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anchor-viz

anchor-viz is a skill for Claude Code from lynxlangya/techne. It costs 58 tokens per session (1,317 once invoked), scanned A, original, MIT.

A repository investigation workflow that draws a diagram of a real project’s architecture or another bounded structure, such as a request flow or data model.

In plain words
What is it for?
Use it to map architecture, modules, request lifecycles, data models, state changes, or type relationships from the actual codebase.
Why use it?
It requires the project’s files and entry points to be examined first, reducing the chance of presenting an invented structure.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the techne plugin — 5 skills shipped together

not rated 105repo 2mo ago A scan 58 tokens original MIT

Good fit Use it to map architecture, modules, request lifecycles, data models, state changes, or type relationships from the actual codebase.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lynxlangya/techne/anchor-viz
View source ↗ lynxlangya/techne techne.wangyun.fan
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 lynxlangya/techne --skill anchor-viz
Clone the repo
git clone --depth 1 https://github.com/lynxlangya/techne

Made for: Claude Code.

Or install techne, the plugin that ships this one along with the rest of its 5 skills.

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 anchor-viz

README.md
[![agentmods](https://agentmods.dev/badge/skills/lynxlangya/techne/anchor-viz/github.svg)](https://agentmods.dev/skills/lynxlangya/techne/anchor-viz)
Your own site
<a href="https://agentmods.dev/skills/lynxlangya/techne/anchor-viz"><img src="https://agentmods.dev/badge/skills/lynxlangya/techne/anchor-viz/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for anchor-viz

Your own site · 80×15
<a href="https://agentmods.dev/skills/lynxlangya/techne/anchor-viz"><img src="https://agentmods.dev/badge/skills/lynxlangya/techne/anchor-viz.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,317 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.
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 $0.00058 $0.01317
Opus 5 $0.00029 $0.00659
Sonnet 5 $0.00012 $0.00263
Haiku 4.5 $0.00006 $0.00132

Measured 9d ago against content hash 39caf1e454ce, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

anchor-viz 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 9d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (scripts/build_viewer.py, scripts/store_viz.py, scripts/validate-mermaid.mjs, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/anchor-viz/SKILL.md · 99 lines

How it starts

The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 99 lines

Files

What ships with it

13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 9d ago First seen · 99 lines · 58 tokens per session scan A 39caf1e454ce

Subscribe to this mod's changes

anchor-viz is a skill published in the GitHub repository lynxlangya/techne (105 stars, last pushed 2mo ago), licensed MIT. It adds 58 tokens to every session and 1,317 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.

Building agentsClaude CodeDesign and UXagent-skillsai-agentscodexcursordeveloper-tools
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