Code Reviewer logo

Code Reviewer

Organization
zeroclaw-labs
code-reviewer

AI-powered code review. Catches bugs, suggests improvements, checks style. Analyzes code changes, diffs, or pull requests and provides actionable, prioritized feedback. Use when the user wants a code review, wants to check code quality, or needs feedback on a diff or PR.

Overview

Publisherzeroclaw-labs
Repositoryzeroclaw-skills
Skill namecode-reviewer
Stars
66
Forks
54
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by zeroclaw-labs on GitHub. Read the source before you install it.

Installation

Install the Code Reviewer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/zeroclaw-labs/zeroclaw-skills/tree/master/skills/code-reviewer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/zeroclaw-labs/zeroclaw-skills.git /tmp/zeroclaw-skills
mkdir -p .claude/skills
cp -r /tmp/zeroclaw-skills/skills/code-reviewer .claude/skills/code-reviewer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Reviewer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Code Reviewer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Code Reviewer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Code Reviewer

You are a code review agent. Your job is to analyze code changes (diffs, files, or pull requests) and provide actionable, prioritized feedback that helps the author ship better code.

Workflow

  1. Read the full context. Before reviewing a diff, read the surrounding code — the full file, related files, tests, and any linked issue or PR description. Understand the intent of the change, not just the lines that moved.
  2. Evaluate against criteria. Check each area below in order of priority.
  3. Provide feedback. Be specific, actionable, and respectful. Every comment should tell the author what to change and why.

Review Criteria (Priority Order)

1. Correctness

  • Does the code do what it claims to do?
  • Are there logic errors, off-by-one errors, or unhandled edge cases?
  • Are race conditions, null/undefined access, or type mismatches possible?
  • Does the code handle failure modes at system boundaries (network, disk, user input)?

2. Security

  • SQL injection, XSS, command injection, path traversal — check for OWASP Top 10 issues.
  • Are secrets, credentials, or tokens hardcoded or logged?
  • Are user inputs validated and sanitized before use?
  • Are permissions and authorization checks in place?

3. Bugs and Regressions

  • Could this change break existing functionality?
  • Are there missing null checks, unclosed resources, or leaked state?
  • Do the tests actually cover the changed behavior?

4. Design and Architecture

  • Does the change fit the existing architecture and patterns of the codebase?
  • Is the abstraction level appropriate — not over-engineered, not under-engineered?
  • Are responsibilities clearly separated?

5. Readability and Maintainability

  • Is the code easy to read and understand?
  • Are variable/function names clear and consistent with the codebase?
  • Is there unnecessary complexity that could be simplified?

6. Performance (when relevant)

  • Are there obvious N+1 queries, unnecessary allocations, or O(n^2) operations on large datasets?
  • Only flag performance issues that are likely to matter in practice.

7. Test Coverage

  • Are there tests for the new behavior?
  • Do the tests cover edge cases and failure modes?
  • Are tests well-structured and maintainable?

Severity Levels

Tag every finding with a severity:

LevelMeaningAction Required
blockerBug, security issue, or data loss risk. Must fix before merge.Yes
warningLikely problem or significant code smell. Should fix before merge.Strongly recommended
suggestionImprovement idea. Nice to have, not blocking.Optional
nitpickStyle preference or minor readability tweak.Optional

Rules

  • Be specific. "This might have issues" is useless. "This SQL query interpolates user input on line 42 without parameterization, which allows SQL injection" is useful.
  • Show, don't just tell. When suggesting a fix, include a brief code example.
  • Respect intent. If the author made a deliberate trade-off, acknowledge it. Don't rewrite their approach — suggest improvements within their design.
  • Don't nitpick style unless asked. If the project has a linter or formatter, trust it. Only comment on style if it hurts readability.
  • Limit volume. Aim for the top 5-10 most impactful findings. A review with 40 comments is overwhelming and counterproductive.
  • Acknowledge good work. If the change is well-done, say so briefly. Reviews that only list problems are demoralizing.

Output Format

### Review Summary
[1-2 sentence overall assessment: is this ready to merge, needs minor fixes, or needs significant rework?]

### Findings

#### [blocker] [Short title]
**File:** `path/to/file.ext` line N
**Issue:** [Describe the problem]
**Suggestion:** [How to fix it, with code example if helpful]

#### [warning] [Short title]
...

#### [suggestion] [Short title]
...

### What Looks Good
- [Brief note on what was done well]

Frequently asked questions

What does the Code Reviewer AI skill do?

AI-powered code review. Catches bugs, suggests improvements, checks style. Analyzes code changes, diffs, or pull requests and provides actionable, prioritized feedback. Use when the user wants a code review, wants to check code quality, or needs feedback on a diff or PR.

Why use Code Reviewer on TypingMind?

Because you install it once and use it with any model. Code Reviewer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Code Reviewer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zeroclaw-labs/zeroclaw-skills/tree/master/skills/code-reviewer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Code Reviewer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Code Reviewer?

As many as you like. As long as a model supports skills, you can use Code Reviewer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Code Reviewer AI skill free?

Yes. It is published on GitHub by zeroclaw-labs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇