pr-review-canvas

pr-review-canvas is a skill for Claude Code, Codex from GCWing/BitFun. It costs 64 tokens per session (1,301 once invoked), scanned A, original, MIT.

A visual review board for understanding a pull request, branch difference, or set of code changes. It presents the changes as focused cards, maps, risk notes, and reviewer guidance.

In plain words
What is it for?
It is for summarising change scope, highlighting risky files and test files, showing key code differences, and guiding a reviewer through the changes.
Why use it?
It helps reviewers understand the important parts of a change quickly instead of working through a raw file-by-file diff alone.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It is for summarising change scope, highlighting risky files and test files, showing key code differences, and guiding a reviewer through the changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gcwing/bitfun/pr-review-canvas
About the project

BitFun is a desktop AI agent that turns tasks into interactive mini-apps while also writing code, creating documents, and controlling computers. It is for software development and general desktop work across Windows, macOS, and Linux. Its catalogue entries extend the agent with skills, agents, and instructions.

GCWing/BitFun · 2,077 stars · on GitHub

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add GCWing/BitFun --skill pr-review-canvas
Clone the repo
git clone --depth 1 https://github.com/GCWing/BitFun

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for pr-review-canvas

README.md
[![agentmods](https://agentmods.dev/badge/skills/gcwing/bitfun/pr-review-canvas.svg)](https://agentmods.dev/skills/gcwing/bitfun/pr-review-canvas)
Your own site
<a href="https://agentmods.dev/skills/gcwing/bitfun/pr-review-canvas"><img src="https://agentmods.dev/badge/skills/gcwing/bitfun/pr-review-canvas.svg" alt="Measured on agentmods" height="20"></a>
Per session 64 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,301 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00064 $0.01301
Opus 5 $0.00032 $0.00651
Sonnet 5 $0.00013 $0.00260
Haiku 4.5 $0.00006 $0.00130

Measured yesterday against content hash e76f2c1c6200, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

pr-review-canvas scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

src/crates/assembly/core/builtin_skills/pr-review-canvas/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PR Review Canvas

Use this skill to produce a session-scoped OpenBitFun Canvas that helps a reviewer understand a PR quickly. The artifact should reorganize the diff for reviewer comprehension, not mirror file-tree order. It should look and feel like a Cursor Canvas review: compact metadata, focused diff stats, pill filters, file diff cards, tables, callouts, traces, and reviewer-facing notes.

Read and follow openbitfun-canvas first. It defines the Canvas tool workflow, source rules, SDK surface, and design constraints.

Inputs

Gather the change set before writing TSX:

  • If the user gives a GitHub PR URL or number, use gh pr view and gh pr diff when available.
  • If the user explicitly asks for current local changes, use git diff, git diff --stat, and git diff --name-status.
  • If the user gives a branch/range, use that exact range.
  • If the diff source is ambiguous, ask which PR, branch, or local diff to review. Do not guess from the current branch.

Collect:

  • PR title, repo, number/link, author, base/head, status, and update time when available.
  • File stats, additions/deletions, generated/mechanical files, test files, and risky files.
  • Core hunks with enough context to understand behavior.
  • Verification commands or CI checks when visible.

Canvas Structure

Organize by review importance, not file-tree order:

  1. Review map: PR identity, scope, diff stats, state, top risks.
  2. Core logic: behavior changes first, with file cards and DiffView snippets.
  3. Wiring and integration: routes, registration, config, dependency injection, feature flags.
  4. Tests and verification: added/changed tests, missing coverage, commands run or expected.
  5. Mechanical changes: imports, renames, generated files, formatting. Summarize in compact lists or tables instead of dumping.
  6. Reviewer checklist: concrete questions, risk callouts, suggested review focus.

The first screen should already be useful to a reviewer. Lead with the behavior that matters most and one compact map of the change: risk summary, file groups, or a before/after flow. Do not make the reviewer scroll past metadata before seeing the core change.

Read the full file on GitHub · 107 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday Changed · +1 tokens per session e76f2c1c6200
  2. 8d ago First seen · 107 lines · 63 tokens per session scan A d2f46bca3c03

Subscribe to this mod's changes

pr-review-canvas is a skill published in the GitHub repository GCWing/BitFun (2,077 stars, last pushed yesterday), licensed MIT. It adds 64 tokens to every session and 1,301 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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