Engineering Review Comments logo

Engineering Review Comments

Organization
agentara
engineering-review-comments

Use when drafting, rewriting, classifying, or improving code review comments as a reviewer. TRIGGER on rough review notes, requests to make comments clearer or kinder, severity labels, "how should I phrase this review comment?", "write review comments", author pushback, or turning findings into PR comments. If role is unclear, ask reviewer or author. DO NOT TRIGGER for full code review, PR descriptions, or author responses unless wording is requested.

Overview

Publisheragentara
Repositoryskills
Skill nameengineering-review-comments
Stars
605
Forks
43
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 agentara on GitHub. Read the source before you install it.

Installation

Install the Engineering Review Comments 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/agentara/skills/tree/main/skills/engineering/engineering-review-comments
  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/agentara/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/engineering/engineering-review-comments .claude/skills/engineering-review-comments
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Engineering Review Comments 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 Comments 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 Comments 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 Comments

Review comments should improve the code and the developer's future judgment. They should be clear about severity, grounded in engineering reasoning, and directed at the code rather than the person.

Adapted from Google Engineering Practices Documentation, especially "How to write code review comments" and "Handling pushback in code reviews." Source: https://google.github.io/eng-practices/review/reviewer/comments.html. License: CC-BY 3.0.

Comment Shape

Use this structure for non-trivial comments:

text
SEVERITY: Observation about the code.

Why this matters: engineering reason, user impact, maintainability impact, or violated project rule.

Requested action: concrete fix, question, or decision the author needs to make.

Keep tiny comments tiny. Use the full shape when intent could be misunderstood.

Severity Labels

  • BLOCKER: must change before approval because correctness, maintainability, security, data, test, or operational risk is unacceptable.
  • IMPORTANT: should change before approval; can be waived only with an explicit rationale.
  • QUESTION: reviewer needs information before deciding.
  • NIT: minor polish. Do not block approval.
  • OPTIONAL or CONSIDER: a suggestion the author can decline.
  • FYI: educational context or future consideration; no action expected.

Writing Rules

  • Comment on the code, design, tests, or behavior; do not characterize the author.
  • Explain why when asking for anything beyond obvious style or mechanical cleanup.
  • Prefer "this makes X harder because..." over "I don't like X."
  • Ask for simpler code when a review-thread explanation is needed to understand ordinary behavior.
  • Accept a code comment only when the code cannot reasonably express the why by itself.
  • Provide direct code or a specific design only when it is faster or safer than making the author infer it.
  • Otherwise describe the problem and let the author choose the implementation.
  • Point out good engineering choices when useful, and say why they are good.

Wording Patterns

Use these patterns directly when helpful:

text
BLOCKER: This path can return success before the write is durable.

If the process crashes after this point, callers will believe the operation completed even though the data may be missing. Please either move the success response after the durable write or make the operation explicitly asynchronous and observable.
text
QUESTION: What guarantees that this callback cannot run concurrently?

If it can run in parallel, `pending` can be decremented twice from the same value. If the framework serializes this callback, please add a short comment at the boundary where that guarantee comes from.
text
NIT: This helper name reads as if it validates the payload, but it only normalizes whitespace. Could we rename it to `normalizePayloadText`?
text
OPTIONAL: Consider extracting this branch into a named helper. The current version is correct, but a helper would make the three policy cases easier to scan.

Handling Pushback

When the author disagrees:

  1. Re-evaluate their argument first. They may be closer to the code or have context you missed.
  2. If they are right, say so plainly and resolve the comment.
  3. If you still disagree, restate their point, then explain the code-health reason for your request.
  4. Distinguish mandatory requests from preferences.
  5. If the thread stops making progress, move to a higher-bandwidth discussion and summarize the decision back in the review.

Do not keep arguing because you wrote the first comment. The goal is the best code, not defending the initial wording.

Common Mistakes

  • Leaving unlabeled comments that authors interpret as mandatory.
  • Writing a correct critique in a tone that makes collaboration harder.
  • Asking the author to explain confusing code only in the review tool.
  • Turning every comment into a complete design assignment.
  • Treating educational notes as blockers.

Frequently asked questions

What does the Engineering Review Comments AI skill do?

Use when drafting, rewriting, classifying, or improving code review comments as a reviewer. TRIGGER on rough review notes, requests to make comments clearer or kinder, severity labels, "how should I phrase this review comment?", "write review comments", author pushback, or turning findings into PR comments. If role is unclear, ask reviewer or author. DO NOT TRIGGER for full code review, PR descriptions, or author responses unless wording is requested.

Why use Engineering Review Comments on TypingMind?

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

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

Which AI models can use Engineering Review Comments?

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 Comments?

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

Is the Engineering Review Comments AI skill free?

Yes. It is published on GitHub by agentara 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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