mermaid-diagrams

mermaid-diagrams is a skill for Claude Code, Codex from langchain-ai/openwiki. It costs 87 tokens per session (822 once invoked), scanned A, original, MIT.

Instructions for adding Mermaid diagrams to generated OpenWiki pages. Mermaid is a text format for diagrams such as request flows, state changes, and data relationships.

In plain words
What is it for?
Use them when documenting runtime flows, call sequences, lifecycles, state machines, data models, or branching control flow.
Why use it?
They help turn complex repository behavior into diagrams that are easier to follow than prose alone.

Skill for Claude CodeCodex ✓ vendor

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

Good fit Use them when documenting runtime flows, call sequences, lifecycles, state machines, data models, or branching control flow.

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Install with agentmods
npx agentmods add skills/langchain-ai/openwiki/mermaid-diagrams
About the project

OpenWiki is a command-line tool that has an AI agent create and maintain a linked Markdown wiki from a codebase or personal knowledge sources. It is for coding agents that need current documentation and for people who want to explore that knowledge through a visualizer.

langchain-ai/openwiki · 16,405 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 langchain-ai/openwiki --skill mermaid-diagrams
Clone the repo
git clone --depth 1 https://github.com/langchain-ai/openwiki

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 mermaid-diagrams

README.md
[![agentmods](https://agentmods.dev/badge/skills/langchain-ai/openwiki/mermaid-diagrams/github.svg)](https://agentmods.dev/skills/langchain-ai/openwiki/mermaid-diagrams)
Your own site
<a href="https://agentmods.dev/skills/langchain-ai/openwiki/mermaid-diagrams"><img src="https://agentmods.dev/badge/skills/langchain-ai/openwiki/mermaid-diagrams/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 mermaid-diagrams

Your own site · 80×15
<a href="https://agentmods.dev/skills/langchain-ai/openwiki/mermaid-diagrams"><img src="https://agentmods.dev/badge/skills/langchain-ai/openwiki/mermaid-diagrams.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 822 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
  • Snyk pass 7 Sept 2026
  • 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 $0.00087 $0.00822
Opus 5 $0.00044 $0.00411
Sonnet 5 $0.00017 $0.00164
Haiku 4.5 $0.00009 $0.00082

Measured 12d ago against content hash cb49ce575481, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

mermaid-diagrams 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 12d ago.

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/mermaid-diagrams/SKILL.md · 45 lines

How it starts

The opening of the file, as written. The whole thing — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Mermaid Diagrams In Generated Wiki Pages

Diagrams are part of high-quality wiki generation, not decoration. Where a flow, lifecycle, or data model is easier to grasp visually, embed a Mermaid diagram in a fenced ```mermaid block on the most relevant page.

Choosing a diagram type

  • sequenceDiagram for runtime and request flows across components (auth flows, request lifecycles, agent tool loops).
  • stateDiagram-v2 for lifecycles and state machines (job states, connection states, run phases).
  • erDiagram for the data model: entities and their relationships.
  • flowchart TD for branching control flow and decision logic.

Discipline

  • Ground every diagram in inspected source. Do not invent participants, states, entities, or relationships the code does not support.
  • Cover the high-value cases: add a diagram wherever a page documents a request or runtime flow, a call sequence, a lifecycle or state machine, or a data model. A repository wiki usually has several such diagrams, not one overall. Skip pages that are navigation, reference tables, or pure configuration.
  • Still prefer a few strong diagrams over decorating every page: one accurate diagram on the page that needs it beats a diagram forced onto every page.
  • Give each diagram a one-line caption directly below it stating what it shows.
  • OpenWiki validates every mermaid fence after your run and converts fences that fail to parse into plain text fences. A degraded diagram is a quality failure; follow the syntax rules below so it does not happen.

Syntax safety

These rules prevent the most common render breakages. When in doubt, rephrase the label.

  • Never place semicolons or pipes inside node, message, or edge labels.
  • Never place unescaped angle brackets in labels; write "returns Promise of User" instead of "returns Promise".
  • In flowchart, wrap any label containing parentheses, brackets, or other punctuation in double quotes: A["calls foo(bar)"].
  • In flowchart, never use the bare word end as a node id, and never start a node id with o or x followed by a dash (both are edge-marker syntax); rename the node.
  • In sequenceDiagram, participant names with spaces or punctuation need an alias: participant AS as Auth Service.
  • Never use a Mermaid reserved word as a participant name, alias, or node id: note, end, loop, alt, opt, par, and, else, activate, deactivate, class, state, click, link. For example a notification participant must be Notifier, not Note (which collides with the note keyword).
  • In erDiagram, entity and attribute names must be single identifier-like tokens; put human phrasing in the relationship label.
  • Keep labels short. Move explanation into the surrounding prose or the caption, not the diagram.

Read the full file on GitHub · 45 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. 12d ago First seen · 45 lines · 87 tokens per session scan A cb49ce575481

Subscribe to this mod's changes

mermaid-diagrams is a skill published in the GitHub repository langchain-ai/openwiki (16,405 stars, last pushed today), licensed MIT. It adds 87 tokens to every session and 822 once invoked, about $0.0004 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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