Official agent skill

Implementation Final Review

by openai in openai/openai-agents-python

Review completed implementation changes before final verification.

OfficialMITAuto-check passedDevelopment

Install Implementation Final Review

skills CLI
$ npx skills add openai/openai-agents-python --skill implementation-final-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openai/openai-agents-python implementation-final-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/implementation-final-review .claude/skills/implementation-final-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
implementation-final-review
GitHub stars
30k
Token cost
~2k tokens
SKILL.md length
999 words
Files
8 (incl. scripts, references)
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Review completed implementation changes before final verification.

  • Repository policy requires independent review
  • SKILL.md covers Choose the review tier, Ordinary independent review and High-risk resources
  • Runs Python scripts from its folder
  • The user explicitly requests it

What it does

Implementation Final Review is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Review completed implementation changes before final verification. Use when repository policy requires independent review or the user explicitly requests it.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/high-risk-review.md` and `references/reviewer-brief.md`).

It sits in Development. It works with OpenAI. The repository describes itself as: A lightweight, powerful framework for multi-agent workflows. The licence is MIT.

When your agent uses it

  • Repository policy requires independent review
  • The user explicitly requests it

Example prompts

  • “/implementation-final-review”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 71c2da4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 4 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Implementation Final Review loads about 2k tokens when it runs, and up to ~21k if it reads all its reference files. Until then it costs about 46 tokens; SKILL.md has 999 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~46
When it runs · the whole SKILL.md, loaded when a task matches
~2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~21k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from openai/openai-agents-python at commit 71c2da4, republished under its MIT licence (© openai). 999 words, ~1,972 tokens.

Download SKILL.mdSave it as .claude/skills/implementation-final-review/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
implementation-final-review
description
Review completed implementation changes before final verification. Use when repository policy requires independent review or the user explicitly requests it.

Implementation Final Review

Review the original requirement and complete task diff, including committed, staged, unstaged, and task-owned untracked files. Use the intended target's merge base for patch ownership and the latest release separately when released compatibility matters. Apply the finding threshold and supported-scope rules in AGENTS.md.

Choose the review tier

Classify the complete change by its semantic impact, not its line count, extension, or location. Record the tier and a short reason in existing working notes; do not create a separate classification report.

TierBoundaryRequired review
LightweightOnly spelling, comments, or formatting; no change to execution, public contracts, test expectations, configuration, or documented meaning.Self-check the complete diff and run applicable focused checks. Independent review is optional.
OrdinaryLocal behavior changes within an established contract, ordinary test additions, or behavioral documentation without a high-risk boundary change.One independent reviewer in a fresh context. Use the procedure below.
High riskChanges to security, credentials, sensitive-data handling, trust, persistence/resume, durable state, concurrency/cancellation/shared lifecycle ownership, released compatibility, package/runtime exports, protocol ownership, or cross-provider lifecycle; also any review cycle with a validated P0/P1.Two independent reviewers with complementary specialties. Read high-risk-review.md, which owns the strict protocol and final-evidence rules.

A one-line condition fix is at least ordinary. Public annotations and schema-generating types can change contracts. Test deletion or changed expectations, CI/build configuration, and policy changes are not lightweight merely because they do not edit runtime files. An uncertainty about high-risk impact must be resolved before accepting an ordinary clean review; escalate when the affected boundary requires it. Do not downgrade a cycle after a validated P0/P1 just because the immediate fix is small.

Planning, investigation, and report-only tasks do not start this implementation workflow. Repo-meta work uses the applicable skill's focused validation. When implementing changes to decision-making guidance, perform realistic scenario checks and an independent pass rather than an SDK runtime review packet. Report-only assessments may inspect existing review/scenario evidence; they do not automatically commission another implementation review. Editorial documentation follows the repository's documentation verification tiers.

Ordinary independent review

Prepare once

Finish affected tests and any formatting or safe hook-equivalent normalization that can change the task diff. Reuse successful focused checks for unchanged content. Do not start broad final verification until review is clean.

Give one reviewer the original request, a short scope contract, target/base/head identifiers, task-owned paths, complete diff including new-file contents, relevant architecture references, and exact focused-check commands and results. Record the reviewed content in a saved diff plus new-file snapshots or a content fingerprint, so later comparison can establish what was reviewed. Keep temporary evidence outside shipped deliverables. scripts/review_state.py is available when useful, but the strict JSON packet, component ledger, evidence IDs, and verification receipts are not required for ordinary review.

Show full SKILL.md (548 more words)Show less
Review and resolve

Launch one independent agent with no inherited implementer conversation (fork_turns: "none" when supported). Do not supply intended findings, previous reviewer conclusions, or proposed fixes. The reviewer inspects the complete diff and relevant surrounding source, verifies the scope contract rather than rerunning implementation strategy, and returns the reviewed scope, concrete actionable findings, and remaining uncertainty. A bare approval without inspected scope is insufficient; no fixed JSON schema is required.

The reviewer performs one read-only pass, does not edit, recursively delegate review, or run broad suites. Focused non-mutating probes are allowed only within existing execution and credential permissions. Keep task content fixed while review runs and use event-driven waits within the host's supported limits. Use wait time for independent work that cannot change reviewed content.

Validate findings against supported behavior and fix them as one batch. Update strategy only when the contract or implementation shape changes. Rerun affected checks and obtain an independent review of the changed content and its relevant boundaries before accepting completion. Review the complete resulting task diff when scope or cross-cutting assumptions changed. Escalate to the high-risk procedure if a changed boundary or validated P0/P1 requires it; an ordinary verdict does not substitute for its required pair.

Preserve a compact count and unresolved-root summary in existing task notes across pauses and compaction. The initial cycle allows up to six reviewed revisions, and concrete feedback after a completed handoff starts a two-revision cycle. These are caps, not targets. On escalation, carry consumed revisions and unresolved root causes into the high-risk handoff; record the remaining cycle budget and give the strict ledger only that remainder. Ordinary verdicts earn no high-risk clean credit. If no budget remains, obtain a concrete user decision before another dispatch. Infrastructure retries on unchanged content do not consume a revision. If repeated findings expose the same design problem, apply the repository complexity-reset rule instead of adding conditions indefinitely. Ask for a concrete scope/design decision when progress needs one or the budget is exhausted; otherwise continue autonomously through fixes, review, verification, and handoff.

Preserve evidence through completion

Compare final task content with the recorded reviewed content. Changed behavior, expectations, contracts, or dependencies requires affected independent re-review. A demonstrably spelling/comment/formatting-only delta can retain prior review after self-check; do not extend that exemption to executable rewrites, public annotations, or behavioral prose. Committing or staging identical content does not invalidate review. A changed base requires checking the upstream delta and integration; if relevant source or tooling changed, or impact is uncertain, obtain affected re-review. Retain review only with recorded evidence of unchanged task behavior and unaffected dependencies.

Run every applicable final verification gate on the final content, using $code-change-verification for eligible SDK changes and documentation tiers for docs. A lighter review does not waive SDK checks. Preserving review after a formatting-only edit does not grant final-stack credit for changed content; follow the verification skill's final-content requirements. Report completion only when review and verification apply to the delivered state, then run $pr-draft-summary when required. If an independent reviewer is unavailable, report the missing review explicitly; self-review cannot satisfy the ordinary or high-risk gate.

High-risk resources

Read high-risk-review.md only for high-risk work. It uses reviewer-brief.md and the existing scripts/review_state.py and scripts/review_protocol.py helpers. Do not prepare their packets, receipts, or component inventories for a lightweight or ordinary change.

© openai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references) in .agents/skills/implementation-final-review of openai/openai-agents-python.

  • SKILL.md
  • agents/openai.yaml
  • references/high-risk-review.md
  • references/reviewer-brief.md
  • scripts/review_protocol.py
  • scripts/review_state.py
  • scripts/test_review_protocol.py
  • scripts/test_review_state.py

Open the folder on GitHubat commit 71c2da4

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders. This page covers the copy in openai/openai-agents-python, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Implementation Final Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Implementation Final Review compared with similar skills
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Implementation Final Review this skillopenai/openai-agents-python30k—~2kAutomated safety check: PassMIT
PR Design DocOpenHands/OpenHands90k—~2.4kAutomated safety check: PassMIT
Get API Docs with chubandrewyng/context-hub14k2 repos~775Automated safety check: PassMIT
Open Code Review CLIalibaba/open-code-review44k—~3.1kAutomated safety check: PassApache-2.0
Codexskills-directory/skill-codex1.5k3 repos~1.8kAutomated safety check: PassMIT
Changeset Validationopenai/openai-agents-js3.9k—~607Automated safety check: PassMIT

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Works with

Categories

Questions about Implementation Final Review

What does Implementation Final Review do?

Review completed implementation changes before final verification. Implementation Final Review is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Review completed implementation changes before final verification.

When should I use Implementation Final Review?

Implementation Final Review fits situations like: repository policy requires independent review; the user explicitly requests it.

How do I install Implementation Final Review in Claude Code?

Run `npx skills add openai/openai-agents-python --skill implementation-final-review -a claude-code`. Or copy the skill folder (.agents/skills/implementation-final-review in openai/openai-agents-python) into .claude/skills/implementation-final-review in your project. Claude Code loads it when a task matches its description.

How do I install Implementation Final Review in Codex?

Run `npx skills add openai/openai-agents-python --skill implementation-final-review -a codex`. Or copy the skill folder (.agents/skills/implementation-final-review in openai/openai-agents-python) into .agents/skills/implementation-final-review in your project. Codex loads it when a task matches its description.

Can I use Implementation Final Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openai/openai-agents-python --skill implementation-final-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/implementation-final-review, .gemini/skills/implementation-final-review, .github/skills/implementation-final-review and .opencode/skills/implementation-final-review in your project.

What does Implementation Final Review need to run?

Going by SKILL.md and its folder, Implementation Final Review needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Implementation Final Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Implementation Final Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Implementation Final Review use?

Implementation Final Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Implementation Final Review use?

About 2k tokens (SKILL.md is roughly 7.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 19k tokens, read only when the agent opens those files.

What are the alternatives to Implementation Final Review?

Skills that share tags, products or a category with Implementation Final Review: PR Design Doc (OpenHands/OpenHands, 90k stars), Get API Docs with chub (andrewyng/context-hub, 14k stars), Open Code Review CLI (alibaba/open-code-review, 44k stars) and Codex (skills-directory/skill-codex, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Implementation Final Review?

openai (a GitHub organization, an official publisher) maintains it in openai/openai-agents-python, which has 29,873 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 7, 2026.

Source: openai/openai-agents-python on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.