Pr Comments logo

Pr Comments

Organization
vstorm-co
pr-comments

Use this skill when user asks to address PR review comments. It will fetch all comments, present them in a structured format.

Overview

Publishervstorm-co
Repositoryagenticos
Skill namepr-comments
Stars
137
Forks
27
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 vstorm-co on GitHub. Read the source before you install it.

Installation

Install the Pr 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/vstorm-co/agenticos/tree/main/.claude/skills/pr-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/vstorm-co/agenticos.git /tmp/agenticos
mkdir -p .claude/skills
cp -r /tmp/agenticos/.claude/skills/pr-comments .claude/skills/pr-comments
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pr 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 Pr 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 Pr 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.

Skill: PR Review Comments

Fetch all review comments from a PR, present them in a structured format, then systematically address each one.

Inputs

Determine from conversation context or user's message:

  • PR number or URL — if not given, detect from current branch: gh pr view --json number -q .number
  • Include nits — default: skip. User may say "include nits"
  • Include outdated — default: skip. User may say "include outdated"
  • Include resolved — default: skip. User may say "include resolved"
  • List only — if user just wants to see comments without fixing

Steps

1. Fetch all review threads

IMPORTANT: Use GraphQL, NOT REST. The REST endpoints (pulls/N/comments, pulls/N/reviews) do NOT return isResolved or isOutdated. Only GraphQL has this data.

bash
OWNER=$(gh repo view --json owner -q .owner.login)
REPO=$(gh repo view --json name -q .name)

Be on the PR's own branch before reading a single file. The comments come from the PR number; the edits, the commit and the push go to whatever is checked out. Given a PR number while another branch is out, every fix lands on an unrelated branch and the PR that was asked about is untouched:

bash
HEAD_REF=$(gh pr view NUMBER --json headRefName -q .headRefName)
[ "$(git branch --show-current)" = "$HEAD_REF" ] || gh pr checkout NUMBER

Refuse to continue if the checkout fails or the working tree is dirty - a fix applied over somebody else's uncommitted work is not a fix.

Run this exact query (replace NUMBER with the PR number):

bash
gh api graphql -F owner="$OWNER" -F name="$REPO" -F pr=NUMBER -f query='
query($owner: String!, $name: String!, $pr: Int!) {
  repository(owner: $owner, name: $name) {
    pullRequest(number: $pr) {
      title
      url
      comments(first: 100) {
        nodes {
          body
          author { login }
          createdAt
          url
        }
        pageInfo { hasNextPage endCursor }
      }
      reviews(first: 100) {
        nodes {
          body
          author { login }
          state
          url
        }
        pageInfo { hasNextPage endCursor }
      }
      reviewThreads(first: 100) {
        nodes {
          isResolved
          isOutdated
          path
          line
          startLine
          resolvedBy { login }
          comments(first: 20) {
            nodes {
              body
              originalLine
              author { login }
              createdAt
              replyTo { id }
            }
          }
        }
        pageInfo { hasNextPage endCursor }
      }
    }
  }
}'

Every connection above asks for pageInfo, because first: 100 caps a page rather than proving the connection was read to the end. Paginate each one whose hasNextPage is true by adding its cursor and updating the query signature. Re-use the same nodes selection set as the first query (shown here so the snippet is directly executable). A PR with more than a hundred conversation comments or reviews is rare and exactly the PR this skill exists for, so do not assume one page covers it.

bash
gh api graphql -F owner="$OWNER" -F name="$REPO" -F pr=NUMBER -F after="$CURSOR" -f query='
query($owner: String!, $name: String!, $pr: Int!, $after: String) {
  repository(owner: $owner, name: $name) {
    pullRequest(number: $pr) {
      comments(first: 100) {
        nodes {
          body
          author { login }
          createdAt
          url
        }
      }
      reviews(first: 100) {
        nodes {
          body
          author { login }
          state
          url
        }
      }
      reviewThreads(first: 100, after: $after) {
        nodes {
          isResolved
          isOutdated
          path
          line
          startLine
          resolvedBy { login }
          comments(first: 20) {
            nodes {
              body
              originalLine
              author { login }
              createdAt
              replyTo { id }
            }
          }
        }
        pageInfo { hasNextPage endCursor }
      }
    }
  }
}'

2. Filter threads

ConditionDefaultInclude when
isResolved: trueSkipuser asks for resolved
isOutdated: trueSkipuser asks for outdated
Nit commentSkipuser asks for nits

Nit detection: first comment body starts with nit:, nit -, nitpick (case-insensitive, first 50 chars).

3. Present in unified format

reviewThreads are per-file/per-line, so they group by path. The two other sources are not anchored to the diff, so present each as its own section:

  • Top-level conversation comments — pullRequest.comments.nodes (IssueComments from the Conversation tab).
  • Review summary bodies — pullRequest.reviews.nodes with a non-empty body (the note left alongside Approve / Request changes / Comment). Skip reviews whose body is empty — those carry only inline threads, already covered above.
## PR #<number>: <title>
<url>

**<N> actionable threads** (filtered: <X> resolved, <Y> outdated, <Z> nits)

---

### Inline threads

#### Thread 1 · `<path>`:<line> · @<author>
> <comment body>
>
> Reply by @<reply_author>:
> <reply body>

#### Thread 2 · `<path>`:<line> · @<author>
> <comment body>

---

### Conversation comments

#### @<author> · <createdAt> · <url>
> <comment body>

---

### Review summaries

#### @<author> · <state> · <url>
> <review body>

Group inline threads by file path, order by line number within file. Mark [outdated], [resolved by @login], [nit] if included via flags. Omit the "Conversation comments" or "Review summaries" section entirely when it has no entries.

4. Stop here if user only wants to list

5. Plan fixes

Before touching code:

  1. Read each file mentioned in the threads
  2. For each thread classify: fix (actionable), skip (question/discussion), or ask (unclear)
  3. Present the plan and wait for user confirmation

6. Apply all fixes

For each fix:

  1. State which thread you're addressing
  2. Apply the fix (Edit tool)

After all fixes are applied:

  1. Run the project's verification command. In this repository that is make check from the repository root - the Python project lives under backend/, so a bare uv run pre-commit at the root fails to spawn it. Substitute the equivalent in other repos.
  2. If it fails → fix before committing
  3. Commit all fixes together: fix: Address PR review comments
  4. Push to remote

All fixes go in one commit. No per-thread commits.

7. Summary

Fixed: N · Skipped: M · Needs input: K
Commit: <hash> <message>

Rules

  • Read the target file before making any fix
  • Run the verification command after all fixes are applied, not per fix
  • All fixes go in a single commit
  • If a comment is ambiguous, ask rather than guess
  • If a comment suggests a large refactor, flag it first

Frequently asked questions

What does the Pr Comments AI skill do?

Use this skill when user asks to address PR review comments. It will fetch all comments, present them in a structured format.

Why use Pr Comments on TypingMind?

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

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

Which AI models can use Pr 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 Pr Comments?

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

Is the Pr Comments AI skill free?

Yes. It is published on GitHub by vstorm-co 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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