Agent skill · DevOps & Cloud

generating-gcp-diagrams

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.

a5c-aigithub.com/a5c-aiGitHub ↗
claude-codecodexcan modify filesships scriptsMIT
Install
npx skills add a5c-ai/babysitter --skill generating-gcp-diagrams --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 22
SKILL.md size: 15 KB
Bundled scripts: yes
Allowed tools: ReadWrite
Requires: Requires image analysis capability for image conversion. Scripts require Python 3 and Bash. DrawIO Desktop optional for…
Path: library/specializations/devops-sre-platform/skills/generating-gcp-diagrams/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 1,642
Language: JavaScript

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# GCP DrawIO Diagram Generator Generates professional DrawIO XML diagrams for Google Cloud Platform architectures. ## Capabilities 1. **Extract** - Analyze existing DrawIO XML files to identify GCP shapes, connections, and structure 2. **Identify** - Recognize GCP service icons from architecture diagram images 3. **Generate** - Create valid DrawIO XML from images or text descriptions 4. **Convert** - Transform GCP architecture diagrams into editable DrawIO format ## Quick Reference ### GCP Shape Pattern ``` shape=mxgraph.gcp2.{service_name} ``` **Note:** GCP uses **snake_case** for shape names (e.g., `cloud_run`, `cloud_sql`, `cloud_storage`). **Icon Pattern:** Unlike AWS, GCP uses a **single icon pattern** for all services — there is no service vs instance icon distinction. The same `shape=mxgraph.gcp2.{name}` is used whether labeling the service itself ("Cloud Run") or a specific instance ("Cloud Run (API Handler)"). Differentiate by label text only. ### Common GCP Services | Service | Shape Code | |---------|-----------| | Cloud Run | `mxgraph.gcp2.cloud_run` | | BigQuery | `mxgraph.gcp2.bigquery` | | Cloud Storage | `mxgraph.gcp2.cloud_storage` | | Vertex AI | `mxgraph.gcp2.clo

What's inside
Steps it walks through
  1. Capabilities
  2. Quick Reference
  3. GCP Shape Pattern
  4. Common GCP Services
  5. GCP Container Types
  6. Task 1: Analyze a DrawIO File
  7. Steps
  8. Input
  9. Output
  10. Task 2: Convert Image to DrawIO
  11. Confidence Report Format
  12. Task 3: Create DrawIO from Description
  13. Example Input
  14. Example Output Structure
Ships with 21 files
  • 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
  • scripts/fix-drawio-icons.py
  • scripts/fix-gcp-icons.py
  • scripts/open-diagram.sh
  • scripts/validate-drawio.py
  • scripts/validate-gcp-icons.py
More from babysitter
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About this skill
What does the generating-gcp-diagrams skill do?

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.

How do I install it?

Run `npx skills add a5c-ai/babysitter --skill generating-gcp-diagrams --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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