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 sparklabx/drawio-ai-kit --skill drawio-bpmngit clone --depth 1 https://github.com/sparklabx/drawio-ai-kitWrote 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/sparklabx/drawio-ai-kit/drawio-bpmn)<a href="https://agentmods.dev/skills/sparklabx/drawio-ai-kit/drawio-bpmn"><img src="https://agentmods.dev/badge/skills/sparklabx/drawio-ai-kit/drawio-bpmn/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/sparklabx/drawio-ai-kit/drawio-bpmn"><img src="https://agentmods.dev/badge/skills/sparklabx/drawio-ai-kit/drawio-bpmn.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 | $0.00102 | $0.01723 |
| Opus 5 | $0.00051 | $0.00861 |
| Sonnet 5 | $0.00020 | $0.00345 |
| Haiku 4.5 | $0.00010 | $0.00172 |
Grade A, and why
drawio-bpmn 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.
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.
This is a copy
75% identical to drawio-aws — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Draw.io BPMN
Produce correct BPMN (Business Process Model and Notation) swimlane diagrams in
draw.io. This skill is a thin frontend; the deterministic engine, validator, and
rules live in the drawio-ai-kit package, reached via the drawio-ai CLI.
0. Preflight — the CLI must be installed
command -v drawio-ai >/dev/null 2>&1 || echo "Install the Kit first: npm i -g github:sparklabx/drawio-ai-kit"
If drawio-ai is not on PATH, stop and tell the user to run
npm i -g github:sparklabx/drawio-ai-kit. Never run npm i -g yourself — nothing mutates the
user's global environment without their say-so.
1. Delegate the build (preferred when your harness supports it)
If your harness can spawn autonomous subagents that run shell commands AND read images (e.g. Claude Code's Task tool, a general-purpose agent), run the whole build loop in a subagent — the rules, icon searches, and every render/fix iteration then cost this conversation nothing. If it can't (or the subagent can't read images), skip to Inline path below — same loop, same rules.
Before spawning, resolve what the subagent cannot ask about: diagram scope, output directory (absolute path under the user's project), filename. Run the preflight above yourself. For a multi-diagram request, spawn one subagent per diagram in parallel with distinct filenames.
Model routing — if your harness lets you choose the subagent's model, route by task weight: a fast/cheap tier (Claude Haiku-class — must support vision) when the request matches a template from the rules' Templates table (reproduction is mechanical; the validator's advice strings teach every fix), your default strong model for free-hand or novel architectures. If a cheap subagent returns VALIDATE not ok or ITERATIONS > 3, respawn ONCE on the strong model before taking over inline. Multi-diagram requests: route each diagram independently.
Subagent prompt (fill every <...>):
Build a BPMN swimlane .drawio diagram with the drawio-ai CLI.
Request: <user's request + clarifications, verbatim>
Output: <ABS_PROJECT_DIR>/<NAME>.drawio — never write inside the Kit, never into cwd.
Follow exactly:
1. Set ROOT="$(drawio-ai root)". Read $ROOT/docs/api-cheatsheet.md — the full layout-engine
API in one file; never read library source.
2. Run `drawio-ai workflow` and `drawio-ai principles --mode bpmn` — the source of
truth. (Fallback if a command is blocked: read $ROOT/rules/*.md directly.)
3. Look up every icon with ONE batched `drawio-ai search "a, b, c"`; never recolor icons.
4. Scaffold, don't write: `drawio-ai scaffold --list`, pick the closest template, then
`drawio-ai scaffold <name>.mjs -o <dir>/build.mjs` — the script arrives runnable
(absolute imports, self-validating, self-rendering with an issues list). Edit only the
deltas. If no template is close AND you'd change more than half of it, Write a new
script instead (keep the scaffold's self-check tail). Layout engine only
(group/frame/grid/icon/box + renderTree), NO hand-written coordinates.
5. Each `node build.mjs` run prints validate JSON AND the render's machine-readable
`issues` list. Fix from THAT checklist — all issues in one Edit round — then re-run.
Loop until issues is empty.
6. Only when issues is empty: Read the PNG once as final visual confirmation (list any
remaining visual problems, fix ALL in one round). Target <= 2 PNG reads total. Then
render once WITHOUT --check for the final deliverable PNG.
Do NOT invoke any drawio skill — this prompt already contains the full procedure.
Do not ask questions — make the standard choice and record it under ASSUMPTIONS.
Return EXACTLY this block, nothing else:
DRAWIO: <absolute path to .drawio>
PNG: <absolute path to .png>
VALIDATE: <verbatim final validate JSON>
ICONS: <comma-separated icon names used>
ITERATIONS: <number of render/fix cycles>
SUMMARY: <one sentence describing the diagram>
ASSUMPTIONS: <choices made without asking, or "none">
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.
- 9d ago First seen · 141 lines · 102 tokens per session scan A 1af87e1e2a20
drawio-bpmn is a skill published in the GitHub repository sparklabx/drawio-ai-kit (640 stars, last pushed 29d ago), licensed MIT. It adds 102 tokens to every session and 1,723 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 75% identical to drawio-aws, differing in 38 lines, and is treated as a copy.
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