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/codex/codex-pr-bodynpx skills add openai/codex --skill codex-pr-bodygit clone --depth 1 https://github.com/openai/codexWhat 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 | $0.00017 | $0.00985 |
| Opus 5 | $0.00009 | $0.00492 |
| Sonnet 5 | $0.00003 | $0.00197 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
codex-pr-body 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 2d 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.
Copies of this mod
7 near-identical copies found in the catalogue:
- codex-pr-body — 100% identical, 0 lines differ
- codex-pr-body — 100% identical, 0 lines differ
- codex-pr-body — 100% identical, 0 lines differ
- codex-pr-body — 100% identical, 0 lines differ
- codex-pr-body — 100% identical, 0 lines differ
- codex-pr-body — 100% identical, 0 lines differ
- codex-pr-body — 92% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 62 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Determining the PR(s)
When this skill is invoked, the PR(s) to update may be specified explicitly, but in the common case, the PR(s) to update will be inferred from the branch / commit that the user is currently working on. For ordinary Git usage (i.e., not Sapling as discussed below), you may have to use a combination of git branch and gh pr view <branch> --repo openai/codex --json number --jq '.number' to determine the PR associated with the current branch / commit.
PR Body Contents
When invoked, use gh to edit the pull request body and title to reflect the contents of the specified PR. Make sure to check the existing pull request body to see if there is key information that should be preserved. For example, NEVER remove an image in the existing pull request body, as the author may have no way to recover it if you remove it.
It is critically important to explain why the change is being made. If the current conversation in which this skill is invoked has discussed the motivation, be sure to capture this in the pull request body.
The body should also explain what changed, but this should appear after the why.
Limit discussion to the net change of the commit. It is generally frowned upon to discuss changes that were attempted but later undone in the course of the development of the pull request. When rewriting the pull request body, you may need to eliminate details such as these when they are no longer appropriate / of interest to future readers.
Avoid references to absolute paths on my local disk. When talking about a path that is within the repository, simply use the repo-relative path.
Avoid references to confidential information including but not limited to codenames or OpenAI-internal URLs.
It is generally helpful to discuss how the change was verified. That said, it is unnecessary to mention things that CI checks automatically, e.g., do not include "ran just fmt" as part of the test plan. Though identifying the new tests that were purposely introduced to verify the new behavior introduced by the pull request is often appropriate.
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.
- 2d ago First seen · 62 lines · 17 tokens per session scan A 716cacb29179
codex-pr-body is a skill published in the GitHub repository openai/codex (120,598 stars, last pushed today), licensed Apache-2.0. It adds 17 tokens to every session and 985 once invoked, about $0.0001 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.