Agent skill

Code Review

by asgeirtj in asgeirtj/system_prompts_leaks

Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level…

CC0-1.0Auto-check passedAgent Workflows

Install Code Review

skills CLI
$ npx skills add asgeirtj/system_prompts_leaks --skill code-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgeirtj/system_prompts_leaks code-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgeirtj/system_prompts_leaks.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Anthropic/claude-code/skills/code-review .claude/skills/code-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
code-review
GitHub stars
69k
Token cost
~1.6k tokens
SKILL.md length
854 words
Files
7
Skills in repo
121
Repo updated
First seen
Licence
CC0-1.0

At a glance

Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level…

  • Works in 3 steps: Gather the diff → Find candidates (3 correctness angles +… → Dedup only (no verify)
  • Tasks that involve Code review
  • SKILL.md covers Phase 0 — Gather the diff, Phase 1 — Find candidates (3…, Phase 2 — Dedup only (no verify) and Output
  • Calls git

What it does

Code Review is an agent skill from asgeirtj/system_prompts_leaks. Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings; ultra: deep multi-agent review in the cloud (requires claude.ai account access)); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply…

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files (for example `high.md`, `low.md` and `max.md`).

It sits in Agent Workflows, covering Code review. It works with GitHub and Git. The repository describes itself as: Documented system prompts from Anthropic - Claude Fable 5.1, Opus 5.5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-6-Astra, Codex. Google - Gemini 3.8 Flash, 3.1 Pro… The licence is CC0-1.0.

When your agent uses it

  • Tasks that involve Code review

Example prompts

  • “/code-review”

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Gather the diff
  2. Find candidates (3 correctness angles + 3 cleanup angles + 1 altitude angle + 1 conventions angle, up to 6 each)
  3. Dedup only (no verify)

What it can do on your machine

Read from SKILL.md and the folder at commit dd45aa5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Code Review loads about 1.6k tokens when it runs. Until then it costs about 222 tokens; SKILL.md has 854 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~222
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgeirtj/system_prompts_leaks at commit dd45aa5, republished under its CC0-1.0 licence (© asgeirtj). 854 words, ~1,634 tokens.

Download SKILL.mdSave it as .claude/skills/code-review/SKILL.md (or your agent's skills folder). This skill also uses 6 other files; get the full folder from GitHub.
name
code-review
description
Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings; ultra: deep multi-agent review in the cloud (requires claude.ai account access)); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review. For ultra on a GitHub.com PR target, --post asks to post the finished review’s findings to the PR as a single comment from the user’s GitHub account (not a review; the launch dialog still confirms in interactive sessions, while non-interactive mode posts on the flag alone) and --no-post hides that option.

high effort → 8 inline angles → dedup (no verify) → ≤10 findings

You are reviewing for recall at high effort: catch every real bug a careful reviewer would catch in one sitting. At this level, catching real bugs matters more than avoiding false positives. Err on the side of surfacing.

Phase 0 — Gather the diff

Run git diff @{upstream}...HEAD (or git diff main...HEAD / git diff HEAD~1 if there's no upstream) to get the unified diff under review. If there are uncommitted changes, or the range diff is empty, also run git diff HEAD and include the working-tree changes in scope — the review often runs before the commit. If a PR number, branch name, or file path was passed as an argument, review that target instead. Treat this diff as the review scope.

Phase 1 — Find candidates (3 correctness angles + 3 cleanup angles + 1 altitude angle + 1 conventions angle, up to 6 each)

Run 8 independent finder angles in sequence yourself, in THIS context — do NOT spawn subagents for them. Each surfaces up to 6 candidate findings with file, line, a one-line summary, and a concrete failure_scenario.

Angle A — line-by-line diff scan

Read every hunk in the diff, line by line. Then Read the enclosing function for each hunk — bugs in unchanged lines of a touched function are in scope (the PR re-exposes or fails to fix them). For every line ask: what input, state, timing, or platform makes this line wrong? Look for inverted/wrong conditions, off-by-one, null/undefined deref, missing await, falsy-zero checks, wrong-variable copy-paste, error swallowed in catch, unescaped regex metachars.

Angle B — removed-behavior auditor

For every line the diff DELETES or replaces, name the invariant or behavior it enforced, then search the new code for where that invariant is re-established. If you can't find it, that's a candidate: a removed guard, a dropped error path, a narrowed validation, a deleted test that was covering a real case.

Angle C — cross-file tracer

For each function the diff changes, find its callers (Grep for the symbol) and check whether the change breaks any call site: a new precondition, a changed return shape, a new exception, a timing/ordering dependency. Also check callees: does a parallel change in the same PR make a call unsafe?

Reuse

The angles above hunt for bugs; this one and the next two hunt for cleanup in the changed code. Flag new code that re-implements something the codebase already has — Grep shared/utility modules and files adjacent to the change, and name the existing helper to call instead.

Simplification

Flag unnecessary complexity the diff adds: redundant or derivable state, copy-paste with slight variation, deep nesting, dead code left behind. Name the simpler form that does the same job.

Efficiency

Flag wasted work the diff introduces: redundant computation or repeated I/O, independent operations run sequentially, blocking work added to startup or hot paths. Also flag long-lived objects built from closures or captured environments — they keep the entire enclosing scope alive for the object's lifetime (a memory leak when that scope holds large values); prefer a class/struct that copies only the fields it needs. Name the cheaper alternative.

Show full SKILL.md (339 more words)Show less
Altitude

Check that each change fixes the root cause at the right depth rather than patching a symptom with a fragile bandaid. Special cases layered on shared infrastructure are a sign the fix isn't deep enough — prefer the simpler, more general change to the underlying mechanism over adding special cases, and name that change.

Conventions (CLAUDE.md)

Find the CLAUDE.md files that govern the changed code: the user-level ~/.claude/CLAUDE.md, the repo-root CLAUDE.md, plus any CLAUDE.md or CLAUDE.local.md in a directory that is an ancestor of a changed file (a directory's CLAUDE.md only applies to files at or below it). Read each one that exists, then check the diff for clear violations of the rules they state.

Only flag a violation when you can quote the exact rule and the exact line that breaks it — no style preferences, no vague "spirit of the doc" inferences. In the finding, name the CLAUDE.md path and quote the rule so the report can cite it. If no CLAUDE.md applies, return nothing for this angle.

Cleanup, altitude, and conventions candidates use the same file/line/summary shape; in failure_scenario, state the concrete cost (what is duplicated, wasted, harder to maintain, or which CLAUDE.md rule is broken) instead of a crash. Correctness bugs always outrank cleanup, altitude, and conventions findings when the output cap forces a cut.

Pass every candidate with a nameable failure scenario through — finders that silently drop half-believed candidates are the dominant cause of misses.

Phase 2 — Dedup only (no verify)

Pool all candidates. Dedup near-duplicates only (same defect, same location, same reason → keep one). Do NOT run verifiers; do NOT re-judge. Sort by severity.

Output

Target at least 5 findings. If fewer genuine findings exist, emit what you have — do not invent to hit the floor.

Return findings as a JSON array of at most 10 objects:

json
[
  {
    "file": "path/to/file.ext",
    "line": 123,
    "summary": "one-sentence statement of the bug",
    "failure_scenario": "concrete inputs/state → wrong output/crash"
  }
]

Ranked most-severe first. If more than 10 survive, keep the 10 most severe. If nothing survives, return []. Do not call the ReportFindings tool even if it is available - this review's output contract is the JSON block above.

© asgeirtj, CC0-1.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 6 other files in Anthropic/claude-code/skills/code-review of asgeirtj/system_prompts_leaks.

  • SKILL.md
  • high.md
  • low.md
  • max.md
  • medium.md
  • report-findings-tool.md
  • xhigh.md

Open the folder on GitHubat commit dd45aa5

Compare with similar skills

Code Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Code Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Code Review this skillasgeirtj/system_prompts_leaks69k—~1.6kAutomated safety check: PassCC0-1.0
PR Review State Fetchprisma/orm48k—~767Automated safety check: PassApache-2.0
Greploop Appsmichaelshimeles/skills1.3k1 repos~3.6kAutomated safety check: PassMIT
Requesting Code ReviewHezaoHezao/poirot2505 repos~1.6kAutomated safety check: PassMIT
PR Triagertk-ai/rtk83k—~2.5kAutomated safety check: NotesApache-2.0
Difit Reviewyoshiko-pg/difit3.2k—~1.2kAutomated safety check: PassMIT

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Works with

Questions about Code Review

What does Code Review do?

Review the current diff, or a PR number/branch/path target, for correctness bugs (plus reuse/simplification/efficiency cleanups where the model's review recipe covers them) at the given effort level…. Code Review is an agent skill from asgeirtj/system_prompts_leaks.ai account access)); with no level given, it reuses the level you typed last.

When should I use Code Review?

Code Review fits situations like: tasks that involve Code review.

How do I install Code Review in Claude Code?

Run `npx skills add asgeirtj/system_prompts_leaks --skill code-review -a claude-code`. Or copy the skill folder (Anthropic/claude-code/skills/code-review in asgeirtj/system_prompts_leaks) into .claude/skills/code-review in your project. Claude Code loads it when a task matches its description.

How do I install Code Review in Codex?

Run `npx skills add asgeirtj/system_prompts_leaks --skill code-review -a codex`. Or copy the skill folder (Anthropic/claude-code/skills/code-review in asgeirtj/system_prompts_leaks) into .agents/skills/code-review in your project. Codex loads it when a task matches its description.

Can I use Code Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgeirtj/system_prompts_leaks --skill code-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/code-review, .gemini/skills/code-review, .github/skills/code-review and .opencode/skills/code-review in your project.

What does Code Review need to run?

Going by SKILL.md and its folder, Code Review needs the command-line tools its instructions call (git).

Does Code Review access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Code Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Code Review use?

Code Review is published under the CC0-1.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Code Review use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Code Review?

Skills that share tags, products or a category with Code Review: PR Review State Fetch (prisma/orm, 48k stars), Greploop Apps (michaelshimeles/skills, 1.3k stars), Requesting Code Review (HezaoHezao/poirot, 250 stars) and PR Triage (rtk-ai/rtk, 83k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Code Review?

asgeirtj (a GitHub user) maintains it in asgeirtj/system_prompts_leaks, which has 69,095 GitHub stars. The repository holds 121 skills in this directory. The repository was last updated on October 7, 2026.

Source: asgeirtj/system_prompts_leaks on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.