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.
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.
npx skills add langchain-ai/openwiki --skill mermaid-diagramsgit clone --depth 1 https://github.com/langchain-ai/openwikiWrote 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.
[](https://agentmods.dev/skills/langchain-ai/openwiki/mermaid-diagrams)<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.
<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>- Snyk pass
- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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
sequenceDiagramfor runtime and request flows across components (auth flows, request lifecycles, agent tool loops).stateDiagram-v2for lifecycles and state machines (job states, connection states, run phases).erDiagramfor the data model: entities and their relationships.flowchart TDfor 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 wordendas a node id, and never start a node id withoorxfollowed 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 beNotifier, notNote(which collides with thenotekeyword). - 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.
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.
- 12d ago First seen · 45 lines · 87 tokens per session scan A cb49ce575481
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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