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Code Review

Organization
IAPro-Community
code-review

Run a comprehensive code review

Overview

PublisherIAPro-Community
RepositoryOrquestrador-Maestro
Skill namecode-review
Stars
63
Forks
11
Bundled files
Instructions only
LicenseApache-2.0
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 IAPro-Community on GitHub. Read the source before you install it.

Installation

Install the Code Review 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/IAPro-Community/Orquestrador-Maestro/tree/v1/codex/skills/code-review
  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/IAPro-Community/Orquestrador-Maestro.git /tmp/Orquestrador-Maestro
mkdir -p .claude/skills
cp -r /tmp/Orquestrador-Maestro/codex/skills/code-review .claude/skills/code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Code Review 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 Review 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 Review 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 Review Skill

Conduct a thorough code review for quality, security, and maintainability with severity-rated feedback.

When to Use

This skill activates when:

  • User requests "review this code", "code review"
  • Before merging a pull request
  • After implementing a major feature
  • User wants quality assessment

What It Does

GPT-5.4 Guidance Alignment

  • Default to concise, evidence-dense progress and completion reporting unless the user or risk level requires more detail.
  • Treat newer user task updates as local overrides for the active workflow branch while preserving earlier non-conflicting constraints.
  • If correctness depends on additional inspection, retrieval, execution, or verification, keep using the relevant tools until the review is grounded.
  • Continue through clear, low-risk, reversible next steps automatically; ask only when the next step is materially branching, destructive, or preference-dependent.

Delegates to the code-reviewer agent (THOROUGH tier) for deep analysis:

  1. Identify Changes

    • Run git diff to find changed files
    • Determine scope of review (specific files or entire PR)
  2. Review Categories

    • Security - Hardcoded secrets, injection risks, XSS, CSRF
    • Code Quality - Function size, complexity, nesting depth
    • Performance - Algorithm efficiency, N+1 queries, caching
    • Best Practices - Naming, documentation, error handling
    • Maintainability - Duplication, coupling, testability
  3. Severity Rating

    • CRITICAL - Security vulnerability (must fix before merge)
    • HIGH - Bug or major code smell (should fix before merge)
    • MEDIUM - Minor issue (fix when possible)
    • LOW - Style/suggestion (consider fixing)
  4. Specific Recommendations

    • File:line locations for each issue
    • Concrete fix suggestions
    • Code examples where applicable

Agent Delegation

delegate(
  role="code-reviewer",
  tier="THOROUGH",
  prompt="CODE REVIEW TASK

Review code changes for quality, security, and maintainability.

Scope: [git diff or specific files]

Review Checklist:
- Security vulnerabilities (OWASP Top 10)
- Code quality (complexity, duplication)
- Performance issues (N+1, inefficient algorithms)
- Best practices (naming, documentation, error handling)
- Maintainability (coupling, testability)

Output: Code review report with:
- Files reviewed count
- Issues by severity (CRITICAL, HIGH, MEDIUM, LOW)
- Specific file:line locations
- Fix recommendations
- Approval recommendation (APPROVE / REQUEST CHANGES / COMMENT)"
)

External Model Consultation (Preferred)

The code-reviewer agent SHOULD consult Codex for cross-validation.

Protocol

  1. Form your OWN review FIRST - Complete the review independently
  2. Consult for validation - Cross-check findings with Codex
  3. Critically evaluate - Never blindly adopt external findings
  4. Graceful fallback - Never block if tools unavailable

When to Consult

  • Security-sensitive code changes
  • Complex architectural patterns
  • Unfamiliar codebases or languages
  • High-stakes production code

When to Skip

  • Simple refactoring
  • Well-understood patterns
  • Time-critical reviews
  • Small, isolated changes

Tool Usage

Before first MCP tool use, call ToolSearch("mcp") to discover deferred MCP tools. Use mcp__x__ask_codex with agent_role: "code-reviewer". If ToolSearch finds no MCP tools, fall back to the code-reviewer agent.

Note: Codex calls can take up to 1 hour. Consider the review timeline before consulting.

Output Format

CODE REVIEW REPORT
==================

Files Reviewed: 8
Total Issues: 15

CRITICAL (0)
-----------
(none)

HIGH (3)
--------
1. src/api/auth.ts:42
   Issue: User input not sanitized before SQL query
   Risk: SQL injection vulnerability
   Fix: Use parameterized queries or ORM

2. src/components/UserProfile.tsx:89
   Issue: Password displayed in plain text in logs
   Risk: Credential exposure
   Fix: Remove password from log statements

3. src/utils/validation.ts:15
   Issue: Email regex allows invalid formats
   Risk: Accepts malformed emails
   Fix: Use proven email validation library

MEDIUM (7)
----------
...

LOW (5)
-------
...

RECOMMENDATION: REQUEST CHANGES

Critical security issues must be addressed before merge.

Review Checklist

The code-reviewer agent checks:

Security

  • No hardcoded secrets (API keys, passwords, tokens)
  • All user inputs sanitized
  • SQL/NoSQL injection prevention
  • XSS prevention (escaped outputs)
  • CSRF protection on state-changing operations
  • Authentication/authorization properly enforced

Code Quality

  • Functions < 50 lines (guideline)
  • Cyclomatic complexity < 10
  • No deeply nested code (> 4 levels)
  • No duplicate logic (DRY principle)
  • Clear, descriptive naming

Performance

  • No N+1 query patterns
  • Appropriate caching where applicable
  • Efficient algorithms (avoid O(n²) when O(n) possible)
  • No unnecessary re-renders (React/Vue)

Best Practices

  • Error handling present and appropriate
  • Logging at appropriate levels
  • Documentation for public APIs
  • Tests for critical paths
  • No commented-out code

Approval Criteria

APPROVE - No CRITICAL or HIGH issues, minor improvements only REQUEST CHANGES - CRITICAL or HIGH issues present COMMENT - Only LOW/MEDIUM issues, no blocking concerns

Scenario Examples

Good: The user says continue after the workflow already has a clear next step. Continue the current branch of work instead of restarting or re-asking the same question.

Good: The user changes only the output shape or downstream delivery step (for example make a PR). Preserve earlier non-conflicting workflow constraints and apply the update locally.

Bad: The user says continue, and the workflow restarts discovery or stops before the missing verification/evidence is gathered.

Use with Other Skills

With Team:

/team "review recent auth changes and report findings"

Includes coordinated review execution across specialized agents.

With Ralph:

/ralph code-review then fix all issues

Review code, get feedback, fix until approved.

With Ultrawork:

/ultrawork review all files in src/

Parallel code review across multiple files.

Best Practices

  • Review early - Catch issues before they compound
  • Review often - Small, frequent reviews better than huge ones
  • Address CRITICAL/HIGH first - Fix security and bugs immediately
  • Consider context - Some "issues" may be intentional trade-offs
  • Learn from reviews - Use feedback to improve coding practices

Frequently asked questions

What does the Code Review AI skill do?

Run a comprehensive code review

Why use Code Review on TypingMind?

Because you install it once and use it with any model. Code Review 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 Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/IAPro-Community/Orquestrador-Maestro/tree/v1/codex/skills/code-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Code Review?

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 Review?

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

Is the Code Review AI skill free?

Yes. It is published on GitHub by IAPro-Community under the Apache-2.0 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.

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