Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/openai/openai-agents-python/maintainer-reviewnpx skills add openai/openai-agents-python --skill maintainer-reviewgit clone --depth 1 https://github.com/openai/openai-agents-pythonWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00069 | $0.06208 |
| Opus 5 | $0.00034 | $0.03104 |
| Sonnet 5 | $0.00014 | $0.01242 |
| Haiku 4.5 | $0.00007 | $0.00621 |
Grade A, and why
maintainer-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Maintainer Review
Objective
Make a maintainer decision, not a generic code-review summary. Separate these questions:
- Is the claimed behavior real?
- What user outcome or constraint exists independently of the reporter's proposed API or fix?
- Can supported functionality already achieve that outcome with reasonable composition or configuration?
- If a gap remains, is the proposed solution the best design and implementation layer?
- Can normal users plausibly reach the gap, and what happens when they do?
- Is it important enough to act on now?
- If this PR did not already exist, would maintainers choose to open and implement the same work?
- For a PR, is this solution worth merging and maintaining?
- Can overlapping or stale operations corrupt shared state or clean up resources owned by surviving work?
- If competing PRs exist, which single implementation path should maintainers pursue?
- Which ambiguous scope or semantic choices are maintainer-owned product/API decisions, and what concrete direction should the contributor implement?
- What concise maintainer message should communicate a closure or change request clearly and politely?
Treat an issue's requested field, callback, flag, class, or implementation strategy as a proposed mechanism, not as the accepted requirement. Do not begin by asking how to implement it. First prove that a concrete user outcome is not already supported and that the proposed mechanism is better than the available alternatives.
Lead with the current review state. Use Preliminary assessment while decision-relevant evidence is pending, and Maintainer decision only when the review can be concluded. Use the diff, issue narrative, or contributor effort as evidence, not as a proxy for impact.
Workflow
1. Establish the exact target
- Accept a GitHub issue or PR URL as the primary input. Resolve its owner, repository, item type, and number before reviewing it.
- For an issue, read the full report, comments, reproduction, environment, linked material, and maintainer responses.
- For a PR, inspect the current remote base and head, full patch, commit history when relevant, tests, linked issue, and review discussion. Do not substitute the current local checkout for the remote change under review.
- State the claim in one falsifiable sentence. Distinguish the reported symptom from the reporter's proposed cause or fix.
- Identify the released behavior boundary when compatibility or regression claims matter.
- When a proposed change removes, reorders, or reinterprets an established observable or an explicit existing test expectation, inspect the introducing commit, blame, and original tests before any positive assessment. Intentional released coverage is compatibility-risk evidence even when it is not by itself a permanent public contract.
- Verify whether linked evidence matches the PR's exact runtime variant, provider or tool type, triggering condition, and user outcome. A generic issue title, conceptual similarity, or wording such as
Related todoes not transfer evidence of need to an adjacent extension. If the reported scenario has already been fixed, treat additional variants as new needs requiring their own evidence.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 251 lines · 69 tokens per session scan A 66b6bc4428b4
maintainer-review is a skill published in the GitHub repository openai/openai-agents-python (29,123 stars, last pushed yesterday), licensed MIT. It adds 69 tokens to every session and 6,208 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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