Review Map logo

Review Map

Organization
vstorm-co
review-map

On-demand visual walkthrough of a pull request for a reviewer — projects the diff onto the system architecture map (changed components highlighted, before/after flows, suggested reading order) and publishes it as a private HTML artifact. Use when a diff is too large or system-touching to hold in your head while reviewing.

Overview

Publishervstorm-co
Repositoryagenticos
Skill namereview-map
Stars
137
Forks
27
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vstorm-co on GitHub. Read the source before you install it.

Installation

Install the Review Map 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/vstorm-co/agenticos/tree/main/.claude/skills/review-map
  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/vstorm-co/agenticos.git /tmp/agenticos
mkdir -p .claude/skills
cp -r /tmp/agenticos/.claude/skills/review-map .claude/skills/review-map
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Map 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 Review Map 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 Review Map 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.

Skill: Review Map

Turn a hard-to-hold-in-your-head pull request into a private, visual walkthrough so a reviewer can grasp what changed and how it moves through the system before reading the diff line by line.

This is a per-reviewer comprehension aid, not part of the PR record: the artifact is private to whoever runs the skill and is meant to be discarded after the review. It changes nothing in the repo or the PR. Contrast with pr-description (durable, for everyone) — this is scaffolding for one review, today.

Inputs

  • PR number — optional. With one, review that PR (gh pr diff <n>, gh pr view <n>). Without one, review the current branch against its own base, which is not necessarily the repository default: a stacked branch targets its parent, and comparing it against main folds the parent's whole change into this walkthrough, corrupting the components, the risk and the reading order.

    bash
    BASE=$(gh pr view --json baseRefName -q .baseRefName 2>/dev/null) \
      || BASE=$(gh repo view --json defaultBranchRef -q .defaultBranchRef.name)
    git fetch origin "$BASE"
    git diff "origin/$BASE"...HEAD

Steps

  1. Gather the change. The diff, the commit log, the PR body if it exists, and the list of changed files with hunk locations.

  2. Locate it on the map. Read docs/architecture.md (routes → services → repositories, DI, the request transaction, background dispatch) and the mermaid diagrams embedded in it. For a frontend-only diff, also check docs/frontend.md; for RAG/ingestion, docs/rag-knowledge topics via docs/file-processing.md. Identify which components (boxes) and interactions (edges) the diff touches. There is no separate ADR log in this repository — the closest record of why a boundary exists is docs/about/design.md ("the six decisions that shape the codebase") and the Hard boundaries section of AGENTS.md; cite those where relevant instead of an ADR number. Prefer reusing the existing diagrams over inventing a new topology.

  3. Build the walkthrough as a self-contained HTML page. Load the artifact-design skill first where the session has it; it comes from the host, not from this repository. It must contain, in this order:

    • Changed components on the system map — the architecture diagram with the boxes and edges this PR touches highlighted; everything else dimmed. Redraw the touched slice as inline SVG in the page (the source lives as a mermaid fence in docs/architecture.md — use it as the reference topology). The reader must see "this PR lights up these parts of the system."
    • Before → after flows for each changed interaction — a small side-by-side (or annotated) sketch of the sequence/topology as it was and as it becomes. The diagram in docs/architecture.md gives you the "before" to derive from.
    • Suggested reading order — the changed files grouped by layer (routes → services → repositories, or the frontend's data layer → store → component), ordered so the reviewer reads foundations before callers, with risky hunks flagged (a new permission gate, a transaction-boundary change, a spec/schema change, a migration, a vault/credential path) and a one-line "why read this next" per group.
    • Anchors back to the code — link each file/group to its path (and line where useful) and cite the governing rule under .claude/rules/ or the hard boundary in AGENTS.md it touches.
  4. Publish it with the Artifact tool, where that tool is available to the session (it starts private). Hand the reviewer the URL and a two-line orientation: where the change lands on the map, and where to start reading.

    Neither the Artifact tool nor the artifact-design skill ships in this repository - both come from the host session - so a clean checkout has to be able to finish without them. Where they are absent, write the same page as a self-contained HTML file in the scratchpad directory (one file, inline CSS and inline SVG, no external requests) and hand over that path instead of a URL. The deliverable is the walkthrough, not the hosting.

Rules

  • Accuracy over polish. Every box you light up and every "before" you draw must match the actual code and the doc's diagram — a confident but wrong map is worse than no map. When the diff and a diagram disagree, say so: the diagram may be stale (worth flagging via scripts/docs_drift.py or a follow-up), or you may have misread the diff.
  • Reuse the doc's diagram. Do not redraw the whole system; start from the mermaid diagram in docs/architecture.md (or the relevant topic page) and mark the delta.
  • Ephemeral and private. Never post the artifact URL to the PR, commit it, or treat it as a deliverable. It is a lens for one reviewer.
  • Scale to the diff. A small, single-layer PR may only need the reading order and one before/after; reserve the full map for genuinely large or cross-cutting changes (touching routes, services, repositories, and the frontend together, or a migration plus a spec change). If a diff is trivial, say so and skip the artifact rather than manufacturing one.

Frequently asked questions

What does the Review Map AI skill do?

On-demand visual walkthrough of a pull request for a reviewer — projects the diff onto the system architecture map (changed components highlighted, before/after flows, suggested reading order) and publishes it as a private HTML artifact. Use when a diff is too large or system-touching to hold in your head while reviewing.

Why use Review Map on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vstorm-co/agenticos/tree/main/.claude/skills/review-map. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Map?

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 Review Map?

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

Is the Review Map AI skill free?

Yes. It is published on GitHub by vstorm-co 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.

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