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.
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.
curl -O https://raw.githubusercontent.com/openai/openai-agents-python/main/.agents/skills/implementation-final-review/SKILL.mdgit clone --depth 1 https://github.com/openai/openai-agents-pythonWrote 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.
[](https://agentmods.dev/skills/openai/openai-agents-python/implementation-final-review)<a href="https://agentmods.dev/skills/openai/openai-agents-python/implementation-final-review"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/implementation-final-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.
<a href="https://agentmods.dev/skills/openai/openai-agents-python/implementation-final-review"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/implementation-final-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.01392 |
| Opus 5.5 | $0.00010 | $0.00557 |
| Sonnet 5.5 | $0.00005 | $0.00278 |
| Haiku 4.5 | $0.00003 | $0.00139 |
Grade A, and why
implementation-final-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.
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
| Tier | Boundary | Required review |
|---|---|---|
| Lightweight | Only 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. |
| Ordinary | Local 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 risk | Changes 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.
What ships with it
7 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.
- 1mo ago Changed · -158 lines · -32 tokens per session 3a27dd1df1cb
- 1mo ago First seen · 209 lines · 58 tokens per session scan A 55046b1e51b0
implementation-final-review is a skill published in the GitHub repository openai/openai-agents-python (30,339 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 1,392 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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