CI Gating

Turn environment evidence into an explicit release decision. CI should parse stable result data, print the reason for a decision, and leave the presentation grid available for humans.

Files

• basic.py — gate on aggregate pass rate and unscored attempts from summary().
• per_task_floor.py — require every task to meet an individual reliability floor.
• baseline_regression.py — reject task-level drops beyond a configured tolerance.

When to use

Use CI gates after local calibration has produced meaningful task rows. An aggregate gate is compact but can hide one weak task; a per-task floor protects critical cases; a baseline diff catches regressions without requiring perfection. The baseline example compares gpt-5.5 high reasoning with a low-reasoning candidate through a policy-only model override.

The dataset workflow in _27_verified_dataset/ uses the same pass-rate evidence for curation. Saved results and diffs are introduced in _13_saved_baselines/ and _14_environment_diff/.

Run

python cookbook/environments/_28_ci_gating/basic.py
python cookbook/environments/_28_ci_gating/per_task_floor.py
python cookbook/environments/_28_ci_gating/baseline_regression.py

# Production enforcement examples: FAIL exits with status 1.
python cookbook/environments/_28_ci_gating/basic.py --enforce
python cookbook/environments/_28_ci_gating/per_task_floor.py --enforce --minimum-task-rate 1.0
python cookbook/environments/_28_ci_gating/baseline_regression.py --enforce --maximum-drop 0.0

Requires OPENAI_API_KEY. The normal teaching commands exit successfully so their live runs can be inspected. Every file accepts --enforce, which maps a FAIL decision to exit status 1 for production CI. The configurable thresholds are --minimum-pass-rate, --minimum-task-rate, and --maximum-drop.