Borrowing it
Nothing to install: this file belongs to ctxpipe-ai/ctxpipe. 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/ctxpipe-ai/ctxpipe/main/.cursor/skills/code-review/SKILL.mdgit clone --depth 1 https://github.com/ctxpipe-ai/ctxpipeWrote 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/ctxpipe-ai/ctxpipe/code-review)<a href="https://agentmods.dev/skills/ctxpipe-ai/ctxpipe/code-review"><img src="https://agentmods.dev/badge/skills/ctxpipe-ai/ctxpipe/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/ctxpipe-ai/ctxpipe/code-review"><img src="https://agentmods.dev/badge/skills/ctxpipe-ai/ctxpipe/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 | $0.00076 | $0.01771 |
| Opus 5.5 | $0.00030 | $0.00708 |
| Sonnet 5.5 | $0.00015 | $0.00354 |
| Haiku 4.5 | $0.00008 | $0.00177 |
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 17d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Three-axis review of the diff between HEAD and a fixed point the user supplies:
- Standards — does the code conform to this repo's documented coding standards?
- Spec — does the code faithfully implement the originating issue / spec?
- Simplicity — is the machinery essential for the job, or accidental leftover?
All three axes run as parallel sub-agents (Opus, high effort) so they don't pollute each other's context, then this skill aggregates their findings. Do not run the review as spec-only.
The issue tracker should have been provided to you — run /setup-matt-pocock-skills if .ai/agents/issue-tracker.md is missing.
Process
1. Pin the fixed point
Whatever the user said is the fixed point — a commit SHA, branch name, tag, main, HEAD~5, etc. If they didn't specify one, ask for it.
Capture the diff command once: git diff <fixed-point>...HEAD (three-dot, so the comparison is against the merge-base). Also note the list of commits via git log <fixed-point>..HEAD --oneline.
Before going further, confirm the fixed point resolves (git rev-parse <fixed-point>) and the diff is non-empty. A bad ref or empty diff should fail here — not inside parallel sub-agents.
2. Identify the spec source
Look for the originating spec, in this order:
- Issue references in the commit messages (
#123,Closes #45, GitLab!67, etc.) — fetch via the workflow in.ai/agents/issue-tracker.md. - A path the user passed as an argument.
- A spec file under
docs/,specs/,.ai/scratchpad/, or.scratch/matching the branch name or feature. - If nothing is found, ask the user where the spec is. If they say there isn't one, the Spec sub-agent will skip and report "no spec available".
3. Identify the standards sources
Anything in the repo that documents how code should be written, such as CODING_STANDARDS.md or CONTRIBUTING.md.
On top of whatever the repo documents, the Standards axis always carries the smell baseline below — a fixed set of Fowler code smells (Refactoring, ch.3) that applies even when a repo documents nothing. Two rules bind it:
What ships with it
1 file 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.
- 17d ago Changed 571c8c849fda
- 1mo ago First seen · 99 lines · 76 tokens per session scan A 4bd296b5e9a0
code-review is a skill published in the GitHub repository ctxpipe-ai/ctxpipe (26 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 1,771 once invoked, about $0.0003 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.
Other skills, from other repositories
qa-review
Validate that implemented code fully satisfies Story acceptance criteria, respects rules, and introduces no regressions. This is the hard quality gate — no pass means no delivery. Activate after implementation is complete.
consistency-check
Detect inconsistencies across related artefacts and governance constraints. Activate after story generation, after plan preparation, before implementation, or after remediation attempts. Reports issues — does not fix them.
code-review
Performs multi-dimensional code review covering security, quality, spec compliance, and maintainability. Used when reviewing completed implementations or before merging changes.
maxsim-simplify
Reviews changed code for reuse opportunities, quality issues, and efficiency improvements, then fixes actionable items. Used after implementing features or when code feels over-engineered.
improve-codebase-architecture
Surface deepening refactors that turn shallow modules into deep ones, informed by CONTEXT.md and docs/adr/. Use when the user asks to improve architecture, find refactor candidates, raise testability, or make a codebase more agent-navigable. Skip for single localized fixes.
security-review
Adversarial security audit — STRIDE, OWASP Top 10, supply-chain (CVE/SBOM), secrets scan, auth/authz analysis. Use on changes touching auth, input parsing, deserialization, network I/O, dependencies, or secrets; before any production release or external-surface PR.