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 jscraik/Agent-Skills --skill codex-reviewgit clone --depth 1 https://github.com/jscraik/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/jscraik/agent-skills/codex-review)<a href="https://agentmods.dev/skills/jscraik/agent-skills/codex-review"><img src="https://agentmods.dev/badge/skills/jscraik/agent-skills/codex-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/jscraik/agent-skills/codex-review"><img src="https://agentmods.dev/badge/skills/jscraik/agent-skills/codex-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 | $0.00057 | $0.01184 |
| Opus 5 | $0.00028 | $0.00592 |
| Sonnet 5 | $0.00011 | $0.00237 |
| Haiku 4.5 | $0.00006 | $0.00118 |
Grade A, and why
codex-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 8d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codex Review
Philosophy
Use codex review as advisory closeout evidence, not approval to ship. Verify every finding against source before fixing or rejecting it.
When To Use
- Codex review, autoreview, second-model review, or merge-readiness evidence.
- Dirty patch, branch/PR diff, or commit review with P1-P3 triage.
- Independent review before final response, commit, PR update, or merge.
Avoid validation-only closeout, CodeRabbit inventory, broad PR sweeps, and Harness Engineering readiness reviews when those workflows own the task.
Inputs
- Repo path, branch, git status, target, base/commit ref, optional validation, and permission posture.
Outputs
Report the review command, target, accepted/rejected/blocked findings, validation result, and final clean result or blocker.
Discovery Interview
- Ask one round at a time when target, base, commit, validation, or permission boundary is unclear.
- Use a plain-language question.
- Explain why this matters before asking the user to choose.
- Avoid dumping the whole interview plan at once.
- Read
references/discovery-interview.mdwhen underspecified.
Procedure
- Pick target:
- dirty patch:
codex review --uncommitted - branch/PR diff:
codex review --base <base> - landed or single commit:
codex review --commit <ref>
- dirty patch:
- Prefer the helper:
Skills/agent-ops/codex-review/scripts/codex-review. - Verify each finding from source and classify it as accepted, rejected, or blocked.
- Patch only verified accepted findings at the smallest ownership boundary.
- Rerun focused validation and rerun review after review-triggered code changes.
- Stop when the final helper/review run exits 0 with no accepted/actionable findings.
- If nested review fails during Codex runtime initialization, rerun the helper once from the active Codex session with the exact filesystem-only retry profile in
references/helper-behavior.md. If it still hits app-server, sandbox, approval, or data-disclosure policy, classifyblocked_runtime, review the selected diff locally from source, and report the blocked command.
What ships with it
13 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.
- agents/openai.yaml 172 B
- references/contract.yaml 2.8 KB
- references/discovery-interview.md 1.9 KB
- references/evals.yaml 10 KB
- references/gitcrawl-recovery.md 755 B
- references/helper-behavior.md 4.2 KB
- references/preserved-behavior.md 2.2 KB
- references/review-output-classification.md 1.5 KB
- references/security-auditability.md 816 B
- references/target-selection.md 1.2 KB
- references/task-profile.json 1.5 KB
- references/validation-matrix.md 3.2 KB
- scripts/codex-review 15 KB
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.
- 8d ago First seen · 110 lines · 57 tokens per session scan A 597ea9ad9bb5
codex-review is a skill published in the GitHub repository jscraik/Agent-Skills (8 stars, last pushed 10d ago), licensed Apache-2.0. It adds 57 tokens to every session and 1,184 once invoked, about $0.0003 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 2026-09-03.
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doubt-driven-development
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magpie-reviewer-routing
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