model-routing-golden
Maintain model-routing audit golden fixtures and expected outputs.
- License 许可
- MIT
- 所属仓库
- github/gh-aw
- 最近更新
- 2026-11-11
- Stars
- ★ 5.6k
- Forks
- 626
01 前言 Front Matter
- name
- model-routing-golden
- description
- Maintain model-routing audit golden fixtures and expected outputs.
03 SKILL.md 正文
Model-Routing Golden Fixtures
Use this skill when a model-routing golden test fails, analysis output changes intentionally, or a new routed workflow run needs coverage.
Verify
Run make verify-model-routing-golden (or
python3 scripts/model-routing-golden.py --verify) before changing fixtures.
Treat a mismatch as a behavior change to investigate, not a reason to regenerate
immediately.
Refresh expected outputs
When analysis behavior intentionally changes, run
make update-model-routing-golden. Review the complete diff for each
expected*.json file and confirm that routing status, classifier attribution,
AWF credits, token totals, warnings, and subagent costs match the intended
behavior. Keep separate legacy goldens when missing artifacts cause documented
information loss. Do not regenerate outputs merely to silence an unexplained
failure.
Add or replace a fixture
Capture a run with:
python3 scripts/model-routing-golden.py --run <id-or-url> --case <case-name>
Use --replace only when intentionally updating an existing source run. The
tool downloads through the local gh aw logs artifact path, minimizes and
redacts its files, and refuses to write if analysis on the fixture differs from
analysis on the full download. Use --source-dir to exercise capture against
the synthetic downloaded-run fixture without GitHub access.
Add the case and its source scenario to modelRoutingGoldenCases and the
fixture README. Then run make update-model-routing-golden, inspect all
generated files, and run make verify-model-routing-golden. Retain the
established file layout and logs/ ignore exception so raw proxy-log fixtures
remain tracked.
04 同仓库更多 Skills
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tiered-memory
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test-discipline
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squad
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