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Review Implementing

Community
mhattingpete
review-implementing

Process and implement code review feedback systematically. Use when user provides reviewer comments, PR feedback, code review notes, or asks to implement suggestions from reviews.

Overview

Publishermhattingpete
Repositoryclaude-skills-marketplace
Skill namereview-implementing
Stars
675
Forks
96
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 mhattingpete on GitHub. Read the source before you install it.

Installation

Install the Review Implementing 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/mhattingpete/claude-skills-marketplace.git /tmp/claude-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/claude-skills-marketplace/engineering-workflow-plugin/skills/review-implementing .claude/skills/review-implementing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Implementing 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 Review Implementing 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 Review Implementing 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.

Review Feedback Implementation

Systematically process and implement changes based on code review feedback.

When to Use

  • Provides reviewer comments or feedback
  • Pastes PR review notes
  • Mentions implementing review suggestions
  • Says "address these comments" or "implement feedback"
  • Shares list of changes requested by reviewers

Systematic Workflow

1. Parse Reviewer Notes

Identify individual feedback items:

  • Split numbered lists (1., 2., etc.)
  • Handle bullet points or unnumbered feedback
  • Extract distinct change requests
  • Clarify ambiguous items before starting

2. Create Todo List

Use TodoWrite tool to create actionable tasks:

  • Each feedback item becomes one or more todos
  • Break down complex feedback into smaller tasks
  • Make tasks specific and measurable
  • Mark first task as in_progress before starting

Example:

- Add type hints to extract function
- Fix duplicate tag detection logic
- Update docstring in chain.py
- Add unit test for edge case

3. Implement Changes Systematically

For each todo item:

Locate relevant code:

  • Use Grep to search for functions/classes
  • Use Glob to find files by pattern
  • Read current implementation

Make changes:

  • Use Edit tool for modifications
  • Follow project conventions (CLAUDE.md)
  • Preserve existing functionality unless changing behavior

Verify changes:

  • Check syntax correctness
  • Run relevant tests if applicable
  • Ensure changes address reviewer's intent

Update status:

  • Mark todo as completed immediately after finishing
  • Move to next todo (only one in_progress at a time)

4. Handle Different Feedback Types

Code changes:

  • Use Edit tool for existing code
  • Follow type hint conventions (PEP 604/585)
  • Maintain consistent style

New features:

  • Create new files with Write tool if needed
  • Add corresponding tests
  • Update documentation

Documentation:

  • Update docstrings following project style
  • Modify markdown files as needed
  • Keep explanations concise

Tests:

  • Write tests as functions, not classes
  • Use descriptive names
  • Follow pytest conventions

Refactoring:

  • Preserve functionality
  • Improve code structure
  • Run tests to verify no regressions

5. Validation

After implementing changes:

  • Run affected tests
  • Check for linting errors: uv run ruff check
  • Verify changes don't break existing functionality

6. Communication

Keep user informed:

  • Update todo list in real-time
  • Ask for clarification on ambiguous feedback
  • Report blockers or challenges
  • Summarize changes at completion

Edge Cases

Conflicting feedback:

  • Ask user for guidance
  • Explain conflict clearly

Breaking changes required:

  • Notify user before implementing
  • Discuss impact and alternatives

Tests fail after changes:

  • Fix tests before marking todo complete
  • Ensure all related tests pass

Referenced code doesn't exist:

  • Ask user for clarification
  • Verify understanding before proceeding

Important Guidelines

  • Always use TodoWrite for tracking progress
  • Mark todos completed immediately after each item
  • Only one todo in_progress at any time
  • Don't batch completions - update status in real-time
  • Ask questions for unclear feedback
  • Run tests if changes affect tested code
  • Follow CLAUDE.md conventions for all code changes
  • Use conventional commits if creating commits afterward

Frequently asked questions

What does the Review Implementing AI skill do?

Process and implement code review feedback systematically. Use when user provides reviewer comments, PR feedback, code review notes, or asks to implement suggestions from reviews.

Why use Review Implementing on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mhattingpete/claude-skills-marketplace/tree/main/engineering-workflow-plugin/skills/review-implementing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Implementing?

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 Review Implementing?

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

Is the Review Implementing AI skill free?

Yes. It is published on GitHub by mhattingpete 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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