Three complete, runnable scripts that chain skills from Algo-Trading-Skills into one workflow each. They import the real helper modules out of skills/<slug>/scripts/ — the walkthroughs are wiring and narration, and the behaviour on show is the skills' own, not a simplified restatement of it.

Included walkthroughs

Walkthrough	Chained skills	Purpose
`01_resilient_order_execution_and_circuit_breaker.py`	`token-lifecycle-live-probing` + `order-placement-idempotency` + `kill-switch-and-drawdown-circuit-breakers`	Simulates a live order loop through a lost broker response: the token is probed before anything is sent, the timed-out order is reconciled against the broker's book instead of re-sent, and a drawdown breach latches the circuit breaker, which then vetoes the next order.
`02_lookahead_free_backtest_with_slippage.py`	`lookahead-bias-elimination` + `execution-realistic-simulation` + `backtest-reporting-standardized-tearsheet`	Builds a point-in-time backtest whose fills land on the next bar's open, calibrates the lookahead auditor before trusting its clean verdict, prices each fill by crossing the half-spread and paying square-root market impact plus the statutory fee stack, and reports one standardized tearsheet.
`03_cross_strategy_risk_parity_allocation.py`	`cross-strategy-correlation-monitoring` + `risk-parity-allocation-across-strategies` + `strategy-lifecycle-retirement-criteria`	Monitors pairwise strategy correlations and the diversification ratio, feeds that same covariance into an equal-risk-contribution allocation, adjudicates each strategy against a pre-declared retirement rule, and reallocates what survives.

Running the examples

Standard library plus numpy and pandas (see requirements-dev.txt), on Python 3.10+. Run them from the repository root — each script derives the repo root from its own __file__ and puts the relevant skills/*/scripts directories on sys.path:

python examples/01_resilient_order_execution_and_circuit_breaker.py
python examples/02_lookahead_free_backtest_with_slippage.py
python examples/03_cross_strategy_risk_parity_allocation.py

Each finishes in seconds, exits 0, and leaves nothing behind: example 01 needs a durable SQLite ledger, so it writes one into a temporary directory that is removed on the way out.

All randomness is seeded (random.seed(42) / numpy.random.default_rng(42)), so the printed numbers are the same on every run. Those numbers describe the machinery on synthetic data; none of them is a claim about a strategy.