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Structured Code Review

CommunityPopular
rohitg00
structured-code-review

Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".

Overview

Publisherrohitg00
Repositoryskillkit
Skill namestructured-code-review
Stars
1.5K
Forks
147
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Structured 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/rohitg00/skillkit.git /tmp/skillkit
mkdir -p .claude/skills
cp -r /tmp/skillkit/packages/core/src/methodology/packs/collaboration/structured-review .claude/skills/structured-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Structured 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 Structured 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 Structured 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.

Structured Code Review

You are performing a structured, multi-stage code review. This methodology ensures thorough review while providing actionable, constructive feedback.

Core Principle

Review in stages. Each stage has a specific focus. Don't mix concerns.

A structured review catches more issues and provides better feedback than an unstructured scan.

Review Stages

Stage 1: Requirements Compliance

First, verify the code meets its requirements.

Checklist:

  • Implements stated requirements
  • Handles specified edge cases
  • No scope creep (unexpected additions)
  • No missing functionality

Feedback at this stage:

  • "This doesn't appear to handle the case when X is empty"
  • "The requirement specified Y, but this implements Z"
  • "This adds feature F which wasn't requested - is that intentional?"

Stage 2: Correctness

Next, verify the code works correctly.

Checklist:

  • Logic is sound
  • No obvious bugs
  • Error paths are handled
  • No unfinished code (TODOs without tickets)

Feedback at this stage:

  • "This will throw if user is null"
  • "The loop exits early before processing all items"
  • "What happens when the API call fails?"

Stage 3: Code Quality

Then, evaluate code quality and maintainability.

Checklist:

  • Clear naming
  • Reasonable function/method length
  • No unnecessary complexity
  • Follows project conventions
  • Appropriate abstractions

Feedback at this stage:

  • "Could you rename data to userProfile for clarity?"
  • "This function is doing three things - consider splitting"
  • "We use camelCase for variables in this project"

Stage 4: Testing

Evaluate test coverage and quality.

Checklist:

  • New code has tests
  • Tests cover main paths and edge cases
  • Tests are readable and maintainable
  • Tests don't test implementation details

Feedback at this stage:

  • "Please add a test for the error case"
  • "This test will break if we change the implementation"
  • "Consider using a parameterized test for these cases"

Stage 5: Security & Performance

Finally, check for security and performance concerns.

Checklist:

  • No SQL injection, XSS, etc.
  • Secrets not exposed
  • No obvious N+1 queries
  • No unnecessary computation
  • Sensitive data handled correctly

Feedback at this stage:

  • "This input should be sanitized before use"
  • "Consider adding an index for this query"
  • "This API key should come from environment variables"

Writing Good Feedback

Feedback Levels

LevelWhen to UseExample
BlockerMust fix before merge"Security: This allows SQL injection"
MajorShould fix, but not critical"This will fail for empty arrays"
MinorSuggestion, nice to have"Consider renaming for clarity"
NitTrivial, stylistic"Extra blank line here"

Constructive Feedback Template

[Level] [Category]: [Issue]

**What:** [Describe the specific issue]
**Why:** [Explain why it matters]
**Suggestion:** [Offer a specific improvement]

Example:

[Major] Correctness: Null reference possible

**What:** `user.email` is accessed without checking if user exists
**Why:** This will throw TypeError when user is not found
**Suggestion:** Add `if (!user) return null;` before accessing properties

Review Checklist Summary

markdown
## Review: [PR Title]

### Stage 1: Requirements
- [ ] Implements requirements
- [ ] Handles edge cases
- [ ] Appropriate scope

### Stage 2: Correctness
- [ ] Logic is sound
- [ ] No bugs
- [ ] Errors handled

### Stage 3: Quality
- [ ] Readable
- [ ] Follows conventions
- [ ] Maintainable

### Stage 4: Testing
- [ ] Has tests
- [ ] Tests are good

### Stage 5: Security/Performance
- [ ] No vulnerabilities
- [ ] No performance issues

### Verdict: [ ] Approve [ ] Request Changes [ ] Comment

Integration with Other Skills

  • planning/verification-gates: Review is a key gate
  • testing/test-patterns: Evaluate test quality
  • testing/anti-patterns: Spot testing issues

Frequently asked questions

What does the Structured Code Review AI skill do?

Performs a structured five-stage code review covering requirements compliance, correctness, code quality, testing, and security/performance. Each stage uses targeted checklists and categorized feedback (Blocker/Major/Minor/Nit) with actionable suggestions and rationale. Use when the user asks for code review, PR feedback, pull request review, or wants their code checked for bugs, style issues, or vulnerabilities — triggered by phrases like "review my code", "check this PR", "review my changes", "pull request review", or "code feedback".

Why use Structured Code Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/skillkit/tree/main/packages/core/src/methodology/packs/collaboration/structured-review. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Structured 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 Structured Code Review?

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

Is the Structured Code Review AI skill free?

Yes. It is published on GitHub by rohitg00 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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