Walk Pr logo

Walk Pr

Community
doodledood
walk-pr

Walk through a PR or large diff together — your own or someone else's — one sub-changeset at a time and one review topic at a time within it. Use when reviewing a substantive PR collaboratively, walking a large refactor, or when the user asks to walk a PR, walk a diff, explain a PR, review collaboratively, or review a change together. Optional --canvas runs the walk in a live HTML artifact.

Overview

Publisherdoodledood
Repositorymanifest-dev
Skill namewalk-pr
Stars
77
Forks
10
Bundled files
2
LicenseMIT
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Walk Pr 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/doodledood/manifest-dev/tree/main/claude-plugins/manifest-dev-tools/skills/walk-pr
  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/doodledood/manifest-dev.git /tmp/manifest-dev
mkdir -p .claude/skills
cp -r /tmp/manifest-dev/claude-plugins/manifest-dev-tools/skills/walk-pr .claude/skills/walk-pr
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Walk Pr 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 Walk Pr 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 Walk Pr 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.

Walk the user through a PR or large diff one sub-changeset (SC) at a time — a group of files that makes sense together — and one review topic at a time within it.

Input = PR number, PR URL, diff range, or nothing (infer the current branch's PR; fall back to the diff against the repository's actual remote-tracking default branch).

Reader model. The reviewer lives in the repo but has zero context on this PR. Codebase vocabulary is shared ground; vocabulary the PR introduces is not — new types, modes, modules, domain terms need explicit introduction before downstream content uses them.

Attention contract. Every mode of this skill serves the same contract: high-level orientation first, one idea active at a time, and depth — rationale, code, diffs, long text — only when the reviewer asks for it. In chat that is the one-SC/one-topic discipline below; in canvas it is a visual map with everything textual behind taps. Whenever a presentation choice arises, the failure to avoid is the text wall: never put more in front of the reviewer than the current idea needs.

Open with orientation. Present a PR primer before the change overview: a one-paragraph problem statement in workflow vocabulary, plus — only when load-bearing — a concept glossary (workflow-level definitions, not type signatures), a component sketch, or a reading hint. Then a categorized overview (load-bearing vs scaffolding/data, biggest-signal-first); skip both on trivial diffs.

Per sub-changeset, depth on demand. The always-visible surface per SC is a one-sentence behavior summary (what changes for the user) and a one-sentence verification probe (how to observe it works). Everything else — boundary view, topics, diff — is depth the reviewer expands only on SCs that need it.

Inside the depth — boundary-first. Open with a boundary view: ≤3 short paragraphs at module-boundary altitude — new/changed types, signatures at module edges, dependency-edge shifts, contract changes — load-bearing pieces only, no inventory. Diff hunks are per-file, on-demand evidence, not the default exposition. Then topics.

Topic shape. Surface review topics (probes, trade-offs, recommendations) one at a time. Each topic = two declarative sentences: a concrete framing of the concern in codebase vocabulary, then the recommended call. No nested clauses, no embedded justification, no narrated reasoning. Put rationale, code excerpts, and alternatives in supporting material (a follow-up message or on-demand probe), not the headline. Wait for the user's response before advancing. Don't batch — "thoughts on all of these?" is the failure mode this skill prevents. Hold positions under pushback when evidence still supports them.

Triage, then post. Capture the user's response per topic. When the walk completes, triage every captured item — topic responses and free notes alike — into three kinds: settled calls that become draft comments; questions the reviewer raised that you must answer first — from the code, the repo's ADRs, and any design doc the PR implements (fetch it; design-vs-implementation deviations are prime review material) — because investigation routinely flips or reshapes the would-be comment; and drops that generate nothing. Then confirm the proposed comments with the reviewer one at a time — a batched approval table recreates the overload the walk exists to prevent — each line-anchored where it ties to a specific code location, file-level or PR-level otherwise. Once all items lock, post them as a single PR review using the available GitHub review mechanism. Whether and how the PR's author addresses the comments is the manifest workflow's job downstream, not /walk-pr's.

--canvas. Load references/CANVAS_MODE.md. The HTML artifact replaces chat as the walkthrough surface — and it is a picture, not a document: one persistent system map of the PR with a stepped story, pinned review topics, comment-anywhere, and diffs in an on-demand drawer, generated once upfront and opened immediately. The user navigates self-paced and hands the consolidated review bundle back to chat, where the triage above takes over.

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 Walk Pr AI skill do?

Walk through a PR or large diff together — your own or someone else's — one sub-changeset at a time and one review topic at a time within it. Use when reviewing a substantive PR collaboratively, walking a large refactor, or when the user asks to walk a PR, walk a diff, explain a PR, review collaboratively, or review a change together. Optional --canvas runs the walk in a live HTML artifact.

Why use Walk Pr on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/doodledood/manifest-dev/tree/main/claude-plugins/manifest-dev-tools/skills/walk-pr. 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 Walk Pr?

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 Walk Pr?

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

Is the Walk Pr AI skill free?

Yes. It is published on GitHub by doodledood under the MIT 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 👇