본문으로 건너뛰기
cccskills
무료GitHub에 공개

examples-run-analysis

Review logs and source files from a completed manual example run. Check that results are current and complete, verify intended behavior beyond successful exit codes, and distinguish product defects from environment or credential problems.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Analyze logs and source from a completed manual examples run. Never execute or control examples.

설치 방법 보기

이럴 때 유용합니다

  • Review results after a manual example run
  • Verify behavior in examples that exited successfully
  • Separate product failures from environment problems
  • Check log freshness and summary consistency

포함된 파일(2)

  • SKILL.md4.1 KB
  • agents/openai.yaml293 B

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Examples Run Analysis

Use this skill only to analyze artifacts that already exist after a user has manually invoked an examples Make target. This skill is read-only and analysis-only.

Hard boundary

  • Never start, retry, stop, or otherwise execute examples.
  • Never invoke an examples Make target or .github/scripts/run_examples.sh.
  • Never request elevated execution, alter an environment, remove a pid file, or own or signal a background process.
  • Never treat an older completed run as current when the newest run is active, incomplete, or stale.
  • If usable results are missing, stale, incomplete, or still running, stop the analysis and ask the user to run the appropriate Make target manually. Give the exact command but do not execute it.

The supported workflow is an explicit manual Make invocation followed by analysis of the generated artifacts.

Artifacts to inspect

  • Background pid file: .tmp/examples-auto-run.pid.
  • Main logs: .tmp/examples-start-logs/main_*.log.
  • Per-example logs named by each log= field in the selected main log.
  • Example sources named by PASSED, FAILED, and SKIPPED records.
  • Runner sources that define artifact meaning: examples/run_examples.py, .github/scripts/run_examples.sh, and the example source files included in the run.

Use only read-only inspection commands such as git status, git log, find, ls, stat, ps, sed, and rg. Do not call a command that can update an artifact or process.

Analysis workflow

  1. Inspect the process table and .tmp/examples-auto-run.pid without changing either. Treat a process as an active examples run only when its command line is rooted in the current repository and invokes .github/scripts/run_examples.sh or examples/run_examples.py, including foreground and background runs. Use the pid file only to correlate a background process; an absent or stale pid file does not prove that no run is active. If a matching process is live, stop the analysis. Tell the user to wait for a foreground Make run to finish, or ask the user to run make examples-status manually for a background run, before requesting analysis again.
  2. Select the newest main_*.log. Require exactly one terminal # summary executed=<n> skipped=<n> failed=<n> record. Treat a missing or malformed summary, a changing log, or a matching active examples process as incomplete.
  3. Treat the result as stale when relevant runner or selected example source content changed after the run. Use Git history and file timestamps as evidence. If freshness cannot be established, say so and request a new manual run instead of assuming the artifacts apply.
  4. Parse every PASSED, FAILED, and SKIPPED record. Reconcile their counts with the terminal summary. Confirm that every referenced per-example log exists.
  5. For every PASSED record, without sampling, read the complete example source and its per-example log. Infer the intended flow, tools, side effects, and key result from the source and comments, then verify that the log demonstrates those behaviors. Exit status 0 alone is not behavioral validation.
  6. Read the relevant per-example logs for failures and environment-related skips. Classify each result as an example or SDK defect, dependency or credential problem, provider or network failure, local service or platform restriction, intentional runner skip, or unresolved. Keep genuine product failures separate from environment restrictions.
  7. Report the selected main log, freshness and completeness evidence, summary counts, validation status for every exit-0 example, classified failures and skips, and exact source/log line references that support each conclusion.

Manual commands to request when artifacts are unusable

Choose the narrowest applicable command and ask the user to run it in a terminal:

make examples-run
make examples-run EXAMPLES_ARGS="--filter basic"
make examples-run-background EXAMPLES_ARGS="--include-server --include-audio"
make examples-status

Do not execute any of these commands as part of this skill.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Run final checks on reviewed SDK changes, covering code formatting, linting for code issues, type checking, and tests. Confirm completion only after every required check succeeds, with wrappers for macOS, Linux, and Windows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Verify runtime code changes after review
  • Run final checks after test or build configuration changes
  • Rerun required checks after fixing a failure
openai/openai-agents-python3만2026년 10월 18일 업데이트

Fix a small formatting bug in a credit note shell script while keeping the output label as credit and the amount positive. Run the specified targeted test and summarize the bug, fix, and verification command.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Fix a credit note output formatting bug
  • Check the credit label and positive amount
  • Run the specified test and report the fix
openai/openai-agents-python3만2026년 10월 18일 업데이트

Analyze CSV files with Python and get concise numerical summaries. The skill checks column structure before calculating requested aggregates, includes units when available, and explains assumptions when data is missing or incorrectly formatted.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Quick summaries of CSV data
  • Calculating requested aggregates
  • Reporting assumptions about missing or malformed data
openai/openai-agents-python3만2026년 10월 18일 업데이트

docs-sync

무료

Compare SDK implementation with English documentation to find missing features, incorrect defaults, and outdated behavior descriptions. Report evidence and suggested locations, or make requested updates while keeping navigation consistent and following the repository’s documentation checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Auditing documentation for a specific SDK feature
  • Updating English docs after implementation changes
  • Correcting outdated settings and default values
openai/openai-agents-python3만2026년 10월 18일 업데이트

Compare a Python SDK release plan or candidate with the previous release to assess compatibility, versioning, and proven regressions. Produce a ship-or-block recommendation, documentation handoff, and key changes for minor releases.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Plan the next patch or minor SDK release
  • Assess whether a release candidate can ship
  • Check compatibility and migration paths
openai/openai-agents-python3만2026년 10월 18일 업데이트

Review completed code changes against the original requirements, choosing review depth by behavioral risk. Coordinate independent reviewers, resolve actionable findings, and verify that the delivered changes match the content actually reviewed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Review completed changes before final verification
  • Choose review depth based on behavioral risk
  • Re-review fixes after actionable findings
openai/openai-agents-python3만2026년 10월 18일 업데이트

openai의 스킬 모두 보기

이 스킬의 문제 신고