Borrowing it
Nothing to install: this file belongs to mcwalrus/oppertunity-party. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/mcwalrus/oppertunity-party/main/.agents/skills/mermaid-diagrams/SKILL.mdgit clone --depth 1 https://github.com/mcwalrus/oppertunity-partyWrote 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/mcwalrus/oppertunity-party/mermaid-diagrams)<a href="https://agentmods.dev/skills/mcwalrus/oppertunity-party/mermaid-diagrams"><img src="https://agentmods.dev/badge/skills/mcwalrus/oppertunity-party/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/mcwalrus/oppertunity-party/mermaid-diagrams"><img src="https://agentmods.dev/badge/skills/mcwalrus/oppertunity-party/mermaid-diagrams.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00160 | $0.02613 |
| Opus 5 | $0.00080 | $0.01307 |
| Sonnet 5 | $0.00032 | $0.00523 |
| Haiku 4.5 | $0.00016 | $0.00261 |
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 11d 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 — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mermaid Diagrams for SWE / DevOps / Platform Engineering
You are helping an engineer produce Mermaid diagrams that communicate technical initiatives clearly — changes across codebases, infrastructure, pipelines, data models, and platforms. Your job is to (a) understand what they're trying to show, (b) choose the right diagram type(s), and (c) generate syntactically valid Mermaid that renders on GitHub, GitLab, Notion, and other common platforms.
Workflow
1. Understand the initiative
Before generating anything, get clear on what the user is trying to communicate. Ask the minimum questions needed — don't over-interview, but don't guess at the important parts. Key things to understand:
- What changed / is changing? (a new service, a refactor, a deployment process, a data model, a branching strategy, a rollout plan…)
- Who's the audience? (teammates in a PR review, stakeholders reading an RFC, on-call engineers reading a runbook, leadership reading a roadmap)
- What question should the diagram answer? This is the most important one. A good diagram answers one question clearly. If the user has multiple questions, plan multiple diagrams.
Common questions → diagram types:
| Question the diagram should answer | Diagram type |
|---|---|
| Who owns what code / which layer does what? | flowchart with subgraphs |
| What does the data look like at each step? | flowchart LR with edge labels |
| What happens when N callers hit this at once? | sequenceDiagram with par/and |
| What changed (before vs after)? | Paired flowchart subgraphs |
| How do we roll this out / migrate? | flowchart with Before→Flagged→After subgraphs |
| What states does this object / job live in? | stateDiagram-v2 |
| How do requests flow through the system? | sequenceDiagram with timing notes |
| What is the system context at a high level? | C4Context or C4Container |
| What does the cloud topology look like? | architecture-beta |
| What's the project / initiative schedule? | gantt |
| What's the branching / release strategy? | gitGraph |
| How do metrics / costs distribute? | xychart-beta, sankey-beta, pie |
| How does this compare across multiple axes? | radar-beta |
| How are requirements / SLAs linked? | requirementDiagram |
| What work is in flight (sprint, incidents)? | kanban |
| How do ideas / capabilities decompose? | mindmap |
| What are the priorities (effort vs impact)? | quadrantChart |
| What does the DB schema look like? | erDiagram |
| What are the domain classes? | classDiagram |
| What's the git branching model? | gitGraph |
What ships with it
5 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.
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.
- 11d ago First seen · 231 lines · 160 tokens per session scan A 5bd306cc070b
mermaid-diagrams is a skill published in the GitHub repository mcwalrus/oppertunity-party (0 stars, last pushed 19d ago), licensed MIT. It adds 160 tokens to every session and 2,613 once invoked, about $0.0008 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 2026-08-31.
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