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Code Review

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DanielKerridge
code-review

Deep code audit that finds dead wiring, silent failures, unfinished features, placeholder stubs, bloated files, and unnecessary complexity. Produces an actionable report with file:line references grouped by severity. Think of it as a senior dev doing a thorough PR review of the entire codebase. Triggers on: "code review", "audit the code", "review the code", "find dead code", "find placeholders", "check for stubs", "prune the code", "code cleanup", "implementation review", "completeness check", "find unused code".

Overview

PublisherDanielKerridge
Repositoryclaude-code-power-platform-skills
Skill namecode-review
Stars
63
Forks
16
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by DanielKerridge 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/DanielKerridge/claude-code-power-platform-skills.git /tmp/claude-code-power-platform-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-power-platform-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 — Deep Codebase Audit Skill

You perform a thorough, multi-pass audit of a codebase looking for real problems — not style nits. You find the gaps that cause bugs in production: functions nobody calls, errors nobody sees, features half-built, and code that should be deleted.

CRITICAL RULES

  1. Every finding must include file:line references. No vague "somewhere in the code" findings.
  2. Categorize by severity. CRITICAL > WARNING > PRUNE > INFO. Read resources/severity-guide.md.
  3. Run ALL passes. Don't skip passes because early ones found nothing. Read resources/audit-passes.md.
  4. Never suggest adding code without showing what to remove. This is a pruning exercise, not a feature request.
  5. Focus on real bugs, not style. Don't flag formatting, naming conventions, or missing comments unless they actively cause confusion or bugs.
  6. Provide the fix, not just the finding. Each finding should say what to do about it.

Audit Architecture

The review runs 7 passes over the codebase. Each pass looks for a different class of problem. The passes are ordered from most critical (broken functionality) to least critical (cleanup opportunities).

Pass 1: WIRING          — Is everything connected end-to-end?
Pass 2: ERROR HANDLING   — Can failures be seen and debugged?
Pass 3: COMPLETENESS     — Are features fully implemented?
Pass 4: DEAD CODE        — What can be deleted right now?
Pass 5: BLOAT            — What's too big, too complex, or redundant?
Pass 6: HARDCODING       — What should be configurable but isn't?
Pass 7: SECURITY         — Any obvious vulnerabilities?

Read resources/audit-passes.md for the detailed checklist for each pass.

Workflow

Phase 1 — Scope the Review

Before auditing, understand the codebase:

  1. What's the project? Read README, CLAUDE.md, package.json, etc.
  2. What's the tech stack? Framework, language, build tools
  3. What's the architecture? Entry points, services, stores, components
  4. What was recently changed? If there's git history, focus on recent additions

Build a mental map of the codebase:

  • Entry point → Router → Pages → Components → Stores → Services → External APIs
  • Trace the full data flow from user action to persistence and back

Phase 2 — Run the 7 Audit Passes

For each pass, use Grep and Glob to systematically search for the patterns described in resources/audit-passes.md.

Use parallel agents when the codebase is large. Spawn agents for independent passes:

  • Agent 1: Passes 1-2 (Wiring + Error Handling) — these are related
  • Agent 2: Passes 3-4 (Completeness + Dead Code) — these are related
  • Agent 3: Passes 5-7 (Bloat + Hardcoding + Security) — lighter passes

Phase 3 — Cross-Reference

After individual passes, cross-reference findings:

  • Does a "dead code" finding explain a "wiring" gap? (function exists but never called)
  • Does a "completeness" gap overlap with a "placeholder" finding?
  • Deduplicate — one root cause might show up in multiple passes

Phase 4 — Compile the Report

Output format (read resources/report-format.md):

markdown
# Code Review Report — [Project Name]
Date: [date]
Files Scanned: [count]
Findings: [count] (X critical, Y warning, Z prune, W info)

## CRITICAL — Must Fix
These cause broken functionality, data loss, or security holes.

### CR-001: [Title]
**File:** `src/stores/game-store.ts:108`
**Pass:** Wiring
**Problem:** `submitGameSession()` is defined in dataverse.ts but never called.
Game results are never persisted to Dataverse.
**Fix:** Call `submitGameSession()` from the `endGame()` action in game-store.ts.

## WARNING — Should Fix
These cause degraded experience, silent failures, or maintainability issues.

## PRUNE — Consider Removing
Dead code, redundant logic, bloated files. Removing these makes the codebase
leaner and easier to maintain.

## INFO — Minor Observations
Nice-to-know items that don't require action.

Phase 5 — Pruning Recommendations

After the main audit, generate a pruning plan. Read resources/pruning-guide.md.

The pruning plan should:

  1. List files/functions/types that can be safely deleted
  2. List files that should be split (too many responsibilities)
  3. List abstractions that should be inlined (used only once)
  4. List dependencies that can be removed from package.json
  5. Estimate the total lines of code that would be removed

Without Agent Teams

If running as a single agent, execute passes sequentially. Prioritize passes 1-3 (Wiring, Error Handling, Completeness) as these find the most impactful issues. Passes 4-7 are still valuable but can be deferred if time-constrained.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Code Review AI skill do?

Deep code audit that finds dead wiring, silent failures, unfinished features, placeholder stubs, bloated files, and unnecessary complexity. Produces an actionable report with file:line references grouped by severity. Think of it as a senior dev doing a thorough PR review of the entire codebase. Triggers on: "code review", "audit the code", "review the code", "find dead code", "find placeholders", "check for stubs", "prune the code", "code cleanup", "implementation review", "completeness check", "find unused code".

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/DanielKerridge/claude-code-power-platform-skills/tree/master/code-review. TypingMind reads its SKILL.md and bundles its files 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 DanielKerridge 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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