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Pr Review Canvas

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cursor
pr-review-canvas

Generate an interactive PR review walkthrough as an HTML page. Fetches PR data via gh API, categorizes files into core vs mechanical changes, adds reviewer annotations, and renders diffs with moved-code detection. Use when the user pastes a GitHub PR URL and asks for a review, walkthrough, or summary, or says "review this PR".

Overview

Publishercursor
Repositoryplugins
Skill namepr-review-canvas
Stars
8K
Forks
728
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Pr Review Canvas 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/cursor/plugins.git /tmp/plugins
mkdir -p .claude/skills
cp -r /tmp/plugins/cursor-team-kit/skills/pr-review-canvas .claude/skills/pr-review-canvas
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pr Review Canvas 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 Review Canvas 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 Review Canvas 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 Review Canvas

Generate an interactive HTML review of a GitHub PR that reads like a peer walking you through what matters.

Workflow

1. Fetch PR data

Run these gh api calls in parallel:

bash
gh api repos/{owner}/{repo}/pulls/{number} --jq '{title, body, user: .user.login, state, additions, deletions, changed_files, base: .base.ref, head: .head.ref}'
gh api repos/{owner}/{repo}/pulls/{number}/files --paginate --jq '.[] | {filename, status, additions, deletions, patch}'
gh api repos/{owner}/{repo}/pulls/{number}/comments --jq '.[] | {user: .user.login, body, path, line}'

2. Analyze the PR and write the body HTML

Read the diffs, understand the PR, and write the <body> content directly as HTML. You have full creative freedom -- the goal is to explain the PR clearly to a reviewer. Use whatever structure best fits the PR.

Typical structure (adapt as needed):

  • Header with title, PR number, author, stats
  • Summary box explaining what the PR does in plain English
  • Core file sections with annotations and diffs
  • Mechanical/boilerplate files collapsed by default
  • Review checklist at the bottom

But you can also add:

  • Pseudocode summaries for verbose code -- show the algorithm in plain English or short pseudocode, with the real diff collapsed below (use a .bp-section card labeled "Show full implementation"). Great when 150 lines of retry/backoff/error-handling code is really just "fetch with exponential backoff and circuit breaker."
  • Diagrams (inline SVG, mermaid via CDN, ASCII art in <pre>)
  • Flowcharts showing before/after control flow
  • Tables comparing old vs new behavior
  • Callout boxes for warnings, questions, or gotchas
  • Interactive widgets if they help
  • Anything else that makes the review clearer

Pseudocode pattern example:

html
<div class="file-card">
  <div class="file-hdr" onclick="toggle(this)">
    <span class="fname">retryClient.ts</span>
    <div class="fstats"><span class="pill add">+173</span><span class="pill del">&minus;11</span><span class="chev open">&#9654;</span></div>
  </div>
  <div class="file-body open">
    <div class="file-note">
      <strong>What this does in plain English:</strong>
      <pre style="margin-top:8px;color:var(--text);font-size:12px;line-height:1.6;">
fetch(url):
  if circuit breaker is open → fail fast
  retry up to N times:
    try fetch with timeout
    on success → close circuit breaker, return
    on retryable error → wait (exponential backoff + jitter)
    on non-retryable error → throw
  circuit breaker records failure</pre>
    </div>
    <div class="bp-section" style="margin:0;border:0;border-radius:0;">
      <div class="bp-hdr" onclick="toggleBP(this)">
        <span>Show full implementation (+173 lines)</span><span class="chev">&#9654;</span>
      </div>
      <div class="bp-body"><div data-diff="retryClient"></div></div>
    </div>
  </div>
</div>

3. Available CSS classes and JS utilities

Read styles.css and renderer.js from this skill directory. These give you a prebuilt dark-themed toolkit. Inject them into template.html verbatim.

CSS classes you can use:

ClassPurpose
.header, .header h1, .header-metaPage header
.pill.add, .pill.del, .pill.filesStat badges (+N, -N, N files)
.contentCentered content wrapper (max 900px)
.summarySummary/TL;DR box
.section-titleSection heading with bottom border
.icInline code reference (mono, blue, dark bg)
.file-card, .file-hdr, .file-bodyCollapsible file card (use onclick="toggle(this)" on .file-hdr)
.file-noteSticky reviewer annotation inside a file card
.bp-section, .bp-hdr, .bp-bodyCollapsed boilerplate card (use onclick="toggleBP(this)")
.bp-noteNote inside a boilerplate card
.verdictReview checklist box

JS functions available:

FunctionUsage
toggle(hdrElement)Toggle a .file-body open/closed
toggleBP(hdrElement)Toggle a .bp-body open/closed
renderDiff(target, diffInput)Render a unified diff. target can be a DOM element, string ID, or CSS selector. diffInput can be a raw patch string OR an array of lines -- both work. Automatically filters imports, collapses whitespace-only changes, detects moved code (blue/purple tint).
esc(string)HTML-escape a string

Rendering diffs -- use data-diff attributes with auto-discovery. Put <div data-diff="KEY"></div> placeholders in your body HTML wherever you want a diff rendered. The renderer finds them automatically after DOM load and fills them from the <script id="pr-diffs-json" type="application/json"> element in template.html.

CRITICAL: Patch strings can contain </script> in addition to newlines, backslashes, and quotes. Even json.dumps(...) is not enough if you paste raw output into executable <script> because HTML parsing can terminate the tag early. Never manually embed patch strings in JS/JSON. Instead, use this safe approach:

  1. During the fetch step, save patches to a JSON file using jq (which handles escaping correctly):
bash
gh api repos/{owner}/{repo}/pulls/{number}/files --paginate \
  --jq '[.[] | {key: (.filename | gsub("[^a-zA-Z0-9]"; "_")), value: (.patch // "")}] | from_entries' \
  > /tmp/pr-patches-{number}.json
  1. During assembly, use Python to safely inject the JSON into template.html:
bash
python3 <<'PY'
import json
from pathlib import Path

patches = json.loads(Path('/tmp/pr-patches-{number}.json').read_text())
html = Path('/tmp/pr-review-{number}-body.html').read_text()
css = Path('styles.css').read_text()
js = Path('renderer.js').read_text()
tmpl = Path('template.html').read_text()

# Prevent literal </script> from terminating HTML script tags early.
safe_json = json.dumps(patches).replace('<', '\\u003c').replace('>', '\\u003e').replace('&', '\\u0026')

out = (
  tmpl.replace('/* INJECT_CSS */', css)
      .replace('/* INJECT_JS */', js)
      .replace('<!-- INJECT_BODY -->', html)
      .replace('{"__PR_DIFFS_PLACEHOLDER__":true}', safe_json)
)

Path('/tmp/pr-review-{number}.html').write_text(out)
PY

This guarantees valid JSON and script-safe HTML embedding. The agent writes body HTML to a temp file, then Python assembles everything safely.

The diff data keys should match the data-diff attribute values in the HTML:

html
<div data-diff="path_to_file_ts"></div>

Since renderer.js loads in <head>, you can also call renderDiff(target, lines) directly from inline <script> tags if needed for custom use cases. The function accepts a DOM element, ID string, or CSS selector as target, and a string or array as lines.

You're not limited to these. Add your own inline <style> blocks, <script> blocks, SVGs, diagrams, or anything else. The prebuilt pieces save time but don't constrain you.

4. Assemble and serve

  1. Write your body HTML (everything that goes inside <body>) to /tmp/pr-review-{number}-body.html

  2. Save patches to /tmp/pr-patches-{number}.json using the jq command from step 3 above

  3. Run the Python assembly script from step 3 above (reads styles.css, renderer.js, template.html from this skill directory, injects body + patches safely, writes final HTML)

  4. Start a local server on a fixed port:

    bash
    cd /tmp && python3 -m http.server 8432 --bind 127.0.0.1

    Run this backgrounded, then navigate the in-app browser to http://127.0.0.1:8432/pr-review-{number}.html.

    Why a fixed port and cd /tmp: Background shells have no TTY, so Python buffers its startup message ("Serving HTTP on...") indefinitely — using port 0 means you can never read which port was chosen. And --directory /tmp works but cd /tmp is more robust across Python versions. If port 8432 is taken, try 8433, 8434, etc.

Diff features (handled automatically by renderer.js)

  • Filters out import-only lines
  • Collapses whitespace-only changes into context lines
  • Detects moved code blocks (3+ consecutive lines deleted in one place and added identically elsewhere) -- renders in blue/purple instead of red/green
  • Near-matches (moved + small edit) get a different purple tint

Style notes

  • Dark theme: #1a1a1a background, Inter body font, IBM Plex Mono for code
  • Use var(--warning) for orange, var(--success) for green, var(--danger) for red, var(--accent) for blue
  • Sticky file headers (position: sticky; top: 0) and notes (top: 35px) pin while scrolling
  • Core files expanded by default (.file-body.open), mechanical files collapsed

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 Review Canvas AI skill do?

Generate an interactive PR review walkthrough as an HTML page. Fetches PR data via gh API, categorizes files into core vs mechanical changes, adds reviewer annotations, and renders diffs with moved-code detection. Use when the user pastes a GitHub PR URL and asks for a review, walkthrough, or summary, or says "review this PR".

Why use Pr Review Canvas on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cursor/plugins/tree/main/cursor-team-kit/skills/pr-review-canvas. 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 Review Canvas?

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 Review Canvas?

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

Is the Pr Review Canvas AI skill free?

It is published on GitHub by cursor. 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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