Maintain reference test data and expected results for model-routing audits, which analyze how AI models are selected. Investigate mismatches, update outputs after intentional behavior changes, and add captured workflow runs as new test cases.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Maintain model-routing audit golden fixtures and expected outputs.
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill when a model-routing golden test fails, analysis output changes intentionally, or a new routed workflow run needs coverage.
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.
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.
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.
아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Coordinate work across AI agents with shared rules for locating team files, recording decisions and their reasons, and requesting help. It also defines who owns revisions after a reviewer rejects an artifact.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Set shared development rules for an agent team: keep specific agent names out of product code, tests, and CI configuration, verify affected tests before finishing, and record lessons learned from mistakes.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Routes requests to design, create, update, debug, or upgrade GitHub Agentic Workflows to the appropriate instructions. It helps the agent select a workflow-specific procedure and apply any additional rules defined for the repository.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Prepare architecture proposals before implementation so the team can agree on a direction. Organize evidence, technical choices, effects on existing work, risks, and the first release’s scope into a document with clear recommendations and decision owners.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Update the gh-aw-firewall version used by gh-aw, rebuild generated files, and review release notes and configuration schemas, which define valid settings. It guides related code and test updates while identifying follow-up work that remains.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review git and gh operations in gh-aw workflows to check credential availability for each repository. Identify missing token handoffs, risky expansions of limited checkouts, and error handling needed where credentials are intentionally unavailable.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.