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Code Review

Community
Houseofmvps
code-review

Code review with principal-engineer-level depth. Reviews for correctness, performance, security, maintainability, and architecture. Use when completing tasks, reviewing PRs, or before merging.

Overview

PublisherHouseofmvps
Repositoryultraship
Skill namecode-review
Stars
122
Forks
14
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 Houseofmvps 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.
  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/Houseofmvps/ultraship.git /tmp/ultraship
mkdir -p .claude/skills
cp -r /tmp/ultraship/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

Review code the way a principal engineer would — not just "does it work?" but "will this cause problems at 3am?"

Review Dimensions

Every review should evaluate these dimensions, in order of importance:

Use an LSP if one is connected. If LSP tools are available (check your tools for a language server — TypeScript, Pyright, gopls, rust-analyzer), use them instead of grep-guessing. find references on a changed function tells you the real blast radius; go to definition confirms a call signature actually matches; rename/diagnostics surface type errors the eye misses. A review that verifies call sites with an LSP catches breakage that a text-only review ships. If no LSP is connected, fall back to Grep/Glob and say so.

1. Correctness

The code must do what it claims to do.

  • Does the logic match the requirements/spec?
  • Are edge cases handled? (empty input, null, max values, concurrent access)
  • Are error paths tested, not just happy paths?
  • Does it handle the "what if this is called twice?" scenario?
  • Are race conditions possible? (async operations, shared state, database transactions)

2. Security

Think like an attacker for every piece of new code.

  • Input validation: Is user input validated before use? (URL params, request body, query strings)
  • IDOR: Can User A access User B's data by changing an ID? (check every route with :id params)
  • Injection: Is user input ever interpolated into SQL, shell commands, or HTML?
  • Auth: Are new endpoints protected by auth middleware? Are permissions checked, not just authentication?
  • Secrets: Are any credentials hardcoded? Any new env vars documented?
  • Data exposure: Do API responses leak internal fields? (password hashes, internal IDs, other users' data)

3. Performance

Will this work at 10x the current load?

  • N+1 queries: Database calls inside loops. The #1 performance killer in web apps.
  • Missing indexes: New columns used in WHERE/JOIN without index.
  • Unbounded queries: findMany() without take/limit. Will return 1M rows when the table grows.
  • Sync I/O: readFileSync, execSync in request handlers. Blocks the event loop.
  • Sequential awaits: Independent awaits that should be Promise.all().
  • Memory leaks: Module-scoped arrays with .push(), event listeners added in request handlers.
  • Over-fetching: Selecting all columns when only 2 are needed. Returning full objects when IDs suffice.

4. Maintainability

Will the next person (including future-you) understand this in 6 months?

  • Naming: Do variable/function names describe what they do, not how they do it?
  • Complexity: Can any function be broken into smaller, testable pieces?
  • Abstraction level: Is the code at a consistent level of abstraction? (mixing HTTP parsing with business logic is a smell)
  • DRY violations: Is the same logic duplicated in multiple places?
  • Dead code: Are there unused functions, imports, or variables?
  • Comments: Are they explaining "why," not "what"? Comments that restate the code are noise.

5. Architecture

Does this fit the existing patterns, or does it introduce divergence?

  • Pattern consistency: Does the new code follow the patterns established in the codebase?
  • Coupling: Does this create tight coupling between modules that should be independent?
  • Layer violations: Is a UI component making direct database calls? Is an API route doing business logic inline?
  • Interface design: Are the function signatures clean? Could the API be simpler?

Confidence Scoring

Every finding should include a confidence level:

ConfidenceMeaningAction
HighThis is almost certainly a real issueFix before merging
MediumThis looks like an issue but context might make it fineInvestigate, fix if confirmed
LowThis is a style preference or minor concernNote for later, don't block merge

Don't cry wolf. A review that flags 30 "high" issues when only 3 are real trains the developer to ignore reviews. Be precise.

Output Format for /ship

When invoked by /ship, output findings with severity levels (critical/high/medium/low/info) in the same format as other auditors:

json
{
  "category": "code-quality",
  "findings": [
    { "severity": "high", "category": "code-quality", "file": "path", "line": N, "message": "description" }
  ]
}

Review Checklist (use mentally, don't output)

  • Every new function has tests
  • Every new route has auth middleware (if the app has auth)
  • Every database query has appropriate indexes
  • Every user input is validated
  • No secrets in code
  • No console.logs left in production code
  • Error handling returns appropriate status codes
  • API responses don't leak internal fields
  • New dependencies are justified (not just convenience)
  • The change is reversible (can be rolled back without data loss)

Key Principles

  • Review the change, not the file. Focus on what's new or modified. Don't nit-pick pre-existing code unless it's directly related to the change.
  • Offer fixes, not just complaints. "This has an N+1 query" is unhelpful. "This has an N+1 query — move the query outside the loop and pass the results as a lookup map" is a review.
  • Distinguish between blocking and non-blocking. Be explicit: "This must be fixed before merge" vs. "This is a suggestion for a follow-up PR."
  • Assume good intent. The developer made the best choice they could with the information they had. Your job is to add information, not judgment.

Frequently asked questions

What does the Code Review AI skill do?

Code review with principal-engineer-level depth. Reviews for correctness, performance, security, maintainability, and architecture. Use when completing tasks, reviewing PRs, or before merging.

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/Houseofmvps/ultraship/tree/main/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 Houseofmvps 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.

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