/review

> Thorough code review across correctness, security, performance, and maintainability.

170 stars

byMiosa-osa

View on GitHub Installation ↓

Best use case

/review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

> Thorough code review across correctness, security, performance, and maintainability.

Teams using /review should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/review/SKILL.md --create-dirs "https://raw.githubusercontent.com/Miosa-osa/canopy/main/library/skills/development/review/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/review/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How /review Compares

Feature / Agent	/review	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

> Thorough code review across correctness, security, performance, and maintainability.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

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SKILL.md Source

# /review

> Thorough code review across correctness, security, performance, and maintainability.

## Usage
```
/review [path|PR#] [--focus <area>] [--strict]
```

## What It Does
Reviews code changes across 5 dimensions: correctness, security, performance, maintainability, and testing. Produces a structured verdict with categorized findings (critical, major, minor).

## Implementation
1. **Load changes** -- read diff from file, directory, or PR.
2. **Correctness check** -- logic errors, edge cases, error handling, null checks.
3. **Security check** -- input validation, injection prevention, auth, secrets in code.
4. **Performance check** -- N+1 queries, unnecessary re-renders, memory leaks, algorithm efficiency.
5. **Maintainability check** -- naming, single responsibility, abstraction level, DRY.
6. **Testing check** -- test existence, edge case coverage, mock appropriateness.
7. **Report** -- verdict (APPROVED / CHANGES REQUESTED / BLOCKED) with categorized findings.

## Examples
```bash
# Review current changes
/review

# Review a specific file
/review lib/optimal_engine/session.ex

# Review a PR
/review 42

# Focus on security only
/review --focus security
```

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Build custom annotation UIs for human review of agent traces, LLM outputs, and labeled data. Generates a self-contained HTML interface for reviewing, labeling, comparing, and exporting judgments. For calibrating evals, auditing agent behavior, and building gold-standard datasets. Triggers on: "review interface", "annotation ui", "labeling interface", "review ui", "human review"

code-review

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Branch code review with structured scoring against project guidelines. Produces categorized findings with severity levels and LLM-generated fix prompts. Quality gate: only posts review to PR if score meets threshold. Triggers on: "code review", "review branch", "review PR", "review changes", "score code"