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 alberduris/skills --skill excalidraw-itgit clone --depth 1 https://github.com/alberduris/skillsWrote 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/alberduris/skills/excalidraw-it)<a href="https://agentmods.dev/skills/alberduris/skills/excalidraw-it"><img src="https://agentmods.dev/badge/skills/alberduris/skills/excalidraw-it/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/alberduris/skills/excalidraw-it"><img src="https://agentmods.dev/badge/skills/alberduris/skills/excalidraw-it.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 | $0.00088 | $0.01238 |
| Opus 5.5 | $0.00035 | $0.00495 |
| Sonnet 5.5 | $0.00018 | $0.00248 |
| Haiku 4.5 | $0.00009 | $0.00124 |
Grade A, and why
excalidraw-it 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 18d 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 — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
A diagram here is a scene in the operator's Excalidraw+ workspace. You write it through the MCP, and the operator's browser shows each write as soon as the server saves it. The operator edits the scene by hand only to correct something, and tells you when they do: read back only the elements they name, never the whole scene.
[!SETUP]:
- Before the first JSON you write by hand, or the first
create_diagram, call the format guide that matches the task, once per session:read_diagram_format,read_freeform_formatorread_presentation_format. It costs about 10k tokens. Skip it whiledraw.py,pieces.pyormermaid.pywrite every element: their payloads already follow the format. - Use an existing scene id, or
create_scenein the collection the operator names; with none named, in the onelist_collectionsmarksisDefault: true(privateworks only with a personal API key). Give the operator the linkhttps://app.excalidraw.com/s/<workspace>/<sceneId>, withmetadata.workspaceandmetadata.idof thecreate_sceneresponse.
[!TYPES]:
python3 <base_directory>/scripts/draw.py --help lists the diagram types that have a generator: a script that draws the whole diagram, title and note included, from its data in YAML. For one of them, draw.py <type> --help shows the YAML it takes, and it is all you need to read: write the data and run python3 <base_directory>/scripts/draw.py <type> data.yaml | python3 <base_directory>/scripts/write.py <sceneId>.
To change a generated diagram, change its data and draw it again: first send {"delete": [...]} with the addedElementIds of its write answer, and their labels go with them. Edit elements by hand only for what the data cannot say.
For a type with no generator, read its entry in ledger/types.yaml: which route draws it (create_diagram, Mermaid through mermaid-to-excalidraw, or by hand with edit_scene_content) and why the others are vetoed. A type with no entry: draw it by hand from the entry of the closest type, and propose an entry for it to the operator.
[!EVERY-WRITE]:
- Before you write JSON by hand, check the plan against the sins and use every piece from the ledger that fits; a generator already does both.
- Write through
python3 <base_directory>/scripts/write.py <sceneId>, with the payload on stdin: anaddarray, or an object withdelete,updateandadd. It callsedit_scene_contentover HTTP and prints one line; the full answer goes to$TMPDIR/excalidraw-it/. - After the write, run
python3 <base_directory>/scripts/lint.py <sceneId> --fix. It applies the fix of every finding and lints the scene again. - Once the lint is clean, call
take_screenshotbefore you show the result. Render per frame when the scene is larger than one screen, because a full-scene render is scaled down to 1920x1080 and small defects stop being visible.
[!READING]:
Each way of reading the scene costs a different amount of context. Measured on a scene of 107 elements:
search_scene_contentwith aqueryreturns only the matching elements, about 200 tokens each. It matches shape labels and standalone text, and never returns the text of a label bound to a shape (containerIdset), so it cannot tell you where a label sits.get_scene_contentreturns every element: 73 KB, about 20k tokens. Never call it.python3 <base_directory>/scripts/fetch.py <sceneId>downloads the same content through the REST API into a local file, with the key inEXCALIDRAW_API_KEY. Query that file with Python and print only the fields you need. Use it for every question about geometry: label padding, arrow endpoints, overlaps.take_screenshotcosts between a few hundred tokens and about 3k, depending on the render size. To check a layout, it is cheaper than reading the JSON.
edit_scene_content answers with the id and position of each element it added: about 4k tokens for 83 elements.
[!SINS]:
ledger/sins.yaml lists the mistakes agents make on the canvas again and again i.e., what happens, and the rule that prevents it. Read it before the first write by hand. Each sin has a lint check with the same id, except the ones marked lint: none.
What ships with it
60 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.
- .gitignore 27 B
- checks/broken_word.py 1.6 KB runs code
- checks/clipped_text_end.py 1018 B runs code
- checks/fallback_glyph.py 1.7 KB runs code
- checks/floating_arrow_end.py 4.1 KB runs code
- checks/label_hides_arrow.py 3.1 KB runs code
- checks/scattered_dashes.py 1.1 KB runs code
- checks/title_on_rounded_border.py 1.3 KB runs code
- generators/agentflow.py 13 KB runs code
- generators/architecture.py 4.4 KB runs code
- generators/bar_chart.py 5.1 KB runs code
- generators/block.py 6.6 KB runs code
- generators/c4.py 9.4 KB runs code
- generators/class.py 4.3 KB runs code
- generators/cynefin.py 11 KB runs code
- generators/er.py 5.1 KB runs code
- generators/event_modeling.py 7.0 KB runs code
- generators/examples/agentflow/1.json 18 KB
- generators/examples/agentflow/1.yaml 1.1 KB
- generators/examples/agentflow/2.json 15 KB
- generators/examples/agentflow/2.yaml 1.1 KB
- generators/examples/architecture/1.json 9.0 KB
- generators/examples/architecture/1.yaml 1.2 KB
- generators/examples/architecture/2.json 8.5 KB
- generators/examples/architecture/2.yaml 1.1 KB
- generators/examples/bar-chart/1.json 6.7 KB
- generators/examples/bar-chart/1.yaml 354 B
- generators/examples/bar-chart/2.json 6.8 KB
- generators/examples/bar-chart/2.yaml 348 B
- generators/examples/block/1.json 6.6 KB
- generators/examples/block/1.yaml 1.0 KB
- generators/examples/block/2.json 7.1 KB
- generators/examples/block/2.yaml 1.0 KB
- generators/examples/c4/1.json 9.2 KB
- generators/examples/c4/1.yaml 1.2 KB
- generators/examples/c4/2.json 8.0 KB
- generators/examples/c4/2.yaml 1.1 KB
- generators/examples/c4/3.json 8.1 KB
- generators/examples/c4/3.yaml 983 B
- generators/examples/class/1.json 9.6 KB
- generators/examples/class/1.yaml 901 B
- generators/examples/class/2.json 9.6 KB
- generators/examples/class/2.yaml 1.1 KB
- generators/examples/cynefin/1.json 20 KB
- generators/examples/cynefin/1.yaml 705 B
- generators/examples/cynefin/2.json 23 KB
- generators/examples/cynefin/2.yaml 1.2 KB
- generators/examples/er/1.json 8.0 KB
- generators/examples/er/1.yaml 880 B
- generators/examples/er/2.json 9.4 KB
- generators/examples/er/2.yaml 1.0 KB
- generators/examples/event-modeling/1.json 12 KB
- generators/examples/event-modeling/1.yaml 496 B
- generators/examples/event-modeling/2.json 13 KB
- generators/examples/event-modeling/2.yaml 576 B
- generators/examples/flowchart/1.json 10 KB
- generators/examples/flowchart/1.yaml 995 B
- generators/examples/flowchart/2.json 11 KB
- generators/examples/flowchart/2.yaml 1.2 KB
- generators/examples/flowchart/3.json 5.3 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.
- 18d ago First seen · 46 lines · 88 tokens per session scan A 3d07860c099b
excalidraw-it is a skill published in the GitHub repository alberduris/skills (20 stars, last pushed 15d ago), licensed MIT. It adds 88 tokens to every session and 1,238 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 2026-10-01.
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