anatomy
Enables automated generation of technical documentation and training materials through interactive visualizations, reducing design time and improving knowled…
An agent skill for creating detailed, interactive isometric illustrations of real-world machines and complex systems. Generates polished SVGs with optional WebGL shaders and 3D effects.
- Generate interactive isometric diagrams of server architectures for technical documentation.
- Create detailed SVG illustrations of manufacturing equipment for training materials.
- Build 3D visual walkthroughs of system components for engineering presentations.
Enables automated generation of technical documentation and training materials through interactive visualizations, reducing design time and improving knowledge transfer for complex system architecture without external design tools.
Engineering teams documenting mechanical systems, infrastructure, or product architecture who need scalable technical illustration capabilities.
https://github.com/wheresryan22/anatomy
By wheresryan22
How to Get It
claude plugins install wheresryan22/anatomy
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Help me generate interactive isometric diagrams of server architectures for technical documentation
Trust Signals Auto-scanned
Community Pulse Active
Discussed on Hacker News
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3 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 7,056 GitHub stars; contributors unknown; last commit 0d ago; license MIT.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
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Evaluation
Scored from trust signals (evidence-eval-v1): 7,056 GitHub stars; contributors unknown; last commit 0d ago; license MIT.