maintainer-review Skill
Review a GitHub issue or pull request URL as an openai-agents-python maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort. Use when assessing Published by openai in openai-agents-python.
What is maintainer-review Skill?
Review a GitHub issue or pull request URL as an openai-agents-python maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort. Use when assessing Published by openai in openai-agents-python. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Design and media
- Design and media use cases
Technical details
- Install or run with Copy skill directory
When to use maintainer-review Skill
- Use it for design and media.
Built with
Editorial notes
Source
- Creator: openai
- Repository: openai/openai-agents-python
- Skill file: .agents/skills/maintainer-review/SKILL.md
What it does
Review a GitHub issue or pull request URL as an openai-agents-python maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported functionality, correctly scoped, better served by another design, and worth maintainer and contributor effort. Use when assessing
Skill instructions
Maintainer Review Objective Make a maintainer decision, not a generic code-review summary. Separate these questions: 1. Is the claimed behavior real? 2. What user outcome or constraint exists independently of the reporter's proposed API or fix? 3. Can supported functionality already achieve that outcome with reasonable composition or configuration? 4. If a gap remains, is the proposed solution the best design and implementation layer? 5. Can normal users plausibly reach the gap, and what happens when they do? 6. Is it important enough to act on now? 7. If this PR did not already exist, would maintainers choose to open and implement the same work? 8. For a PR, is this solution worth merging and maintaining? 9. Can overlapping or stale operations corrupt shared state or clean up resources owned by surviving work? 10. If competing PRs exist, which single implementation path should maintainers pursue? 11. Which ambiguous scope or semantic choices are maintainer-owned product/API decisions,
Explore related resources
Frequently asked questions
What is maintainer-review?
maintainer-review is a open-source AI agent skill with Copy skill directory. Review a GitHub issue or pull request URL as an openai-agents-python maintainer, with a staged assessment of whether the claim is real, practically important, already solvable with supported.
Who is maintainer-review best for?
maintainer-review is best for reusing agent instructions, scripts, and references, design and media workflows.
How do I install maintainer-review?
Install or run maintainer-review using Copy skill directory. Check maintainer-review for the latest setup command.
Is maintainer-review actively maintained?
maintainer-review may need a closer maintenance check before production use.
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