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Agent skill

AI Code Review

by mohitagw15856 in mohitagw15856/pm-claude-skills

Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts.

MITAuto-check passedDevelopment

Install AI Code Review

skills CLI
$ npx skills add mohitagw15856/pm-claude-skills --skill ai-code-review -a claude-code

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

GitHub CLI
$ gh skill install mohitagw15856/pm-claude-skills ai-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/mohitagw15856/pm-claude-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-code-review .claude/skills/ai-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
ai-code-review
GitHub stars
1.4k
Token cost
~1.5k tokens
SKILL.md length
757 words
Files
1
Skills in repo
1,348
Repo updated
First seen
Licence
MIT

At a glance

Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts.

  • Works in 7 steps: Plausible-but-wrong logic. The code… → Hallucinated or misused APIs. Methods… → Tests that test nothing. Asserting mocks… → …
  • Reviewing an AI-generated
  • SKILL.md covers What This Skill Produces, Required Inputs, The AI-Characteristic Failure… and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Code Review is an agent skill from mohitagw15856/pm-claude-skills. Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts. Use when reviewing an AI-generated or heavily AI-assisted PR, when AI-written code keeps shipping subtle bugs, or when setting review standards for a team using coding agents. Produces a focused review with AI-specific findings, verification steps per risk class, and a team checklist for AI-authored changes. For general PR review use…

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Code review, Pull requests and Code simplification. The repository describes itself as: 1255 professional Agent Skills for Claude, ChatGPT, Gemini, Cursor & Codex — PRDs, postmortems, leases, medical bills, layoffs, go-bags, new countries. Plain markdown, MIT, in… The licence is MIT.

When your agent uses it

  • Reviewing an AI-generated
  • Heavily AI-assisted PR
  • AI-written code keeps shipping subtle bugs
  • Setting review standards for a team using coding agents

Example prompts

  • “/ai-code-review”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Plausible-but-wrong logic. The code reads correctly and does something subtly different: inverted edge conditions, off-by-one on…
  2. Hallucinated or misused APIs. Methods that don't exist in this version, config keys from a different library, plausible-sounding…
  3. Tests that test nothing. Asserting mocks return what they were mocked to return; happy-path-only suites with confident names; tests copied…
  4. Reinvention and drift. A new utility duplicating an existing one (the AI didn't know your utils/), a new pattern where the codebase has a…
  5. Over-engineering as default. Speculative generality: interfaces with one implementer, config for things that never vary, error hierarchies…
  6. Dead scaffolding. Unused imports/variables, TODO stubs presented as done, commented-out alternatives, leftover debug logging. Cheap to…
  7. Silent security shortcuts. Broad exception swallowing, disabled TLS verification "for now", string-built SQL, secrets in examples that…

What it can do on your machine

Read from SKILL.md and the folder at commit 1cbf1f0. 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

    No scripts in the folder and no shell commands in SKILL.md.

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

  • Network

    No URLs in SKILL.md.

    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

AI Code Review loads about 1.5k tokens when it runs. Until then it costs about 153 tokens; SKILL.md has 757 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~153
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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 mohitagw15856/pm-claude-skills at commit 1cbf1f0, republished under its MIT licence (© mohitagw15856). 757 words, ~1,523 tokens.

Download SKILL.mdSave it as .claude/skills/ai-code-review/SKILL.md (or your agent's skills folder).
name
ai-code-review
description
Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts. Use when reviewing an AI-generated or heavily AI-assisted PR, when AI-written code keeps shipping subtle bugs, or when setting review standards for a team using coding agents. Produces a focused review with AI-specific findings, verification steps per risk class, and a team checklist for AI-authored changes. For general PR review use code-review-checklist — this skill covers what that one assumes a human wouldn't do.

AI Code Review Skill

Human code fails where the human got tired or didn't know; AI code fails where plausibility diverged from correctness — and it fails fluently, with confident naming, clean formatting, and tests that pass without testing anything. Reviewing it with human-code instincts ("looks careful, probably is careful") is how the new bug class ships. This skill reviews for the failure modes that are characteristically AI.

Not quite this? Use code-review-guide when the code was written by people and needs a general review.

What This Skill Produces

  • A review of the change organised by AI-characteristic risk, each finding with file/line and severity
  • Verification steps the reviewer must actually run (not read) per risk class
  • A team checklist for AI-authored PRs, calibrated to this codebase

Required Inputs

Ask for (if not already provided):

  • The diff or PR (or the files changed)
  • Provenance honestly: fully agent-written, human-piloted, or mixed — and whether the author reviewed it themselves before requesting review
  • The codebase context: existing conventions/utilities the AI may not have known, and what the change claims to do
  • Test infrastructure: what CI actually runs (the AI may have written tests CI never executes)

The AI-Characteristic Failure Modes

Review in this order — most damaging first:

  1. Plausible-but-wrong logic. The code reads correctly and does something subtly different: inverted edge conditions, off-by-one on boundaries the prompt never mentioned, the right algorithm for a slightly different problem. Verification: trace 2-3 concrete inputs through the changed logic by hand — the fluency of the code is not evidence; it's the camouflage.
  2. Hallucinated or misused APIs. Methods that don't exist in this version, config keys from a different library, plausible-sounding parameters silently ignored. Verification: for every external API call touched, check the actual dependency version's docs — not memory, not the AI's comment.
  3. Tests that test nothing. Asserting mocks return what they were mocked to return; happy-path-only suites with confident names; tests copied from the implementation (tautological). Verification: mentally break the implementation — would any test fail? If not, the coverage number is decoration.
  4. Reinvention and drift. A new utility duplicating an existing one (the AI didn't know your utils/), a new pattern where the codebase has a convention, a second source of truth. Verification: for each new helper/abstraction, grep for the existing equivalent.
  5. Over-engineering as default. Speculative generality: interfaces with one implementer, config for things that never vary, error hierarchies for a script. AI pads scope because scope was ambiguous. Finding, not felony — but it's yours to maintain forever.
  6. Dead scaffolding. Unused imports/variables, TODO stubs presented as done, commented-out alternatives, leftover debug logging. Cheap to catch, and its presence predicts the deeper failures — a diff with scaffolding wasn't self-reviewed.
  7. Silent security shortcuts. Broad exception swallowing, disabled TLS verification "for now", string-built SQL, secrets in examples that became code, permissive CORS. AI reproduces the internet's average security posture unless told otherwise. Verification: run the security linters even for a "trivial" change; the shortcut is rarely where the feature is.
Show full SKILL.md (262 more words)Show less

Output Format

AI Code Review: [PR/change] — provenance: [stated]

Verdict: ✅ approve / 🟡 approve with required fixes / 🔴 request changes — [one line]

Findings

#Failure modeLocationSeverityFinding + fix

Verified by running: [the hand-traces, API checks, and break-the-test exercises actually performed — a review that only read the diff says so]

Debt accepted knowingly: [over-engineering/style items merged anyway, listed so they're chosen]

Team checklist for AI-authored PRs: [the 7 modes as a calibrated checklist + the house rule: AI-assisted PRs declare provenance, and the author self-reviews before requesting review]

Quality Checks

  • At least one concrete input was hand-traced through the changed logic
  • Every touched external API was verified against the actual dependency version
  • Each test was assessed by "what breakage would this catch?"
  • New helpers were grepped against existing utilities
  • The verdict distinguishes required fixes from accepted debt

Anti-Patterns

  • Do not extend human-code trust heuristics ("clean and well-named, so probably correct") — fluency is the failure mode's costume
  • Do not approve on green CI without checking whether the tests can fail
  • Do not review the description instead of the diff — AI PR descriptions are confident summaries of intent, not of behaviour
  • Do not reject code for being AI-written — review the code; provenance calibrates scrutiny, not verdicts
  • Do not skip security linting because the change is small — the shortcut hides in the periphery
  • Do not accept "the agent tested it" as verification — demand the evidence in the PR

Example Trigger Phrases

  • "Review this AI-generated pull request."
  • "Claude wrote this code: what did it get wrong?"
  • "Check this PR for hallucinated APIs."
  • "Set review standards for a team using coding agents."

© mohitagw15856, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/ai-code-review of mohitagw15856/pm-claude-skills.

Open the folder on GitHubat commit 1cbf1f0

Compare with similar skills

AI 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.

AI Code Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Code Review this skillmohitagw15856/pm-claude-skills1.4k—~1.5kAutomated safety check: PassMIT
Lean CodeAnastasiyaW/codex-claude-code-config154—~1.3kAutomated safety check: PassMIT
PR Babysitteropeninterpreter/openinterpreter69k3 repos~4.2kAutomated safety check: PassApache-2.0
WooCommerce Code Reviewwoocommerce/woocommerce11k3 repos~1.1kAutomated safety check: PassCustom licence
Open Code Review CLIalibaba/open-code-review46k—~3.1kAutomated safety check: PassApache-2.0
GitHub Review Iterationprisma/orm48k—~2.2kAutomated safety check: PassApache-2.0

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Categories

Questions about AI Code Review

What does AI Code Review do?

Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts. AI Code Review is an agent skill from mohitagw15856/pm-claude-skills. Review AI-authored code for its characteristic failure modes — plausible-but-wrong logic, hallucinated APIs, over-engineering, dead scaffolding, and silent security shortcuts.

When should I use AI Code Review?

AI Code Review fits situations like: reviewing an AI-generated; heavily AI-assisted PR; AI-written code keeps shipping subtle bugs; setting review standards for a team using coding agents.

How do I install AI Code Review in Claude Code?

Run `npx skills add mohitagw15856/pm-claude-skills --skill ai-code-review -a claude-code`. Or copy the skill folder (skills/ai-code-review in mohitagw15856/pm-claude-skills) into .claude/skills/ai-code-review in your project. Claude Code loads it when a task matches its description.

How do I install AI Code Review in Codex?

Run `npx skills add mohitagw15856/pm-claude-skills --skill ai-code-review -a codex`. Or copy the skill folder (skills/ai-code-review in mohitagw15856/pm-claude-skills) into .agents/skills/ai-code-review in your project. Codex loads it when a task matches its description.

Can I use AI 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 mohitagw15856/pm-claude-skills --skill ai-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/ai-code-review, .gemini/skills/ai-code-review, .github/skills/ai-code-review and .opencode/skills/ai-code-review in your project.

What does AI Code Review need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Code Review is instructions for the agent only.

Does AI Code Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI 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 AI Code Review use?

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

How many tokens does AI Code Review use?

About 1.5k tokens (SKILL.md is roughly 6.1k 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 AI Code Review?

Skills that share tags, products or a category with AI Code Review: Lean Code (AnastasiyaW/codex-claude-code-config, 154 stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars), WooCommerce Code Review (woocommerce/woocommerce, 11k stars) and Open Code Review CLI (alibaba/open-code-review, 46k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Code Review?

mohitagw15856 (a GitHub user) maintains it in mohitagw15856/pm-claude-skills, which has 1,434 GitHub stars. The repository holds 1,348 skills in this directory. The repository was last updated on October 9, 2026.

Source: mohitagw15856/pm-claude-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.