openai-agents-python: Skill for Codex

.agents/skills/maintainer-review/SKILL.md

maintainer-review is a skill for Codex from openai/openai-agents-python. It costs 29 tokens per session (6,566 once invoked), scanned A, original, MIT.

A maintainer’s review guide for deciding how to handle a GitHub issue or pull request. It separates whether a problem is real from whether the proposed change is suitable for the project.

In plain words
What is it for?
Use it to assess reported behavior, compare competing approaches, check code and repository readiness, choose an action, and draft a response for the contributor.
Why use it?
It helps maintainers avoid treating every suggested solution as necessary and makes decisions based on user impact, existing alternatives, design fit, and readiness.

Skill for Codex ✓ vendor

Written for Codex: agents/openai.yaml present. Also seen: reads ~/.codex paths; installed under .agents/ (shared by several agents); mentions AGENTS.md.

This is openai/openai-agents-python's own configuration. It tells Codex how to work on openai-agents-python itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything openai-agents-python configures →

About the project

OpenAI Agents SDK is a Python framework for building workflows in which multiple AI agents use tools, delegate tasks, follow safety checks, and involve people when needed. Developers use it to create and monitor agent, sandbox, realtime, and voice applications.

openai/openai-agents-python · 30,339 stars · on GitHub · openai.github.io

Reuse

Borrowing it

Nothing to install: this file belongs to openai/openai-agents-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/openai/openai-agents-python/main/.agents/skills/maintainer-review/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/openai/openai-agents-python

Made for: Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for maintainer-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/openai/openai-agents-python/maintainer-review/github.svg)](https://agentmods.dev/skills/openai/openai-agents-python/maintainer-review)
Your own site
<a href="https://agentmods.dev/skills/openai/openai-agents-python/maintainer-review"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/maintainer-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for maintainer-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/openai/openai-agents-python/maintainer-review"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/maintainer-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 6,566 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Snyk warn 7 Sept 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00029 $0.06566
Opus 5.5 $0.00012 $0.02626
Sonnet 5.5 $0.00006 $0.01313
Haiku 4.5 $0.00003 $0.00657

Measured 1mo ago against content hash 9fd4ecc9f9ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 1mo 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.

.agents/skills/maintainer-review/SKILL.md · 261 lines

How it starts

The opening of the file, as written. The whole thing — 261 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:

  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, and what concrete direction should the contributor implement?
  12. 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 to does 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.

Read the full file on GitHub · 261 lines

Files

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.

Changes

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.

  1. 1mo ago Changed · +10 lines · -40 tokens per session 9fd4ecc9f9ab
  2. 1mo ago First seen · 251 lines · 69 tokens per session scan A 66b6bc4428b4

Subscribe to this mod's changes

maintainer-review is a skill published in the GitHub repository openai/openai-agents-python (30,339 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 6,566 once invoked, about $0.0001 per session on Opus 5.5. 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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