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 Cristhianzl/claude-skills-czl --skill reviewing-codegit clone --depth 1 https://github.com/Cristhianzl/claude-skills-czlWrote 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/cristhianzl/claude-skills-czl/reviewing-code)<a href="https://agentmods.dev/skills/cristhianzl/claude-skills-czl/reviewing-code"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/reviewing-code/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/cristhianzl/claude-skills-czl/reviewing-code"><img src="https://agentmods.dev/badge/skills/cristhianzl/claude-skills-czl/reviewing-code.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 | $0.00101 | $0.02901 |
| Opus 5 | $0.00051 | $0.01451 |
| Sonnet 5 | $0.00020 | $0.00580 |
| Haiku 4.5 | $0.00010 | $0.00290 |
Grade A, and why
reviewing-code 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 12d 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 — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reviewing Code
Produce a PR review optimized for posting as a single GitHub comment. The review applies a security lens, a comprehension audit, and a structural check, then labels findings by severity with a copy-paste-safe action checklist.
Read first (always)
List learnings/ and read every file relevant to the current PR (the touched modules, frameworks, or risk areas). Project-specific review conventions, severity adjustments, banned patterns, or scope rules live there and override the defaults in this SKILL.md. If a learning conflicts with this file, the learning wins — mention it to the user.
Tradeoff — when to apply, when to lighten up
Apply the full discipline (security lens + comprehension audit + checklist + grep) for production-bound PRs touching shared services, payments, auth, user data, AI runtime, or anything externally observable.
Lighten formality for: docs-only changes, lockfile bumps with no API change, single-line typo fixes, internal-tooling-only changes behind a feature flag not yet enabled. Still apply the security lens — even doc PRs can leak secrets in examples.
Hard rules — output is a chat message, not a side effect
Apply the same restrictions as the writing-pull-requests skill, plus review-specific ones:
- Never run
gh pr review,gh pr comment, or any GitHub-mutating command. The user posts the review themselves. - Never run
git commit,git add,git push. Only the human commits. - Never write the review to a file. Output goes in the chat.
- Never use
#Nto label findings — GitHub auto-links it to PR/issueN. UseB1,I2,R3, etc. - Never
@mentionusers unless the user explicitly asks. - Never include
Fixes #N,Closes #N,Resolves #N— they auto-close issues on merge. - Never paste full file contents — link with
path/to/file.ts:42and quote 3-10 relevant lines max. - Never use absolute local paths (
/Users/...,/home/...). Use repo-relative paths only. - All review documents are written entirely in English, regardless of the conversation language.
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.
- 12d ago First seen · 186 lines · 101 tokens per session scan A 4c97494c7f2c
reviewing-code is a skill published in the GitHub repository Cristhianzl/claude-skills-czl (5 stars, last pushed yesterday), licensed MIT. It adds 101 tokens to every session and 2,901 once invoked, about $0.0005 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 2026-08-31.
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