state-transformation",
|
||
"content_version": "r1",
|
||
"content_sha256": "4f25d260b533bbd958bd2a29b0115d735cf79cdabc2e8e22f57ff14153f0613a",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "跨领域问题的状态与约束求解表达",
|
||
"classification_reason": "同时提出状态变换理论及理解、生成、选择、检验、更新的求解框架。",
|
||
"reason": "预计能统一表达可行性、可控性与价值约束，帮助常规求解建模；跨领域抽象本身不证明迁移效果。",
|
||
"confidence": "low",
|
||
"falsifier": "若状态或约束不可观测、领域映射损失关键语义，或候选路径不能实际执行，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-minimal-prior-agent-assisted-solving",
|
||
"content_version": "r1",
|
||
"content_sha256": "0cdfa62012abcb3b56875094c294032b92b8f4e9482e77f8bccca44507a9de6e",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "陌生领域的受控代理增强交付",
|
||
"classification_reason": "明确任务定义、检索复用、选型、PoC、实现及双重验证的施行链。",
|
||
"reason": "预计在陌生技术的迁移任务中通过成熟能力和关键假设检验降低知识缺口，前提是权限与来源质量受控。",
|
||
"confidence": "low",
|
||
"falsifier": "若代理借权限扩张替代验证、来源不可靠、接口不可集成，或结果不满足独立验收，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-projection-representation",
|
||
"content_version": "r1",
|
||
"content_sha256": "7ad6e7c29d231787585474c9bad2a64e85a3489c91a776880498a097c397c999",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "任务相关表示的构造与检验",
|
||
"classification_reason": "规定对象与任务结构分析、表示选择、推演、检验、误差诊断和更新。",
|
||
"reason": "预计能改善表示中的必要信息保留与运算简化；信息损失和映射正确性必须具体检验。",
|
||
"confidence": "low",
|
||
"falsifier": "若表示运算与原关系不对应、关键结构丢失，或转换成本超过任务收益，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-state-classification",
|
||
"content_version": "r1",
|
||
"content_sha256": "22960352424cb9e6eaf21f75b78332cfc0685c732dc6f175b579d5400f60773d",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "演化过程的有限状态建模与更新",
|
||
"classification_reason": "明确状态识别、归属、转移推演、结果判定与经验检验，属于分类分析方法。",
|
||
"reason": "预计能改善起点、过渡和判定状态的边界表达；有限状态标签不自动描述所有连续变化。",
|
||
"confidence": "low",
|
||
"falsifier": "若状态重叠、隐藏变量或路径依赖使模型无法可靠判定转移，初评应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-top-down-system-design",
|
||
"content_version": "r1",
|
||
"content_sha256": "9fa4b5861d87e9d221244db001058a619151e8582388cf39eb2165a6dce2361c",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "需求较稳定系统的整体设计与拆解",
|
||
"classification_reason": "规定先整体目标、架构和接口，再拆模块并验证的设计方法。",
|
||
"reason": "预计能在目标较稳定时减少组件偏离整体和集成返工；需求变化大时必须反馈修订。",
|
||
"confidence": "medium",
|
||
"falsifier": "若早期架构固化错误需求、组件接口无法集成，或返工成本高于合理迭代基线，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-bottom-up-system-building",
|
||
"content_version": "r1",
|
||
"content_sha256": "80e8562bebf39d2f10874ad98440e739abab28ee7718311fcb335fe559826665",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "已有组件条件下的增量系统构建",
|
||
"classification_reason": "规定先实现或复用可靠组件，再逐步组合并核对整体目标。",
|
||
"reason": "预计能降低组件级不确定性并较早获得可运行部分；局部成功不保证整体目标满足。",
|
||
"confidence": "medium",
|
||
"falsifier": "若组件无法集成、能力拼接不满足总体需求，或局部优化阻碍整体设计，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-divide-and-conquer",
|
||
"content_version": "r1",
|
||
"content_sha256": "93a23c4213f381f7de16ad98ac7aeeb3aa58d6870a97aefca35fbfe04fc01b26",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "可分解复杂问题的边界与集成",
|
||
"classification_reason": "给出分解、独立求解、接口安排和结果合并的施行方式。",
|
||
"reason": "预计在可分解的困难任务中降低求解负担并隔离验证；前提是边界与合并契约合理。",
|
||
"confidence": "medium",
|
||
"falsifier": "若分解切断关键依赖、子问题解无法组合，或协调成本超过分解收益，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-problem-definition-framework",
|
||
"content_version": "r1",
|
||
"content_sha256": "5f98b23774d0fd73e31f8db76ed410d74f062dde92ab7df18e7670ab1a05c7b2",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-intermediate",
|
||
"task_family": "复杂需求的问题界定与求解闭环",
|
||
"classification_reason": "限定体系节规定把问题、目标、约束和求解路径组织成闭环。",
|
||
"reason": "预计在含糊需求与跨领域任务中减少解错问题和忽略约束；不能替代真实用户验收。",
|
||
"confidence": "medium",
|
||
"falsifier": "若问题界定遗漏利益相关者或硬约束、验收与目标不对应，或闭环只更新表述不更新事实，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-methodology-delivery-loop",
|
||
"content_version": "r1",
|
||
"content_sha256": "6f224bc1df90eee7f2d857e492b3465a1d04801e579b7f8f20e592758c1ca5c6",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-advanced",
|
||
"task_family": "从需求澄清到交付验证的整体作业",
|
||
"classification_reason": "限定六步总体作业流组织澄清、方案、实现和验证，不吞并各个独立方法。",
|
||
"reason": "预计在复杂交付与迁移任务中减少阶段脱节和无验收实现，形成目标与结果的可核对链。",
|
||
"confidence": "medium",
|
||
"falsifier": "若前后阶段的条件和证据不一致、实现未被独立验收，或额外流程成本超过任务预算，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-cybernetic-feedback-method",
|
||
"content_version": "r1",
|
||
"content_sha256": "b6800e058614a22a18c93159c4b5c6a23a65169cdbac638bdb3a489eb1ce65c4",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "反馈控制视角的工程目标与迭代设计",
|
||
"classification_reason": "限定节同时解释信息、正负反馈、稳态及科学检验，并给出AI编程应用准则。",
|
||
"reason": "预计能帮助识别反馈方向与验证作用；源文的稳态和确定性概括不是任意系统的保证。",
|
||
"confidence": "low",
|
||
"falsifier": "若反馈信号不可靠、时滞和增益引发振荡，或稳定性主张无法落实为可检验条件，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-recursive-generator-optimization",
|
||
"content_version": "r1",
|
||
"content_sha256": "df3ed67eccc4c7550ab8f0ba42472f6147300f2ffd569b14695480cee121f990",
|
||
"ontology_type": "dao-content",
|
||
"grade_id": "huang-intermediate",
|
||
"task_family": "生成器自修改机制的建模与试验设计",
|
||
"classification_reason": "完整原文结合自指与不动点形式模型、更新目标和附录启动步骤，保留复合内容。",
|
||
"reason": "预计能提出受控自修改试验的结构假设；连续性和固定点表示本身不足以预期迭代改进或可达收敛。",
|
||
"confidence": "low",
|
||
"falsifier": "若质量指标不可判别、修改形成循环或退化，或无独立验收与可执行回滚，初评不成立。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-specification-before-implementation",
|
||
"content_version": "r1",
|
||
"content_sha256": "16be4868fed3dca76ed76552f2a03824185d7e0b9846b4cc15178a89ade7a18c",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "di-initial",
|
||
"task_family": "实现前的行为与边界约定",
|
||
"classification_reason": "限定三条内容要求先明确输入输出、边界与验收，是行动优先准则而非完整步骤流程。",
|
||
"reason": "预计在接口复杂和需求易错任务中减少先实现再补解释的返工；规约仍须忠实于用户任务。",
|
||
"confidence": "medium",
|
||
"falsifier": "若规约与真实需求不符、关键边界未覆盖，或实现违反已定行为却仍被接受，应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-state-ownership-questions",
|
||
"content_version": "r1",
|
||
"content_sha256": "9dc904132dbd34cd63d4672ab5682fa5e6400d345f4d51dc510aa3852a89f84c",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "可变状态的时间、更新和责任审查",
|
||
"classification_reason": "限定节用状态何时存在、何时更新及谁负责等问题规定设计检查路径。",
|
||
"reason": "预计能减少时序歧义和多头更新的设计缺口；问题清单不能直接证明并发实现正确。",
|
||
"confidence": "medium",
|
||
"falsifier": "若状态拥有者仍不唯一、时间边界无法确定，或设计检查通过但实际更新存在竞态，应下调。"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"id": "gongfa-author-repo-expansion-20261007",
|
||
"vocabulary_id": "gongfa-grade-vocabulary-v1",
|
||
"policy_id": "gongfa-grading-policy",
|
||
"policy_version": "0.1.0",
|
||
"status": "provisional",
|
||
"basis": "expert_judgment",
|
||
"assessor": "AI编者人工初评；非独立评审者",
|
||
"assessed_at": "2026-10-07T09:20:36.251520Z",
|
||
"application_context": "在中文软件工程知识库中选择并应用功法：以各条task_family中的判断、设计、排错或受控交付为目标，先要求正确性、安全、权限和可验收性，再考虑效果、可靠性与成本；所需观测、技能和验证能力需另行满足。任务集、环境、预算与阈值尚未实验冻结。",
|
||
"baseline_assumption": "假定对照采用胜任的现行需求、设计、实现和相关验证流程，在相同目标、信息、环境及预算下比较；不故意选择不测试、忽略安全或能力不足的弱基线。若基线已完整采用同一方法，则不得据初评推定额外增益。此处是前瞻假设，不是已运行基线。",
|
||
"limits": [
|
||
"本批次是基于限定原文的人工内容判断与预计结果带，不是实验分数、实际效果评级或独立语义批准。",
|
||
"类型不决定档位。按0.1.0的黄/玄/地/天方向预估限定任务效果、合理基线增益、困难/迁移表现和能力缺口；未借标题、字数、步骤数、名气、抽象性或形式化程度打分。",
|
||
"初/中/高表示本阶预计结果带而非置信度、次数或等距倍率；区间尚未校准，不能跨任务、跨类型直接排名。",
|
||
"confidence只是作者对前瞻判断的主观把握标签，不是准确率、概率或实测可靠性；所有效果条件都还需要受控验证。",
|
||
"未给天阶建议：本批材料不足以让我预期能在合理基线之外突破已明确的能力缺口；不为填满词表而硬赋高阶。",
|
||
"每条falsifier指出可推翻或下调初评的条件，不是已经执行的实验或失败记录。内容版本变更不自动继承本批意见；正式评级仍另追加assessments。",
|
||
"本批只覆盖本轮新增仓内条目；旧批次和旧58条完全保持，不按最新时间替代旧意见。"
|
||
],
|
||
"supersedes": null,
|
||
"items": [
|
||
{
|
||
"gongfa_id": "gongfa-high-cohesion",
|
||
"content_version": "r1",
|
||
"content_sha256": "043346c2aa5b1ceac845349c246a04079518a5a96457685e8257e99a2dfea70d",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "按相关性与职责组织模块",
|
||
"classification_reason": "限定三条要求规定相关内容靠近、不相关内容隔离及单一职责取舍；没有一次执行的完整流程，主类为准则型。",
|
||
"reason": "预计在模块职责混杂的设计与维护任务中，相对胜任但未系统检查内聚的流程减少跨模块认知负担；原文未给变化边界识别与迁移验证，暂不预期困难任务稳定达标。",
|
||
"confidence": "medium",
|
||
"falsifier": "若在同目标与预算的模块修改任务中，按该准则分组未减少变更扩散或反而拆出更多重复与交接，则下调；基线已有同等内聚不预定增益。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-low-coupling",
|
||
"content_version": "r1",
|
||
"content_sha256": "5f50ecf5fa02490a514fe64352ce2ca2fd8ee669977022d3b1101f42b0be8642",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "模块依赖取舍与独立演进",
|
||
"classification_reason": "正文提出可预测而不束缚、减少依赖的偏好，不是依赖识别或消除算法；主类为准则型。",
|
||
"reason": "预计对不必要依赖造成连锁修改的任务有较强增益；但没有区分必要协作、数据一致性与部署约束，不能据少依赖直接预计所有复杂迁移达标。",
|
||
"confidence": "medium",
|
||
"falsifier": "若减少依赖在成对修改任务中增加了重复事实、同步错误或总维护成本，或基线已消除这些依赖，则本档增益预期不成立。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-stable-interface-contract",
|
||
"content_version": "r1",
|
||
"content_sha256": "5d2157fe1081ddbed9c5dbd380ee318068412a02d8e4ae354102d7f9c9d2b71d",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "接口兼容下的实现替换",
|
||
"classification_reason": "三条内容把 API 指认为契约、实现指认为细节，并要求不破坏契约；核心是兼容取舍准则，不是实现替换步骤。",
|
||
"reason": "预计在已有接口消费者的实现替换中减少外部破坏；原文未说明契约范围、弃用、兼容测试和迁移办法，故保守停在玄阶预计增益。",
|
||
"confidence": "medium",
|
||
"falsifier": "若在固定消费者与验收条件下按该准则仍遗漏可观察行为变化，或维护错误契约的成本超过收益，则应下调；不能用接口名称不变证明兼容。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-explicit-dependencies",
|
||
"content_version": "r1",
|
||
"content_sha256": "81c50c27c950240229ca928a1eaf9fd24dd5ddc18ef64bc42c11017363837b3a",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "依赖关系表达与排查",
|
||
"classification_reason": "限定内容用显式优于隐式、透明优于优雅规定表达取舍，并警示隐式依赖；不是依赖图构造步骤。",
|
||
"reason": "预计对隐藏初始化、调用关系等依赖难以理解的任务有较强帮助，但这些是未来任务族而非原文补出的机制；原文没有依赖枚举和验证协议，不预定迁移场景成功。",
|
||
"confidence": "medium",
|
||
"falsifier": "若固定排查任务中显式表达未降低依赖遗漏或排错成本，或显式化增加无必要暴露和耦合，则应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-invariant-constraints",
|
||
"content_version": "r1",
|
||
"content_sha256": "5c820280d17a1f5cfb2cb303d03afaac36c6546a27d334ab2fde85378a3a976d",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "明确系统不变式与约束",
|
||
"classification_reason": "原文用物理法则类比说明不变式并要求明确约束；没有建模或证明步骤，按主导的约束准则归类。",
|
||
"reason": "预计把隐含一致性要求表达出来可增强设计和检查，但本条没有给出不变式发现、执行或验证方法，不能借既有轻量形式化条目补成地阶方法。",
|
||
"confidence": "medium",
|
||
"falsifier": "若明确约束仍不能发现预定违约，或所选不变式误拒合法状态，则增益预期应下调；仅写出断言不算有效约束。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-reasonable-code",
|
||
"content_version": "r1",
|
||
"content_sha256": "02556cfc972942c7104dd64050fc1c927723fc8cd4beea0a80111a21f4e7cc83",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "代码可理解性与维护取舍",
|
||
"classification_reason": "内容规定可预测性优先及变量少、分支浅、状态明、逻辑平的设计偏好，不提供具体改写流程。",
|
||
"reason": "预计一般维护任务能因较低认知负担获益；原文对性能与可维护性的关系表述很强且缺适用例外，预计结果带保守，不据口号认定跨场景优势。",
|
||
"confidence": "low",
|
||
"falsifier": "若固定维护任务中这些取舍没有减少理解错误，或导致性能合同、正确性或必要状态被破坏，则本建议应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-least-surprise",
|
||
"content_version": "r1",
|
||
"content_sha256": "35c11147118b90685768b6dbd0bb5e437e8680d171a8d5cc45a3a2a8f383d18b",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "代码与接口的使用预期设计",
|
||
"classification_reason": "原文要求代码像常识运作，并偏好不惊讶及可预测性；是使用预期取舍而不是可执行测试规约。",
|
||
"reason": "预计对约定一致的接口与维护任务减少误解，但不同使用者的常识可能不同，原文没有用户预期识别及冲突规则，暂定玄阶中级。",
|
||
"confidence": "low",
|
||
"falsifier": "若独立使用者在同目标任务中没有减少误用，或所谓常识只符合作者习惯而伤害目标用户，则本建议应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-validated-abstraction",
|
||
"content_version": "r1",
|
||
"content_sha256": "9c35a2abb7c73d0d6ef2a6a0ad58897246a69aab7a8c44331a34b2a1be6863ee",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "重复与抽象成本的设计取舍",
|
||
"classification_reason": "限定节规定抽象有代价，优先真实使用验证而不是过早抽象，允许适度重复；不是抽象提炼算法。",
|
||
"reason": "预计对尚未稳定的相似需求减少错误共性及后续变更成本；原文不提供验证周期或分拆标准，不能预期所有困难复用场景稳定达标。",
|
||
"confidence": "medium",
|
||
"falsifier": "若在同信息和预算的需求变更任务中适度重复反而增加不一致，且延后抽象没有减少维护成本，则下调；不能把一切复用都当过早抽象。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-technical-debt-ledger",
|
||
"content_version": "r1",
|
||
"content_sha256": "53099acdfb332ed247425ff5707c1b29130918accb29fdeb2fd1342545224d44",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "交付折衷与偿还责任管理",
|
||
"classification_reason": "原文允许现实折衷，但要求技术债可见、记录、评估并明确利息和偿还时点；核心是责任准则而非债务计算流程。",
|
||
"reason": "预计对不得不折衷的交付任务减少隐性遗留与无期限承诺；原文没有成本量化和执行偿还机制，登记本身不证明维护收益已经实现。",
|
||
"confidence": "medium",
|
||
"falsifier": "若受控交付中债务记录未改变遗漏、偿还或后续缺陷，或者记录维护成本超过回收收益，则应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-performance-by-measurement",
|
||
"content_version": "r1",
|
||
"content_sha256": "77c5408834e12ba77ca1cd8a92f0f254cb9a5481d5908d50055edd75de2770fd",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "性能瓶颈定位与优化取舍",
|
||
"classification_reason": "正文要求依靠监控、profiling、压测和真实数据而非感觉，给出优化次序偏好但无完整测量协议；主类为准则型。",
|
||
"reason": "预计能减少猜测驱动的无效优化；原文没有负载、缓存状态、尾延迟及复测合同，不能借工具名预计跨环境测量可靠。源文的次序表述不替代本项目正确性优先契约。",
|
||
"confidence": "medium",
|
||
"falsifier": "若同负载与预算下按测量选出的优化未改善目标，或测量负载不代表真实任务、改善以正确性损失为代价，则下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-controllable-failure",
|
||
"content_version": "r1",
|
||
"content_sha256": "c84538242086e542ab36b740bbf33388e676b0ed1bbd17c06848b023eec722d6",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "di-initial",
|
||
"task_family": "有故障条件下的可观测、隔离与恢复设计",
|
||
"classification_reason": "原文以故障可被发现、定位、隔离、恢复作为稳定目标，并列出支持能力；不是一次事故处置的执行顺序。",
|
||
"reason": "预计不仅帮助正常任务，也能在预定义故障注入与恢复任务中保留关键处置能力；没有给恢复期限、数据完整性及验收阈值，因此只作地阶初级前瞻建议。",
|
||
"confidence": "medium",
|
||
"falsifier": "若代表性故障任务中无法发现、隔离或恢复，或恢复破坏数据完整性并违反事前期限，则地阶预期不成立；工具已部署不等于这些能力已取得。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-decision-rationale-documentation",
|
||
"content_version": "r1",
|
||
"content_sha256": "fb91b1b518e5156f658a2ef9dfd3a6e09a98a69606c7b48f98d056dd7e085306",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "设计决策解释与维护交接",
|
||
"classification_reason": "原文规定文档让别人少猜，并优先记录为什么这样做；没有给文档维护或决策评审流程。",
|
||
"reason": "预计在后续维护中减少决策背景猜测，但没有说明过期、责任与复核机制，理由记录的准确性尚待独立消费检验，暂定玄阶中级。",
|
||
"confidence": "medium",
|
||
"falsifier": "若独立维护者无法凭理由文档理解约束，或陈旧理由误导修改、增加返工，则应下调；文档字数不算收益。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-dataset-first-architecture",
|
||
"content_version": "r1",
|
||
"content_sha256": "45bb5f2e9244ad307d77b85aea7a182e2c153fb56c6fbba253e025c8abe61282",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "di-initial",
|
||
"task_family": "稳定数据集产品的服务边界与控制面设计",
|
||
"classification_reason": "只取目录模板之前的判断规则、适用/不适用情境和五项组织原则；主导功能是选择 dataset 边界、契约与控制面，不把目录图或后续施工流程借入。",
|
||
"reason": "预计在补数、巡检、血缘和质量治理等困难数据任务中，显式数据边界与契约比按脚本隐藏数据对象更容易保持一致性；仅适用于稳定数据集产品，尚无实测迁移或成本证据。",
|
||
"confidence": "medium",
|
||
"falsifier": "若预定义补数与修复任务中仍出现数据对象遗漏、契约冲突，或共享控制面使独立数据集难以维护，则地阶预期应下调；纯网关等不适用场景不得用于抬高该级。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-dataset-service-construction",
|
||
"content_version": "r1",
|
||
"content_sha256": "baf1bafb0ba2902b325e23c3b23b87152af259ade01b0d4ec07ea9e9169646cc",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "di-initial",
|
||
"task_family": "按数据契约构建采集、补数与修复服务",
|
||
"classification_reason": "限定节给出 dataset 清单、contract、控制面、逐数据集实现及文档/验证的五步先后安排，明确规定怎样施工。",
|
||
"reason": "预计对含多个数据集与补数/修复路径的构建任务减少漏项，并支持比单脚本更困难的服务交付；原文没有提供独立测试合同和完成阈值，地阶初级仅是预计。",
|
||
"confidence": "medium",
|
||
"falsifier": "若固定多数据集构建任务仍遗漏契约、补数或修复能力，或在同预算下控制面成本超过收益，则地阶预期应下调；走完五步不证明输出正确。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-external-source-dataset-adaptation",
|
||
"content_version": "r1",
|
||
"content_sha256": "235adcd7c283cc1ab19d2f54f49ad677c2a7d9edf3a43d26dadc792c44cf5620",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "外部采集源码向数据集服务结构的适配",
|
||
"classification_reason": "限定节明确先盘点数据对象、反向映射脚本、抽出公共能力、隔离 legacy 的四步流程；不是执行克隆或认证的授权。",
|
||
"reason": "预计比先搬脚本的适配减少边界混乱与重复公共逻辑；原文没有验收、权限、许可及迁移回滚协议，暂不预计复杂源接入一定成功。",
|
||
"confidence": "medium",
|
||
"falsifier": "若同源与预算的适配任务中数据对象仍遗漏、公共能力失真或新逻辑继续回流 legacy，则下调；未获授权的源不能作为有效迁移样本。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-symbol-name-index-maintenance",
|
||
"content_version": "r1",
|
||
"content_sha256": "d29e54373844c7f3ae0f9e1f93dd7f08869228cdafc85bbc04f08119af0e1963",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "huang-advanced",
|
||
"task_family": "团队与 AI 的变量命名登记和检索",
|
||
"classification_reason": "该父节包含建立统一索引文件、记录变量名/说明/位置/频率的示例和用途，是明确的维护方法，不只是命名术语。",
|
||
"reason": "预计在限定的小型命名维护任务中可达标并形成可检索结果；独立索引的同步成本可能抵消相对现有 IDE/检索基线的收益，故不预定玄阶增益。",
|
||
"confidence": "low",
|
||
"falsifier": "若索引不能正确反映变量位置与语义，或人工同步成本超过直接检索收益，则应下调；出现频率高不表示变量更重要。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-input-state-transform-separation",
|
||
"content_version": "r1",
|
||
"content_sha256": "5ecabc148817ed14ae49cafac8bfda07428578a9c95dd4262971b8bb3ee9a7cb",
|
||
"ontology_type": "method-specification",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "系统输入、状态、变换与输出的职责划分",
|
||
"classification_reason": "原文定义四种环节并要求明确划分、独立管理输入处理输出，说明按哪些角色拆分系统行为；不是四个名称的孤立清单。",
|
||
"reason": "预计对数据流职责混杂的任务有增益，但未规定状态所有权、事务或时序的一般处理，不能借既有状态时间追踪条目补成完整方法。",
|
||
"confidence": "medium",
|
||
"falsifier": "若划分后仍无法识别实际输入/输出和状态变换，或边界拆分破坏事务一致性且未降低认知负担，则本建议应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-shared-resource-concurrency",
|
||
"content_version": "r1",
|
||
"content_sha256": "80b42a60c078ff6669a80f0f3c8ea724cb6c769eba351e8cc9089251766ab30a",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "并发共享状态设计与检查",
|
||
"classification_reason": "限定节要求区分共享资源、避免竞争、必要时同步并区分并发与异步，是安全取舍约束而不是线程调度算法。",
|
||
"reason": "预计能减少把异步等同并行和忽略共享资源的设计错误；没有资源生命周期、锁顺序及恢复规则，不预定困难并发任务均能达标。",
|
||
"confidence": "medium",
|
||
"falsifier": "若受控并发任务中仍发生竞争、死锁或资源泄漏，或不必要同步使总成本增加且无正确性收益，则应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-dry-reuse",
|
||
"content_version": "r1",
|
||
"content_sha256": "11cd67ed2c3cb02be26a68472a22711530f4a9900aa6dfb84024df080fb2bf1f",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-intermediate",
|
||
"task_family": "重复逻辑与公共表达的取舍",
|
||
"classification_reason": "正文要求用函数、类、模块复用而不复制粘贴，是复用偏好；没有判定何时同一知识或怎样提炼的流程。",
|
||
"reason": "预计对已知真实共性的重复逻辑减少多点修改，但原文没有重复与偶然相似的区分及过度抽象例外，不能借另条抽象准则替本条补齐边界。",
|
||
"confidence": "medium",
|
||
"falsifier": "若抽出的共用逻辑把独立变化强绑在一起，造成更多条件分支或修改冲突，则应下调；字符相似不算复用收益。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-input-scale-complexity-constraints",
|
||
"content_version": "r1",
|
||
"content_sha256": "908cc7665d599efa46196df220fdf07a9b68a2d73bc400739c02c7dfb56458e7",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "xuan-advanced",
|
||
"task_family": "算法选型与隐藏遍历成本检查",
|
||
"classification_reason": "原文是关于输入规模、最坏情况、容器操作及 helper 成本的一组禁止性约束，不是复杂度计算算法。",
|
||
"reason": "预计对忽略嵌套调用、复制与容器成本的性能判断有较强增益；规则不提供代表性负载与实测归因，不能由大 O 或循环层数断言实际达标。",
|
||
"confidence": "medium",
|
||
"falsifier": "若同输入规模与预算的任务中这些约束未减少算法误选，或复杂度审查忽略真实资源瓶颈并增加无效成本，则应下调。"
|
||
},
|
||
{
|
||
"gongfa_id": "gongfa-cache-lifecycle-isolation",
|
||
"content_version": "r1",
|
||
"content_sha256": "3ca37523c3c65d4ed5db914528885cb9224ede6067c91e3a6e857585ebf28005",
|
||
"ontology_type": "normative-allocation-content",
|
||
"grade_id": "di-initial",
|
||
"task_family": "有状态与多用户场景的缓存正确性设计",
|
||
"classification_reason": "原文对缓存对象、状态版本、容量、用户隔离、失效、观测及进程/分布式语义作禁止性规定，不是缓存实现或通用算法。",
|
||
"reason": "预计在状态更新、多用户及多进程等困难任务中防止错值、泄漏与一致性误判；未提供失效实现、测试矩阵和阈值，地阶初级只针对这些任务族作预计。",
|
||
"confidence": "medium",
|
||
"falsifier": "若预定义更新、隔离及多进程任务仍产生陈旧错值或跨用户泄漏，或内存和一致性成本高于省下的计算，则地阶预期不成立。"
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|