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 skills add dayfinggg/openai-codex-agent-skills --skill code-reviewgit clone --depth 1 https://github.com/dayfinggg/openai-codex-agent-skillsWrote 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/dayfinggg/openai-codex-agent-skills/code-review)<a href="https://agentmods.dev/skills/dayfinggg/openai-codex-agent-skills/code-review"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/code-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/dayfinggg/openai-codex-agent-skills/code-review"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00034 | $0.00262 |
| Opus 5.5 | $0.00014 | $0.00105 |
| Sonnet 5.5 | $0.00007 | $0.00052 |
| Haiku 4.5 | $0.00003 | $0.00026 |
Grade A, and why
code-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 today.
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.
What it actually says
Recover the requested review scope, verified baseline, requirements, and affected code. Preserve the distinction between a review and implementation work.
Read host integration once for the imported review procedures.
| Review task | Read |
|---|---|
| Check requirement coverage, scope, repository standards, and maintainability | General review |
| Review security-sensitive changes, regressions in protection, or concrete attack paths | Security-focused differential review |
Use the security procedure when the user requests that analysis or the changed trust boundary makes it necessary. An ordinary low-risk review does not require a complete security audit. Keep evidence for requirement failures and security findings distinct.
The security procedure retains its methodology, adversarial analysis, reporting checklist, patterns, and assets. Use report templates as evidence checklists and follow the user's requested output format.
Perform the review directly with available tools. Confirm findings with affected code and behavior. A review request alone does not authorize edits, commits, publication, or new issue creation.
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.
- today First seen · 21 lines · 34 tokens per session scan A 154929883d32
code-review is a skill published in the GitHub repository dayfinggg/openai-codex-agent-skills (8 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 262 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 2026-10-30.
Other skills, from other repositories
audit
Repo audits, one mode or all (codebase, docs, process, perf, tests, threat-model, motion, SEO, debt). Triggers "audit the codebase", "full audit", "perf audit", "audit the tests", "threat model", "motion audit", "seo audit", "debt ledger".
self-review
Deliberate end-to-end review pass over a completed change, applying the laziness ladder. Invoke when the user asks for a review, or when orchestrating one. Not a routine step after every edit.
code-that-fits-in-your-head
Software-engineering heuristics based on Mark Seemann's Code That Fits in Your Head (2021), updated for agent-driven development. Use when writing or reviewing code, refactoring accidental complexity or a Big Ball of Mud, controlling technical or architectural debt in generated code, designing APIs and invariants…
dr-review
Review the local diff against Darkroom's checklist, or address feedback on the active PR. Triggers "review my changes", "check this diff", "address PR feedback". Native /code-review handles a diff or PR without the checklist.
review-batch
Batch-review diffs from several agents with per-change re-entry cards and abridged reading diffs. Triggers "review batch", "what's pending review", "reading diff", or after fanning out several agents.
git-pr-review
A read-only reviewer for GitHub pull requests, which are proposed code changes submitted for review. It produces an evidence-based report about whether a pull request should be merged.