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Pr Review Canvas

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cursor
pr-review-canvas

Render a PR diff review as a Cursor Canvas that groups changes by reviewer importance, separates boilerplate from core logic, and highlights tricky or unexpected code. Use when reviewing a pull request, summarizing a diff for review, or when the user asks for a PR review canvas, diff walkthrough, or change-set overview.

Overview

Publishercursor
Repositoryplugins
Skill namepr-review-canvas
Stars
8K
Forks
728
Bundled files
Instructions only
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 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/pr-review-canvas/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

Build a canvas that presents a PR diff reorganized for reviewer comprehension — not in file-tree order.

Prerequisites

Read ~/.cursor/skills-cursor/canvas/SKILL.md first. It contains the generation policy, design guidance, slop rules, self-check, and file-path conventions you must follow. The full component and hook surface is declared in ~/.cursor/skills-cursor/canvas/sdk/index.d.ts and its sibling .d.ts files — read them to discover exact exports and prop shapes rather than guessing.

Gather the diff

Expect a GitHub PR link (a full URL like https://github.com/<owner>/<repo>/pull/<n>, or an equivalent gh-resolvable reference). Use gh pr diff <pr> to collect every file's path, additions, deletions, and hunks.

If the user didn't provide a PR link, stop and ask. Do not guess at the current branch, infer from recent history, or fall back to a local git diff. Ask the user which diff they want to review — a specific PR URL or number — and wait for their reply before continuing.

Group changes for comprehension

Do not present files in alphabetical or tree order. Reorganize into sections ordered by reviewer value:

  1. Core logic — New behavior, algorithm changes, state transitions, API surface changes. Show full diffs with surrounding context.
  2. Wiring & integration — Route registration, dependency injection, config plumbing that connects the core logic. Condensed — enough to confirm correctness.
  3. Boilerplate & mechanical — Import reordering, renames, generated code, formatting, type re-exports. Summarize as a list of file names and stats. No inline diffs unless specifically relevant.

Lead with core logic. The reviewer's attention is freshest at the top.

Distill complex logic into pseudocode

When a core change involves dense or intricate logic — deeply nested conditions, state machines, retry/backoff flows, multi-step transformations — add a short pseudocode summary next to the diff. The pseudocode should strip away language syntax, error handling, and boilerplate to expose the essential algorithm or control flow in a few lines. This lets the reviewer confirm intent before reading the real code.

Only do this when the actual diff is hard to scan. Straightforward changes don't need a pseudocode mirror.

Trace tricky logic on a concrete example

Pseudocode shows the shape of the change; an example trace shows it executing. When a hunk changes behavior in a way that's hard to predict from reading it — reordered effects, new short-circuits, altered edge cases — pick a concrete input and walk it through both the old and new code paths side-by-side, highlighting the step where they diverge and what the observable outcome is. Keep the input small and realistic.

Use this for genuinely surprising behavior changes, not every core hunk.

Call attention to tricky things

When a hunk contains something surprising, risky, or easy to miss, visually separate it from the surrounding diff and pair it with a short tag (e.g. "Subtle", "Breaking", "Race condition", "Perf") and a one-sentence explanation so the reviewer sees the concern and the code together.

Reserve these callouts for genuinely tricky items — overuse destroys signal.

Tone and content

Write reviewer-facing commentary, not a changelog. Focus on:

  • Why something changed, not just what changed.
  • Interactions between files — e.g. "The new validator in core.ts is invoked by the route added in routes.ts."
  • Anything the diff alone doesn't make obvious.

Keep commentary terse. One or two sentences per note.

Be creative

The sections above are a floor, not a ceiling. The goal is the fastest possible path for the reviewer to understand this specific change — so look at the diff in front of you and ask what representation would actually help. A tiny state diagram, a before/after call graph, a table of input→output pairs, a timeline of commits, a confidence annotation per file, a single large callout with everything else collapsed — whatever fits the change.

The canvas SDK has charts, tables, diff views, DAG layout, cards, stats, interactive state, and more. Reach for whichever components best serve the change at hand. A review of a refactor looks different from a review of a bug fix looks different from a review of a new feature — let the canvas reflect that.

Frequently asked questions

What does the Pr Review Canvas AI skill do?

Render a PR diff review as a Cursor Canvas that groups changes by reviewer importance, separates boilerplate from core logic, and highlights tricky or unexpected code. Use when reviewing a pull request, summarizing a diff for review, or when the user asks for a PR review canvas, diff walkthrough, or change-set overview.

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/pr-review-canvas/skills/pr-review-canvas. TypingMind reads its SKILL.md 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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