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Superpowers Receiving Code Review

Community
christopherarter
superpowers-receiving-code-review

Got review feedback? Load BEFORE changing anything — verify each point, push back if wrong.

Overview

Publisherchristopherarter
Repositorysuperpowers-reasonix
Skill namesuperpowers-receiving-code-review
Stars
101
Forks
7
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 christopherarter on GitHub. Read the source before you install it.

Installation

Install the Superpowers Receiving 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.
    https://github.com/christopherarter/superpowers-reasonix/tree/main/skills/superpowers-receiving-code-review
  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/christopherarter/superpowers-reasonix.git /tmp/superpowers-reasonix
mkdir -p .claude/skills
cp -r /tmp/superpowers-reasonix/skills/superpowers-receiving-code-review .claude/skills/superpowers-receiving-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Superpowers Receiving 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 Superpowers Receiving 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 Superpowers Receiving 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 Reception

Overview

Code review = technical evaluation. Not emotional performance.

Core principle: Verify before implementing. Ask before assuming. Technical correctness beats social comfort.

The Response Pattern

WHEN receiving code review feedback:

1. READ: Full feedback, no reacting
2. UNDERSTAND: Restate requirement in own words (or ask)
3. VERIFY: Check against codebase reality
4. EVALUATE: Technically sound for THIS codebase?
5. RESPOND: Technical ack or reasoned pushback
6. IMPLEMENT: One item at a time, test each

Forbidden Responses

NEVER:

  • "You're absolutely right!" (performative agreement)
  • "Great point!" / "Excellent feedback!" (performative)
  • "Let me implement that now" (before verification)

INSTEAD: restate technical requirement; ask clarifying questions; push back with technical reasoning if wrong; or just start working (actions > words).

Handling Unclear Feedback

IF any item unclear:
  STOP - implement nothing yet
  ASK for clarification on unclear items

WHY: Items may be related. Partial understanding = wrong implementation.

Example:

Partner: "Fix 1-6"
You understand 1,2,3,6. Unclear on 4,5.

❌ WRONG: Implement 1,2,3,6 now, ask 4,5 later
✅ RIGHT: "Understand items 1,2,3,6. Need clarification on 4 and 5 before proceeding."

Source-Specific Handling

From your human partner

  • Trusted — implement after understanding
  • Still ask if scope unclear
  • No performative agreement — skip to action or technical ack

From External Reviewers (or a reviewer subagent)

BEFORE implementing:
  1. Technically correct for THIS codebase?
  2. Breaks existing functionality?
  3. Reason for the current implementation?
  4. Works on all platforms/versions?
  5. Does the reviewer understand full context?

IF suggestion seems wrong: push back with technical reasoning
IF can't easily verify: "I can't verify this without [X]. Should I [investigate/ask/proceed]?"
IF conflicts with your human partner's prior decisions: stop and discuss first

Rule: External feedback — stay skeptical. Check carefully.

YAGNI Check for "Professional" Features

IF reviewer suggests "implementing properly":
  grep codebase for actual usage
  IF unused: "Endpoint isn't called. Remove it (YAGNI)?"
  IF used: implement properly

Implementation Order

FOR multi-item feedback:
  1. Clarify anything unclear FIRST
  2. Then implement in order:
     - Blocking issues (breaks, security)
     - Simple fixes (typos, imports)
     - Complex fixes (refactoring, logic)
  3. Test each fix individually
  4. Verify no regressions

When To Push Back

Push back when suggestion: breaks existing functionality; comes from reviewer lacking full context; violates YAGNI (unused feature); is technically incorrect for this stack; ignores legacy/compatibility reasons; conflicts with your human partner's architectural decisions.

How: technical reasoning, not defensiveness; ask specific questions; reference working tests/code; involve your human partner if architectural.

Acknowledging Correct Feedback

✅ "Fixed. [what changed]"
✅ "Good catch - [specific issue]. Fixed in [location]."
✅ [Just fix it; show it in code]

❌ "You're absolutely right!"  ❌ "Great point!"  ❌ "Thanks for catching that!"
❌ ANY gratitude expression

Why no thanks: actions speak. Just fix it — code shows you heard. Catch yourself writing "Thanks"? Delete it. State the fix.

Gracefully Correcting Your Pushback

Pushed back, but wrong:

✅ "You were right - checked [X], it does [Y]. Implementing now."
✅ "Verified, you're correct. My understanding was wrong because [reason]. Fixing."

❌ Long apology   ❌ Defending why you pushed back   ❌ Over-explaining

State correction factually. Move on.

Common Mistakes

MistakeFix
Performative agreementState requirement or just act
Blind implementationVerify against codebase first
Batch without testingOne at a time, test each
Assuming reviewer is rightCheck if it breaks things
Avoiding pushbackTechnical correctness > comfort
Partial implementationClarify all items first
Can't verify, proceed anywayState limitation, ask for direction

GitHub Thread Replies

Replying to inline GitHub review comments? Reply in the thread (gh api repos/{owner}/{repo}/pulls/{pr}/comments/{id}/replies), not as a top-level PR comment.

The Bottom Line

External feedback = suggestions to evaluate, not orders to follow.

Evaluate each item against the actual code before acting. Push back on what's wrong. No performative agreement. Technical rigor always.

Frequently asked questions

What does the Superpowers Receiving Code Review AI skill do?

Got review feedback? Load BEFORE changing anything — verify each point, push back if wrong.

Why use Superpowers Receiving Code Review on TypingMind?

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

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

Which AI models can use Superpowers Receiving 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 Superpowers Receiving Code Review?

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

Is the Superpowers Receiving Code Review AI skill free?

Yes. It is published on GitHub by christopherarter 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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