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Agent skill

Drawio GCP

by sparklabx in sparklabx/drawio-ai-kit

A skill your agent uses when the user asks for a GCP or Google Cloud architecture diagram — VPC/networking, GKE, Cloud Run, landing zone, multi-region, or any diagram built with GCP service icons.

MITAuto-check passedDevelopment

Install Drawio GCP

skills CLI
$ npx skills add sparklabx/drawio-ai-kit --skill drawio-gcp -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install sparklabx/drawio-ai-kit drawio-gcp --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/sparklabx/drawio-ai-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drawio-gcp .claude/skills/drawio-gcp && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
drawio-gcp
GitHub stars
654
Token cost
~1.6k tokens
SKILL.md length
511 words
Files
1
Skills in repo
5
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when the user asks for a GCP or Google Cloud architecture diagram — VPC/networking, GKE, Cloud Run, landing zone, multi-region, or any diagram built with GCP service icons.

  • Works in 2 steps: Preflight — the CLI must be installed → Delegate the build (preferred when your…
  • The user asks for a GCP
  • SKILL.md covers 0. Preflight — the CLI must be…, 1. Delegate the build…, Inline path (no subagent… and Domain notes, plus 1 more section
  • Calls npm

What it does

Drawio GCP is an agent skill from sparklabx/drawio-ai-kit. Use when the user asks for a GCP or Google Cloud architecture diagram — VPC/networking, GKE, Cloud Run, landing zone, multi-region, or any diagram built with GCP service icons. Builds with the declarative layout engine using ground-truth GCP stencils, validates (stencils/colors/nesting/geometry), runs a render-based vision self-check. Default output is .drawio; PNG/SVG only on request.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Diagrams and Verification before completion. It works with Google Cloud, draw.io, Cloud Run and Google Kubernetes Engine. The repository describes itself as: Teach your AI to draw correct, beautiful draw.io diagrams — declarative layout engine, ground-truth stencils, structural validator, vision self-check. AWS · Azure · GCP ·… The licence is MIT.

When your agent uses it

  • The user asks for a GCP
  • Google Cloud architecture diagram — VPC/networking
  • Any diagram built with GCP service icons

Example prompts

  • “/drawio-gcp”

Requirements

  • Node.js

Workflow steps

2 steps, taken from the step headings in SKILL.md.

  1. Preflight — the CLI must be installed
  2. Delegate the build (preferred when your harness supports it)

What it can do on your machine

Read from SKILL.md and the folder at commit 1a03d87. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Drawio GCP loads about 1.6k tokens when it runs. Until then it costs about 100 tokens; SKILL.md has 511 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~100
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from sparklabx/drawio-ai-kit at commit 1a03d87, republished under its MIT licence (© sparklabx). 511 words, ~1,591 tokens.

Download SKILL.mdSave it as .claude/skills/drawio-gcp/SKILL.md (or your agent's skills folder).
name
drawio-gcp
description
Use when the user asks for a GCP or Google Cloud architecture diagram — VPC/networking, GKE, Cloud Run, landing zone, multi-region, or any diagram built with GCP service icons. Builds with the declarative layout engine using ground-truth GCP stencils, validates (stencils/colors/nesting/geometry), runs a render-based vision self-check. Default output is .drawio; PNG/SVG only on request.
version
1.0.1
license
MIT

Draw.io GCP

Produce correct Google Cloud architecture 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

bash
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 <...>):

text
Build a GCP architecture .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 gcp` — 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">

Relay DRAWIO, PNG and SUMMARY to the user verbatim; do NOT re-read the .drawio or PNG in this conversation — the subagent already ran the vision self-check. If VALIDATE is not ok, take over via the Inline path (the build .mjs and .drawio are on disk at the returned paths).

Show full SKILL.md (197 more words)Show less

Inline path (no subagent support)

1. Shared Workflow
bash
drawio-ai workflow

Prints the build → validate → render → write-to-project-path loop every diagram follows. Read it; it is the source of truth for the process.

2. Domain rules
bash
drawio-ai principles --mode gcp

Returns the GCP rules + shared principles + catalog categories.

3. Build with the engine, then validate + render

Resolve the Kit's install dir, then import the engine by absolute path (the Shared Workflow shows the exact pattern):

bash
ROOT="$(drawio-ai root)"     # absolute path to the installed Kit

Build with the declarative layout engine (NO hand-written coordinates), then: drawio-ai validate <file> → drawio-ai render <file> -o <file>.png (Read the PNG for the vision self-check) → write the .drawio to an absolute path under the user's project (never the Kit, never cwd).

Domain notes

Hierarchy: Organization → Folder → Project → global VPC → regional Subnet. VPCs are global resources spanning all regions; subnets are regional. Shared VPC allows one host project to own the VPC while service projects deploy resources into it.

Self-check (before delivering)

  • Built with the layout engine — no hand-written coordinates.
  • drawio-ai validate → ok, no warnings, no advice.
  • drawio-ai suggest-layout → recommended archetype matches your layout; no sparsity (one-icon-frame) warning.
  • Every icon came from drawio-ai search (category colors intact).
  • drawio-ai render vision self-check passed.
  • Output written under the user's project, not the Kit.

© sparklabx, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/drawio-gcp of sparklabx/drawio-ai-kit.

Open the folder on GitHubat commit 1a03d87

Compare with similar skills

Drawio GCP next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Drawio GCP compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Drawio GCP this skillsparklabx/drawio-ai-kit654—~1.6kAutomated safety check: PassMIT
GCP DrawIO Diagram Generatora5c-ai/babysitter1.8k—~3.7kAutomated safety check: PassMIT
Drawio MCP Diagrammingthomast1906/github-copilot-agent-skills202—~6.6kAutomated safety check: PassNone
Architecture DiagramMathews-Tom/armory328—~4.8kAutomated safety check: PassMIT
Devopsnicepkg/auto-company1942 repos~814Automated safety check: PassMIT
Google Agents CLI Publishpifferologo/cloud-agents-cli1291 repos~2.4kAutomated safety check: PassApache-2.0

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Works with

Categories

Questions about Drawio GCP

What does Drawio GCP do?

A skill your agent uses when the user asks for a GCP or Google Cloud architecture diagram — VPC/networking, GKE, Cloud Run, landing zone, multi-region, or any diagram built with GCP service icons. Drawio GCP is an agent skill from sparklabx/drawio-ai-kit. Use when the user asks for a GCP or Google Cloud architecture diagram — VPC/networking, GKE, Cloud Run, landing zone, multi-region, or any diagram built with GCP service icons.

When should I use Drawio GCP?

Drawio GCP fits situations like: the user asks for a GCP; google Cloud architecture diagram — VPC/networking; any diagram built with GCP service icons.

How do I install Drawio GCP in Claude Code?

Run `npx skills add sparklabx/drawio-ai-kit --skill drawio-gcp -a claude-code`. Or copy the skill folder (skills/drawio-gcp in sparklabx/drawio-ai-kit) into .claude/skills/drawio-gcp in your project. Claude Code loads it when a task matches its description.

How do I install Drawio GCP in Codex?

Run `npx skills add sparklabx/drawio-ai-kit --skill drawio-gcp -a codex`. Or copy the skill folder (skills/drawio-gcp in sparklabx/drawio-ai-kit) into .agents/skills/drawio-gcp in your project. Codex loads it when a task matches its description.

Can I use Drawio GCP in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sparklabx/drawio-ai-kit --skill drawio-gcp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/drawio-gcp, .gemini/skills/drawio-gcp, .github/skills/drawio-gcp and .opencode/skills/drawio-gcp in your project.

What does Drawio GCP need to run?

Going by SKILL.md and its folder, Drawio GCP needs the command-line tools its instructions call (npm). Our summary lists: Node.js.

Does Drawio GCP access the network?

SKILL.md contains no URLs. Its commands use npm, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Drawio GCP safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Drawio GCP use?

Drawio GCP is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Drawio GCP use?

About 1.6k tokens (SKILL.md is roughly 6.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Drawio GCP?

Skills that share tags, products or a category with Drawio GCP: GCP DrawIO Diagram Generator (a5c-ai/babysitter, 1.8k stars), Drawio MCP Diagramming (thomast1906/github-copilot-agent-skills, 202 stars), Architecture Diagram (Mathews-Tom/armory, 328 stars) and Devops (nicepkg/auto-company, 194 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Drawio GCP?

sparklabx (a GitHub organization) maintains it in sparklabx/drawio-ai-kit, which has 654 GitHub stars. The repository holds 5 skills in this directory. The repository was last updated on October 8, 2026.

Source: sparklabx/drawio-ai-kit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.