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 drawiogit 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)<a href="https://agentmods.dev/skills/sparklabx/drawio-ai-kit/drawio"><img src="https://agentmods.dev/badge/skills/sparklabx/drawio-ai-kit/drawio/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"><img src="https://agentmods.dev/badge/skills/sparklabx/drawio-ai-kit/drawio.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 | $0.01444 |
| Opus 5.5 | $0.00041 | $0.00578 |
| Sonnet 5.5 | $0.00020 | $0.00289 |
| Haiku 4.5 | $0.00010 | $0.00144 |
Grade A, and why
drawio 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 3d 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
draw.io diagrams
You build the diagram by writing a small Node script that uses the drawio-ai engine.
The engine places every box and routes every arrow. You only declare what is inside what.
Follow the steps in order. Do not skip a step. Each step names the exact file to read — read only that file, nothing else in the kit.
Step 0 — Check the CLI
command -v drawio-ai >/dev/null 2>&1 && drawio-ai root || echo "MISSING"
If it prints MISSING: stop. Tell the user to install it, then try again:
npm i -g drawio-ai-kit
Never run npm i -g yourself.
Step 1 — Understand the request
You need these 4 facts. Take them from the user's message first.
| Fact | Example | If missing |
|---|---|---|
| Domain | AWS · Azure · GCP · Databricks · multi-cloud · BPMN | Ask. |
| What to draw | "3-tier web app in one VPC, 2 AZs" · "order approval process" | Ask. |
| Output path | ./docs/arch.drawio |
Use <cwd>/<short-name>.drawio. Do not ask. The build script goes in the same folder. |
| Format | .drawio (default) · also .png |
Use .drawio. Do not ask. |
Rules for asking:
- Ask one short message with all missing questions together. Offer choices, e.g. "Which cloud: AWS, Azure, or GCP?"
- If the user said "just do it" or the request is already clear, do not ask. Pick the common choice and tell the user what you assumed at the end.
- The source is a Terraform/Terramate repo? Follow
workflows/from-iac.mdbefore Step 2.
Step 2 — Pick the domain reference
Read exactly one row's files from this skill folder:
| Domain | Read | Template to start from (drawio-ai scaffold --list shows all) |
|---|---|---|
| AWS | references/aws-architecture.md |
build_vpc.mjs, build_serverless.mjs, build_pipeline.mjs, build_landingzone.mjs |
| Azure | references/azure-architecture.md |
build_azure_vnet.mjs, build_azure_hub_spoke_lz.mjs |
| GCP | references/gcp-architecture.md |
build_gcp_vpc.mjs, build_gcp_shared_vpc_landing_zone.mjs |
| Databricks | references/databricks-architecture.md |
build_lakehouse.mjs, build_data_platform.mjs |
| Multi-cloud / hybrid | the reference of each cloud involved | build_multicloud.mjs, build_hybrid.mjs |
| BPMN / swimlane | references/bpmn.md |
build_bpmn.mjs |
What ships with it
12 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.
- references/api.md 7.5 KB
- references/aws-architecture.md 3.9 KB
- references/azure-architecture.md 4.5 KB
- references/bpmn.md 5.6 KB
- references/databricks-architecture.md 6.4 KB
- references/diagram-types.md 5.1 KB
- references/gcp-architecture.md 5.1 KB
- references/principles.md 7.5 KB
- references/style-guide.md 1.6 KB
- workflows/build.md 5.4 KB
- workflows/delegate.md 2.1 KB
- workflows/from-iac.md 1.3 KB
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.
- 3d ago First seen · 103 lines · 102 tokens per session scan A ace81e57ac18
drawio is a skill published in the GitHub repository sparklabx/drawio-ai-kit (657 stars, last pushed 4d ago), licensed MIT. It adds 102 tokens to every session and 1,444 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-10-13.
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