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Pull Request Feedback Loop

Community
ArchieIndian
pull-request-feedback-loop

Handles PR review feedback by fetching comments, grouping issues, fixing one group at a time, and verifying before replies.

Overview

PublisherArchieIndian
Repositoryopenclaw-superpowers
Skill namepull-request-feedback-loop
Stars
74
Forks
14
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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 ArchieIndian on GitHub. Read the source before you install it.

Installation

Install the Pull Request Feedback Loop 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/ArchieIndian/openclaw-superpowers/tree/main/skills/core/pull-request-feedback-loop
  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/ArchieIndian/openclaw-superpowers.git /tmp/openclaw-superpowers
mkdir -p .claude/skills
cp -r /tmp/openclaw-superpowers/skills/core/pull-request-feedback-loop .claude/skills/pull-request-feedback-loop
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pull Request Feedback Loop 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 Pull Request Feedback Loop 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 Pull Request Feedback Loop 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.

Pull Request Feedback Loop

PR feedback is a queue of review obligations, not a pile of comments. Use this skill when a user asks you to address PR comments, requested changes, failed checks tied to review feedback, or stale review threads.

When to Use

  • A PR has unresolved review comments or requested changes
  • The user asks you to "address review feedback" or "fix PR comments"
  • You need to reconcile code changes, replies, and verification

Do not use this for ordinary local refactors with no review thread.

Process

  1. Identify the PR.

    • Prefer the PR number or URL from the user.
    • If missing, run gh pr status or gh pr view --json number,url,headRefName.

  2. Fetch review context.

    • Run gh pr view <pr> --comments.
    • If needed, run gh api for review threads or inline comments.
    • Capture author, file, line, status, and requested change.

  3. Group comments by issue.

    • Merge duplicate comments that point at the same underlying fix.
    • Separate behavior bugs, test gaps, naming/docs cleanup, and questions.
    • Mark comments that need clarification before code changes.

  4. Fix one group at a time.

    • Inspect the referenced code before editing.
    • Make the smallest coherent change for that group.
    • Avoid bundling unrelated cleanup into review fixes.

  5. Verify before replying.

    • Run the narrowest relevant test or check for each group.
    • If no test can run, explain the exact reason and inspect manually.
    • Do not claim a thread is resolved until verification passes or is explicitly waived.

  6. Prepare replies.

    • Summarize what changed and how it was verified.
    • Keep replies short and factual.
    • Do not submit replies automatically unless the user asked you to.

Output

Report:

  • Groups handled
  • Files changed
  • Verification run
  • Threads ready for reply
  • Threads needing user or reviewer clarification

Guardrails

  • Do not force-push, delete branches, or dismiss reviews without explicit permission.
  • Do not mark comments resolved only because code was edited.
  • Do not ignore a comment because it looks small; either address it or explain why it is not applicable.

Frequently asked questions

What does the Pull Request Feedback Loop AI skill do?

Handles PR review feedback by fetching comments, grouping issues, fixing one group at a time, and verifying before replies.

Why use Pull Request Feedback Loop on TypingMind?

Because you install it once and use it with any model. Pull Request Feedback Loop 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 Pull Request Feedback Loop in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ArchieIndian/openclaw-superpowers/tree/main/skills/core/pull-request-feedback-loop. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pull Request Feedback Loop?

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 Pull Request Feedback Loop?

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

Is the Pull Request Feedback Loop AI skill free?

It is published on GitHub by ArchieIndian. Check the repository for licensing terms. 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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