Engineering Review Feedback logo

Engineering Review Feedback

Organization
MiniMax-AI
engineering-review-feedback

Use when handling code review feedback as the change author, addressing reviewer comments, drafting replies, resolving disagreements, or summarizing updates. TRIGGER on "address review feedback", "respond to this review comment", "reviewer asked", "I disagree with reviewer", "what should I reply?", or "apply PR comments". If role is unclear, ask reviewer or author. DO NOT TRIGGER for reviewer comment writing, full code review, or PR descriptions unless author-response behavior is requested.

Overview

PublisherMiniMax-AI
Repositorymcode-stack
Skill nameengineering-review-feedback
Stars
464
Forks
36
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 MiniMax-AI on GitHub. Read the source before you install it.

Installation

Install the Engineering Review Feedback 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/MiniMax-AI/mcode-stack/tree/main/stacks/ronny/skills/engineering-review-feedback
  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/MiniMax-AI/mcode-stack.git /tmp/mcode-stack
mkdir -p .claude/skills
cp -r /tmp/mcode-stack/stacks/ronny/skills/engineering-review-feedback .claude/skills/engineering-review-feedback
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Engineering Review Feedback 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 Engineering Review Feedback 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 Engineering Review Feedback 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.

Engineering Review Feedback

Handle reviewer feedback as collaboration toward better code. The best response often changes the code, tests, or comments so future readers benefit, rather than explaining only inside the review thread.

Adapted from Google Engineering Practices Documentation, especially "How to handle reviewer comments." Source: https://google.github.io/eng-practices/review/developer/handling-comments.html. License: CC-BY 3.0.

Response Workflow

  1. Pause before responding if the comment feels frustrating.
  2. Identify what the reviewer is asking for.
  3. Classify each comment:
    • Code change required.
    • Test change required.
    • Documentation or comment change required.
    • Clarification needed.
    • Disagreement to discuss.
    • Non-blocking suggestion.
  4. Prefer improving the artifact over explaining in the review tool.
  5. Run the relevant verification.
  6. Reply with what changed, why, and how it was tested.

If a reviewer says they do not understand the code, first try to make the code clearer. Add a code comment only when the why cannot be expressed cleanly in code.

When You Agree

Use a short response and make the change.

text
Done. I renamed the helper to describe normalization rather than validation and updated the two call sites.

Tested with: npm test -- user-profile.test.ts

When You Need Clarification

Ask for the missing decision or constraint. Avoid defensive wording.

text
I want to make sure I understand the concern. Are you asking for this to reject duplicate requests at the API boundary, or for the worker to make duplicate processing idempotent?

When You Disagree

Disagree by comparing tradeoffs, not by rejecting the person.

text
I chose X because it keeps the retry policy in one place and avoids a second queue state. My concern with Y is that it makes cancellation observable in two different layers.

If you think Y better serves the rollback requirement, I can switch to it. Is that the tradeoff you want prioritized?

If the reviewer provides better technical reasoning, accept it and adjust. If neither side can converge, move to a higher-bandwidth conversation and summarize the decision back in the review.

When Feedback Is Not Constructive

Do not reply in kind. Extract any technical concern that can be addressed in code, tests, or docs. If the comment is hostile, vague, or repeatedly unproductive, move to a private or higher-bandwidth conversation, involve a lead when needed, and summarize only the technical decision back in the review.

Fix the Right Place

  • Confusing expression: simplify or rename it.
  • Hidden invariant: encode it in types, validation, assertions, or a focused comment.
  • Missing behavior confidence: add or improve tests.
  • Missing context: update the PR description, code comment, README, or design doc.
  • Reviewer misunderstanding caused by stale diff: rebase, remove dead code, or update the description.
  • Pure preference: ask whether it is required; otherwise treat as optional.

Summary Reply Template

markdown
Addressed this round:
- Changed ...
- Added/updated tests for ...
- Clarified ...

Verification:
- `...`

Open:
- ...

Common Mistakes

  • Replying in anger or with sarcasm.
  • Explaining confusing code only in the review thread.
  • Treating every reviewer suggestion as mandatory without clarifying severity.
  • Saying "will clean up later" when the current change introduces the complexity.
  • Making broad unrelated refactors while addressing a narrow review comment.

Also published in agentara/skills (MIT, Copyright (c) 2026 agentara) by the same author. The content is adapted from Google Engineering Practices under CC-BY 3.0; see the source note above. Full notices: NOTICE.md.

Frequently asked questions

What does the Engineering Review Feedback AI skill do?

Use when handling code review feedback as the change author, addressing reviewer comments, drafting replies, resolving disagreements, or summarizing updates. TRIGGER on "address review feedback", "respond to this review comment", "reviewer asked", "I disagree with reviewer", "what should I reply?", or "apply PR comments". If role is unclear, ask reviewer or author. DO NOT TRIGGER for reviewer comment writing, full code review, or PR descriptions unless author-response behavior is requested.

Why use Engineering Review Feedback on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/MiniMax-AI/mcode-stack/tree/main/stacks/ronny/skills/engineering-review-feedback. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Engineering Review Feedback?

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 Engineering Review Feedback?

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

Is the Engineering Review Feedback AI skill free?

Yes. It is published on GitHub by MiniMax-AI 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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