Skip to content

rahulsub/code-practices-plugin

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 

Repository files navigation

Code Practices Plugin for Claude Code

A collection of coding best practices for LLM-assisted development. These skills help you and your team write better code with Claude Code by applying proven patterns from Karpathy's insights and Anthropic's official best practices.

Installation

From GitHub

/plugin marketplace add rahulsub/code-practices-plugin

Local Development

/plugin marketplace add ~/code-practices-plugin

Skills Included

Development Workflow

Skill Command Description
Explore-Plan-Code /explore-plan-code Read files → plan → approve → implement workflow
Goal-Driven /goal-driven Define success criteria instead of step-by-step instructions
Test-First /test-first Write failing tests before implementation
Naive-Then-Optimize /naive-then-optimize Implement obvious solution first, then optimize

Code Quality

Skill Command Description
Code-Review /code-review Review code like a skeptical senior engineer
Simplify /simplify Remove over-engineering and unnecessary abstractions
Cleanup /cleanup Systematically remove dead code and tech debt
Surface-Assumptions /surface-assumptions Identify and validate hidden assumptions
Tradeoffs /tradeoffs Present multiple approaches with explicit tradeoffs

Advanced Workflows

Skill Command Description
Visual-Iteration /visual-iteration UI feedback loop with screenshots
Context-Management /context-management Manage long sessions with /clear, checklists, subagents
Multi-Claude /multi-claude Run parallel Claude instances for writer+reviewer patterns
Headless-Automation /headless-automation CI/CD integration, fan-out migrations, pipelines

Usage Examples

Before implementing a feature

/explore-plan-code

Claude will read relevant files, create a plan, and wait for your approval before coding.

After writing code

/code-review

Review the changes for hidden assumptions, over-engineering, and edge cases.

For complex algorithms

/naive-then-optimize

Implement the obvious correct solution first, then optimize while preserving behavior.

Attribution

These practices are based on:

License

MIT

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

 
 
 

Contributors