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nategarelik AI Skills

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Browse published AI skills maintained by nategarelik. Compare skill descriptions and follow each source repository for setup instructions.

Author statistics

Indexed skills

15

Repository stars across indexed skills

0

Repository forks

0

Average repository stars per indexed skill

0

Repository stars may repeat across skills from the same repository.

15 skills

Browse skills

GitHub

powershell-patterns

nategarelik

PowerShell best practices for Windows development, including cmdlet patterns, module development, and error handling

GitHub

docker-kubernetes

nategarelik

Dockerfile best practices, multi-stage builds, docker-compose patterns, and Kubernetes concepts

GitHub

api-design

nategarelik

REST API best practices, OpenAPI/Swagger patterns, authentication, and error response formats

GitHub

testing-patterns

nategarelik

Auto-load when writing tests. Provides TDD workflow, test structure patterns, and common testing idioms.

GitHub

git-workflow

nategarelik

Auto-load for git operations. Provides branching strategy, commit conventions, and PR workflow.

GitHub

composition-data-modeling

nategarelik

Design and work with composition data structures. Use when defining schemas, creating database models, building API responses, or transforming composition data between formats.

GitHub

cern-detector-ui

nategarelik

Design system for the CERN Particle Detector aesthetic. Use when building UI components, styling elements, or implementing the detector workstation interface.

GitHub

composition-ai-research

nategarelik

Research product compositions using AI and web sources. Use when needing to find what products, substances, or objects are made of - including ingredients, materials, chemicals, and elements. Essential for buil...

GitHub

3d-composition-visualization

nategarelik

Build 3D visualizations using Three.js and React Three Fiber. Use when implementing 3D components, debugging rendering issues, optimizing performance, or creating interactive visualization features.

The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.