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Anchor Viz

Community
lynxlangya
anchor-viz

Draw a faithful architecture or structure diagram of a real codebase or project by scanning the actual repo, never guessing. Use for system architecture, request lifecycle, data model, state transitions, type structure, draw the architecture, map this project, 画架构图.

Overview

Publisherlynxlangya
Repositorytechne
Skill nameanchor-viz
Stars
106
Forks
18
Bundled files
11
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.

  • 11 bundled files

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

  • Open source

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

Installation

Install the Anchor Viz 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/lynxlangya/techne/tree/main/skills/anchor-viz
  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/lynxlangya/techne.git /tmp/techne
mkdir -p .claude/skills
cp -r /tmp/techne/skills/anchor-viz .claude/skills/anchor-viz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anchor Viz 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 Anchor Viz 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 Anchor Viz 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.

anchor-viz

Investigate by diagramming. The diagram is the byproduct; the value is forcing real investigation before drawing.

Forced Procedure

  1. Route the request. Choose one diagramKind from the table below. If the user's intent fits multiple kinds, ask one short clarification instead of guessing.
  2. Find evidence for that kind. For architecture, enumerate roots, manifests, workspace/build/native/container/edge/infra/source-only signals. For other kinds, find the bounded entrypoint, schema, state enum, workflow, or module/type namespace before drawing.
  3. Classify shape or scope. For architecture, state one project shape and cite evidence: single-frontend, single-backend, coupled-fullstack, monorepo, library, native, native+edge, source-only code, or non-code. For other kinds, state the bounded scope and the files that prove it.
  4. Apply the non-code decision. Never declare non-code only because manifests are absent. First scan roots; then run a bounded source-extension/code-signal scan. If both are absent, or docs/vault signals clearly dominate, do not fabricate a code diagram. Draw the real top-level structure or ask what the user wants.
  5. Match the user's language. Human-facing diagram text follows the user's primary language: titles, display labels, edge/message labels, shape notes, and short explanations. Keep code-facing identifiers source-native: Mermaid IDs, file paths, symbols, package names, class/function/module names, and established technical terms such as API, SDK, CLI, HTTP, SwiftUI, AppKit, or React.
  6. Draw only evidenced relationships. Use the evidence contract for the selected kind. Every node, participant, entity, state, type, and edge/message must come from a file read.
  7. Enforce complexity gates. Keep architecture at 12-15 top-level nodes by default. For other kinds, use the validator's participant/message, entity/relationship, state/transition, or type/member limits. Split into drill-down diagrams when needed.
  8. Mark provenance. Add render-neutral comments for every element before storing: %% techne:source <elementRef> <path>[#<symbol>] or, for relationship-like elements only, %% techne:inferred <relationshipRef> <reason>. Entity-like elements always need verified techne:source; relationship-like elements need file+symbol proof unless explicitly inferred with sourced endpoints. Use actor for human sequence participants so the actor path-only allowance is mechanically scoped. Solid relationships mean read-from-code. Dashed or labeled relationships mean inference only where the selected Mermaid type supports it.
  9. Emit, validate, store, build viewer, open when interactive. Write Mermaid source first, validate it, store it with scripts/store_viz.py, always build the self-contained viewer, and only open it when the session is interactive.

Diagram Routing

User intentdiagramKindMermaid typeEvidence focus
Project architecture, module/service/package topologyarchitectureflowchart / graphManifests, workspaces, imports, services, bindings, infra edges.
Request lifecycle, command/job flow, actor conversationinteractionsequenceDiagramEntrypoint, ordered calls/messages, handlers, clients, queues.
Tables, entities, persistence relationshipsdata-modelerDiagramSchema, migrations, ORM/entity declarations, foreign keys, associations.
Status/order/task/review lifecyclestate-modelstateDiagram-v2 / stateDiagramStatus constants, state machines, reducers, guards, transition handlers.
Classes, interfaces, structs, protocols, bounded type structuretype-structureclassDiagramType declarations, inheritance, implementation, composition, public method groups.

Examples:

  • "map this project" -> architecture.
  • "show how login request flows" -> interaction.
  • "draw the database relationships" -> data-model.
  • "how does order status change" -> state-model.
  • "show the public types in this module" -> type-structure.

Script Usage

  • Validate Mermaid and altitude: node skills/anchor-viz/scripts/validate-mermaid.mjs diagram.md --project /path/to/project --max-nodes 15
  • Store a diagram in a target project: python3 skills/anchor-viz/scripts/store_viz.py --project /path/to/project --name login-flow --title "Login flow" --diagram diagram.md --shape monorepo
  • Build the self-contained viewer, opening it only when interactive: python3 skills/anchor-viz/scripts/build_viewer.py --project /path/to/project --open

The validator expects mermaid@11.15.0 and jsdom to be installed in one of these places: TECHNE_VIZ_NODE_MODULES, skills/anchor-viz/scripts/node_modules, the current working directory, or an ancestor directory.

Stop Conditions

  • Stop and ask if you cannot inspect the project files.
  • Stop and ask if the requested diagram kind is ambiguous.
  • Stop before drawing unsupported architecture for a non-code project.
  • Stop before drawing a non-architecture diagram without a bounded entrypoint, schema, status lifecycle, or module/type scope.
  • Stop before storing a diagram that fails syntax validation, provenance validation, or a complexity gate.
  • Do not create .techne/ content inside this repository while authoring the skill itself.

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 Anchor Viz AI skill do?

Draw a faithful architecture or structure diagram of a real codebase or project by scanning the actual repo, never guessing. Use for system architecture, request lifecycle, data model, state transitions, type structure, draw the architecture, map this project, 画架构图.

Why use Anchor Viz on TypingMind?

Because you install it once and use it with any model. Anchor Viz 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 Anchor Viz in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/lynxlangya/techne/tree/main/skills/anchor-viz. 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 Anchor Viz?

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 Anchor Viz?

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

Is the Anchor Viz AI skill free?

Yes. It is published on GitHub by lynxlangya 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.

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