Pr Writing Review logo

Pr Writing Review

CommunityPopular
evalstate
pr-writing-review

Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer suggestions to help refine future writing.

Overview

Publisherevalstate
Repositoryfast-agent
Skill namepr-writing-review
Stars
3.9K
Forks
444
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by evalstate on GitHub. Read the source before you install it.

Installation

Install the Pr Writing 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.
  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/evalstate/fast-agent.git /tmp/fast-agent
mkdir -p .claude/skills
cp -r /tmp/fast-agent/examples/hf-toad-cards/skills/pr-writing-review .claude/skills/pr-writing-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pr Writing 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 Pr Writing 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 Pr Writing 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.

PR Writing Review

Extract editorial feedback from GitHub PRs to learn from review improvements.

Prerequisites

  • GitHub CLI: gh installed
  • Authenticated gh session: gh auth status should show you’re logged in
    • For private repos, your token needs appropriate scopes (typically repo).
  • Python: 3.12+
  • uv (recommended): https://github.com/astral-sh/uv

Division of Labor

ToolResponsibility
Python scriptAPI calls, parsing, file tracking across renames, structured extraction
LLM analysisPattern recognition, paragraph comparison, style lesson synthesis

Quick Start

bash

> **All paths are relative to the directory containing this SKILL.md file.**
> Before running any script, first `cd` to that directory or use the full path.

# Get suggestions and feedback
uv run scripts/extract_pr_reviews.py <pr_url>

# Get full first→final comparison for deep analysis
uv run scripts/extract_pr_reviews.py <pr_url> --diff

# Same as above, but cap each FIRST/FINAL dump to 2k chars for LLM prompting
uv run scripts/extract_pr_reviews.py <pr_url> --diff --max-file-chars 2000

Workflow

Step 1: Extract with --diff

bash
uv run scripts/extract_pr_reviews.py https://github.com/org/repo/pull/123 --diff

This outputs:

  1. Explicit Suggestions — exact before/after text from suggestion blocks (supports multiple suggestion blocks per comment)
  2. Reviewer Feedback — plain text comments (the "why" behind changes)
  3. File Evolution — first draft and final version of each text file

Tip: add --max-file-chars 2000 to keep each FIRST/FINAL dump lightweight, or pair --diff with --no-files if you only need the suggestion/feedback summaries.

Step 2: Analyze the Output

With the script output, perform this analysis:

A. Catalog the Explicit Suggestions

Create a table of mechanical fixes:

PatternOriginalFixed
Grammar"Its easier""It's easier"
Filler removal"using this way""this way"
Capitalization"Image Generation""image generation"
B. Map Feedback to Changes

For each reviewer feedback comment:

  1. Find the relevant section in FIRST DRAFT
  2. Find the same section in FINAL VERSION
  3. Document what changed and why

Example:

Feedback: "would be nice to end more enthusiastically"

First draft: "...it's simple to add new tools to Claude and use them straight away."

Final: "...Let us know what you find and create in the comments below!"

Lesson: End blog posts with a call-to-action

C. Paragraph-by-Paragraph Comparison

Compare FIRST DRAFT to FINAL VERSION section by section:

  • What was added?
  • What was removed?
  • What was reworded?
  • What structural changes were made?
D. Synthesize Style Patterns

Group findings into categories:

CategoryPatterns Found
ClarityPassive→active, shorter sentences, remove filler
PrecisionVague→specific, "Create"→"Generate"
ToneAdded enthusiasm, call-to-action endings
StructureAdded transitions, better section flow
Grammarits/it's, subject-verb agreement
ContentAdded links, examples, context

Script Options

📁 All paths are relative to the directory containing this SKILL.md file.

FlagOutputUse Case
(none)Suggestions + feedbackQuick review of what reviewers said
--diffAdds FIRST/FINAL file dumps to the default outputDeep analysis of how the author responded
--max-file-chars NTruncates each FIRST/FINAL block to N chars (appends ...[truncated X chars])Keep prompts within LLM token limits
--no-filesSuppresses FIRST/FINAL dumps even when --diff is setWhen you only need explicit suggestions + reviewer feedback
--jsonRaw JSON (includes file_evolutions when --diff without --no-files)Programmatic processing

Input formats: pass either a full PR URL, owner/repo PR_NUMBER, or owner repo PR_NUMBER.

Output Structure

Default Output

  • Writing Suggestions: Grouped by reviewer, shows original→suggested text (fenced blocks) along with any reviewer note and a permalink back to GitHub
  • Reviewer Feedback: Plain comments without code suggestions, each tagged with its GitHub link

With --diff

  • Explicit Suggestions: Compact before/after pairs, reviewer notes, and GitHub permalinks in one place
  • Reviewer Feedback: Numbered list of requests (same as default view)
  • File Evolution: FIRST DRAFT and FINAL VERSION for each .md/.txt/.rst/.mdx file; add --max-file-chars to truncate each block with a visible ...[truncated X chars] indicator

Handling File Renames

The script traces files through renames by:

  1. Checking each commit for rename operations
  2. Building a path history (e.g., claudeimages.mdclaude-images.mdclaude-and-mcp.md)
  3. Fetching content using the correct path for each commit

Example Analysis Output

After running the script and performing LLM analysis, produce a summary like:

markdown
## Style Lessons from PR #123

### Mechanical Fixes

- Fix grammar: "Its" → "It's" (contraction)
- Lowercase generic terms: "Image Generation" → "image generation"
- Remove filler: "the output quality of" → "the quality of"

### Reviewer-Driven Changes

- **"end more enthusiastically"** → Added call-to-action in conclusion
- **"emphasize these are SoTA"** → Changed "latest" to "state-of-the-art"
- **"add blurb about MCP Server"** → Added explanatory paragraph

### Structural Improvements

- Added transition sentence between sections
- Simplified setup instructions (3 sentences → 1)
- Added new bullet point for model flexibility

Limitations

  • Only extracts inline PR review comments (not issue comments or the PR description)
  • Extremely long files can still be heavy; when that happens, lower --max-file-chars or pass --no-files to keep outputs prompt-friendly

{{currentDate}} {{env}}

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Pr Writing Review AI skill do?

Extract and analyze writing improvements from GitHub PR review comments. Use when asked to show review feedback, style changes, or editorial improvements from a GitHub pull request URL. Handles both explicit suggestions and plain text feedback. Produces structured output comparing original phrasing with reviewer suggestions to help refine future writing.

Why use Pr Writing Review on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/evalstate/fast-agent/tree/main/examples/hf-toad-cards/skills/pr-writing-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Pr Writing 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 Pr Writing Review?

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

Is the Pr Writing Review AI skill free?

Yes. It is published on GitHub by evalstate 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇