GCP DrawIO Diagram Generator

What it does

When your agent uses it

• Drawing a GCP architecture diagram from a text description
• Converting a screenshot of a GCP diagram into an editable DrawIO file
• Listing the GCP components and connections in an existing .drawio file

Example prompts

• “Draw a DrawIO diagram of a Cloud Run service reading from Pub/Sub and writing to BigQuery.”
• “Convert architecture.png into an editable DrawIO file using GCP icons.”
• “List every GCP component and connection in infra/platform.drawio.”

Requirements

• Python 3 and Bash for the helper scripts
• Image analysis capability for converting images
• DrawIO Desktop, optional, for validation
• Compatibility (from SKILL.md): Requires image analysis capability for image conversion. Scripts require Python 3 and Bash. DrawIO Desktop optional for validation.
• Pre-approved tools (allowed-tools): Read, Write

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Extract - Analyze existing DrawIO XML files to identify GCP shapes, connections, and structure
2. 2Identify - Recognize GCP service icons from architecture diagram images
3. 3Generate - Create valid DrawIO XML from images or text descriptions
4. 4Convert - Transform GCP architecture diagrams into editable DrawIO format

What it can do on your machine

Read from SKILL.md and the folder at commit feb68ab. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Write

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships 3 files in scripts/ (Python and Shell, from the files we listed), which the agent can run.

  Shell commands in SKILL.md call:

  • python
  • brew

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

• Network

  No URLs in SKILL.md.

  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.

• Compatibility

  Requires image analysis capability for image conversion. Scripts require Python 3 and Bash. DrawIO Desktop optional for validation.

  From compatibility in the SKILL.md frontmatter.

Context cost

GCP DrawIO Diagram Generator loads about 3.7k tokens when it runs, and up to ~16k if it reads all its reference files. Until then it costs about 81 tokens; SKILL.md has 1,255 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~81

When it runs · the whole SKILL.md, loaded when a task matches

~3.7k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~16k

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

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); the scripts in this folder are not scanned.

SKILL.md

The full file from a5c-ai/babysitter at commit feb68ab, republished under its MIT licence (© a5c-ai). 1,255 words, ~3,725 tokens.

name

generating-gcp-diagrams

description

Generates DrawIO XML diagrams for Google Cloud Platform architectures from text descriptions or images. Analyzes existing .drawio files to extract GCP components. Use for GCP architecture diagrams, cloud infrastructure documentation, or when converting GCP diagram images to editable DrawIO format.

allowed-tools

Read, Write

compatibility

Requires image analysis capability for image conversion. Scripts require Python 3 and Bash. DrawIO Desktop optional for validation.

license

MIT

graph.domains

domain:devops

graph.specializations

specialization:devops-sre-platform

graph.skillAreas

skill-area:deployment-infrastructure-management

graph.roles

role:platform-engineer, role:architect

graph.topics

topic:infrastructure-as-code

shape=mxgraph.gcp2.{service_name}

# DrawIO Analysis Report

## Summary
- Total shapes: X
- Total connections: Y
- Containers: Z

## Shape Inventory

| ID | Label | Type | Position | Parent |
|----|-------|------|----------|--------|
| abc | Cloud Run | mxgraph.gcp2.cloud_run | (100,200) | vpc1 |

## Connection Matrix

| From | To | Label | Type |
|------|-----|-------|------|
| Cloud Run | BigQuery | API | solid |

## Container Hierarchy

- VPC-SC (vpc1)
  - Cloud Run (run1)
  - Cloud Run (run2)
  - BigQuery (bq1)

## Style Analysis

### Unique Shapes Found
- mxgraph.gcp2.cloud_run (4 instances)
- mxgraph.gcp2.bigquery (2 instances)

# Conversion Confidence Report

## Overall Confidence: 85%

## Identified Components

### High Confidence (>90%)
- Cloud Run x4 - Clear icon match
- BigQuery x2 - Clear icon match
- VPC-SC container - Green border, correct label

### Medium Confidence (70-90%)
- Vertex AI Search - Icon similar, label confirms

### Low Confidence (<70%)
- Unknown icon at position (300, 400) - Mapped to generic service

## Connection Accuracy
- 12/14 connections clearly visible
- 2 connections inferred from layout

## Notes
- "Same Instance" dashed container identified
- Bidirectional arrows on 3 connections

Create a GCP architecture with:
- VPC-SC container
- Cloud Scheduler triggering Cloud Run
- Cloud Run connecting to BigQuery and Cloud Storage
- Vertex AI Search connected to BigQuery

<mxfile ...>
  <diagram name="GCP Architecture">
    <mxGraphModel ...>
      <root>
        <mxCell id="0" />
        <mxCell id="1" parent="0" />
        <!-- VPC-SC Container -->
        <mxCell id="vpc" value="VPC-SC" style="..." vertex="1" parent="1">
          <mxGeometry x="50" y="50" width="700" height="400" />
        </mxCell>
        <!-- Cloud Scheduler -->
        <mxCell id="sched" value="Cloud Scheduler" style="...mxgraph.gcp2.cloud_scheduler" vertex="1" parent="vpc">
          <mxGeometry x="50" y="100" width="50" height="50" />
        </mxCell>
        <!-- More shapes... -->
        <!-- Connections -->
        <mxCell id="conn1" edge="1" source="sched" target="run" style="..." />
      </root>
    </mxGraphModel>
  </diagram>
</mxfile>

labelBackgroundColor=#FFFFFF;fontSize=10;fontColor=#333333;

© a5c-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 21 other files (scripts, references, assets) in library/specializations/devops-sre-platform/skills/generating-gcp-diagrams of a5c-ai/babysitter.

• SKILL.md
• README.md
• assets/ICON-COMPATIBILITY.md
• assets/containers.json
• assets/gcp-icons.json
• assets/templates/connection-template.xml
• assets/templates/drawio-base.xml
• assets/templates/node-template.xml
• references/DIAGRAM-BEST-PRACTICES.md
• references/ICON-COMPATIBILITY.md
• references/coordinate-system.md
• references/style-guide.md
• references/xml-examples.md
• references/xml-parser-guide.md
• scripts/analyze-existing.py
• scripts/export-diagram.sh
• scripts/extract-shape-names.py
• … and 5 more

Open the folder on GitHubat commit feb68ab