AWS Architecture Diagram
vidanov/aws-architecture-diagram-skill
Always use when user asks to create, generate, or build an AWS architecture diagram, cloud infrastructure diagram, or system diagram with AWS services.
A skill your agent uses when the user asks for an AWS architecture diagram — VPC/networking, event-driven, landing zone, multi-AZ, serverless pipeline, or any diagram built with AWS service icons.
Install Drawio AWS
$ npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sparklabx/drawio-ai-kit drawio-aws --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sparklabx/drawio-ai-kit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/drawio-aws .claude/skills/drawio-aws && rm -rf skills-srcUse ~/.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/
Install the "drawio-aws" agent skill from https://github.com/sparklabx/drawio-ai-kit/tree/main/skills/drawio-aws into .claude/skills/drawio-aws/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drawio-aws", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sparklabx/drawio-ai-kit/tree/main/skills/drawio-awsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sparklabx/drawio-ai-kit drawio-aws --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sparklabx/drawio-ai-kit.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/drawio-aws .agents/skills/drawio-aws && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "drawio-aws" agent skill from https://github.com/sparklabx/drawio-ai-kit/tree/main/skills/drawio-aws into .agents/skills/drawio-aws/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drawio-aws", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sparklabx/drawio-ai-kit drawio-aws --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sparklabx/drawio-ai-kit.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/drawio-aws .cursor/skills/drawio-aws && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "drawio-aws" agent skill from https://github.com/sparklabx/drawio-ai-kit/tree/main/skills/drawio-aws into .cursor/skills/drawio-aws/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drawio-aws", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sparklabx/drawio-ai-kit.git --path skills/drawio-aws--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sparklabx/drawio-ai-kit drawio-aws --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sparklabx/drawio-ai-kit.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/drawio-aws .gemini/skills/drawio-aws && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "drawio-aws" agent skill from https://github.com/sparklabx/drawio-ai-kit/tree/main/skills/drawio-aws into .gemini/skills/drawio-aws/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drawio-aws", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sparklabx/drawio-ai-kit drawio-awsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sparklabx/drawio-ai-kit.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/drawio-aws .github/skills/drawio-aws && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "drawio-aws" agent skill from https://github.com/sparklabx/drawio-ai-kit/tree/main/skills/drawio-aws into .github/skills/drawio-aws/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drawio-aws", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sparklabx/drawio-ai-kit drawio-aws --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sparklabx/drawio-ai-kit.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/drawio-aws .opencode/skills/drawio-aws && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "drawio-aws" agent skill from https://github.com/sparklabx/drawio-ai-kit/tree/main/skills/drawio-aws into .opencode/skills/drawio-aws/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "drawio-aws", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
drawio-aws- GitHub stars
- 654
- Token cost
- ~1.6k tokens
- SKILL.md length
- 512 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 an AWS architecture diagram — VPC/networking, event-driven, landing zone, multi-AZ, serverless pipeline, or any diagram built with AWS service icons.
- Works in 2 steps: Preflight — the CLI must be installed → Delegate the build (preferred when your…
- The user asks for an AWS architecture diagram — VPC/networking
- 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 and aws
What it does
Drawio AWS is an agent skill from sparklabx/drawio-ai-kit. Use when the user asks for an AWS architecture diagram — VPC/networking, event-driven, landing zone, multi-AZ, serverless pipeline, or any diagram built with AWS service icons. Builds with the declarative layout engine using ground-truth mxgraph.aws4 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 Backend & APIs, covering Diagrams, Cloud architecture and Verification before completion. It works with draw.io and Amazon Web Services. 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 an AWS architecture diagram — VPC/networking
- Serverless pipeline
- Any diagram built with AWS service icons
Example prompts
- “/drawio-aws”
Requirements
- Node.js
Workflow steps
2 steps, taken from the step headings in SKILL.md.
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:
npmaws
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md. Its commands use npm and aws, 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 AWS loads about 1.6k tokens when it runs. Until then it costs about 102 tokens; SKILL.md has 512 words of instructions outside code blocks.
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). 512 words, ~1,600 tokens.
Download SKILL.mdSave it as
.claude/skills/drawio-aws/SKILL.md (or your agent's skills folder).- name
- drawio-aws
- description
- Use when the user asks for an AWS architecture diagram — VPC/networking, event-driven, landing zone, multi-AZ, serverless pipeline, or any diagram built with AWS service icons. Builds with the declarative layout engine using ground-truth mxgraph.aws4 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 AWS
Produce correct AWS 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
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 an AWS 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 aws` — 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 (199 more words)Show less
Inline path (no subagent support)
1. Shared Workflow
drawio-ai workflowPrints 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
drawio-ai principles --mode awsReturns the AWS 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):
ROOT="$(drawio-ai root)" # absolute path to the installed KitBuild 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
Container nesting order: AWS Cloud → Region → VPC → AZ → Subnet → SG.
Managed/global services (CloudFront, Route 53, S3, DynamoDB, SQS/SNS)
sit outside the VPC — they are not subnet-resident. Category colors from the
catalog are authoritative; never recolor AWS icons.
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 rendervision 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-aws of sparklabx/drawio-ai-kit.
Open the folder on GitHubat commit 1a03d87
Compare with similar skills
Drawio AWS 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Drawio AWS this skillsparklabx/drawio-ai-kit | 654 | — | ~1.6k | Automated safety check: Pass | MIT | |
| AWS Architecture Diagramvidanov/aws-architecture-diagram-skill | 160 | — | ~4.4k | Automated safety check: Pass | MIT | |
| AWS Drawio Architecture Diagramsgiuseppe-trisciuoglio/developer-kit | 356 | — | ~3.1k | Automated safety check: Notes | MIT | |
| AWS Architecture Diagramvidanov/aws-architecture-diagram-skill | 160 | — | ~4.9k | Automated safety check: Pass | MIT | |
| AWS DrawIO Diagram Generatora5c-ai/babysitter | 1.8k | — | ~4.2k | Automated safety check: Pass | MIT | |
| AWS Architecture Diagramawslabs/agent-plugins | 915 | — | ~3.8k | Automated safety check: Notes | Apache-2.0 |
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Questions about Drawio AWS
What does Drawio AWS do?
A skill your agent uses when the user asks for an AWS architecture diagram — VPC/networking, event-driven, landing zone, multi-AZ, serverless pipeline, or any diagram built with AWS service icons. Drawio AWS is an agent skill from sparklabx/drawio-ai-kit. Use when the user asks for an AWS architecture diagram — VPC/networking, event-driven, landing zone, multi-AZ, serverless pipeline, or any diagram built with AWS service icons.
When should I use Drawio AWS?
Drawio AWS fits situations like: the user asks for an AWS architecture diagram — VPC/networking; serverless pipeline; any diagram built with AWS service icons.
How do I install Drawio AWS in Claude Code?
Run `npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a claude-code`. Or copy the skill folder (skills/drawio-aws in sparklabx/drawio-ai-kit) into .claude/skills/drawio-aws in your project. Claude Code loads it when a task matches its description.
How do I install Drawio AWS in Codex?
Run `npx skills add sparklabx/drawio-ai-kit --skill drawio-aws -a codex`. Or copy the skill folder (skills/drawio-aws in sparklabx/drawio-ai-kit) into .agents/skills/drawio-aws in your project. Codex loads it when a task matches its description.
Can I use Drawio AWS 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-aws -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-aws, .gemini/skills/drawio-aws, .github/skills/drawio-aws and .opencode/skills/drawio-aws in your project.
What does Drawio AWS need to run?
Going by SKILL.md and its folder, Drawio AWS needs the command-line tools its instructions call (npm and aws). Our summary lists: Node.js.
Does Drawio AWS 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 AWS 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 AWS use?
Drawio AWS 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 AWS 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 AWS?
Skills that share tags, products or a category with Drawio AWS: AWS Architecture Diagram (vidanov/aws-architecture-diagram-skill, 160 stars), AWS Drawio Architecture Diagrams (giuseppe-trisciuoglio/developer-kit, 356 stars), AWS Architecture Diagram (vidanov/aws-architecture-diagram-skill, 160 stars) and AWS DrawIO Diagram Generator (a5c-ai/babysitter, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Drawio AWS?
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.