Claude Code Skills Repository

Remove signs of AI-generated writing from text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide maintained by WikiProject AI Cleanup. Detects and fixes 24 patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words (delve, tapestry, landscape), negative parallelisms, sycophantic tone, and generic conclusions.

Systematically debugs issues by analyzing error messages, stack traces, and code flow. Adds strategic logging, identifies root causes, and suggests fixes.

Generates comprehensive unit tests for functions and classes. Covers happy paths, edge cases, error conditions, and boundary values. Supports Jest, Pytest, JUnit, and other frameworks.

Performs thorough code reviews checking for bugs, security issues, performance problems, and style inconsistencies. Provides actionable feedback with specific line references.

Automatically generates meaningful, conventional commit messages by analyzing staged changes. Follows the Conventional Commits specification with type prefixes like feat, fix, docs, refactor, etc.

Remove signs of AI-generated writing from text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide maintained by WikiProject AI Cleanup. Detects and fixes 24 patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words (delve, tapestry, landscape), negative parallelisms, sycophantic tone, and generic conclusions.

Manages environment variables across different environments. Creates .env files, validates required variables, and generates TypeScript types for type-safe access.

Analyzes and optimizes SQL queries for better performance. Suggests indexes, rewrites queries, and explains execution plans.

Creates GitHub Actions CI/CD workflows for testing, building, and deploying applications. Includes caching, matrix builds, and deployment steps.

Generates realistic mock data for testing and development. Creates users, products, orders, and other entities with proper relationships and valid data.

Explains complex code in simple terms. Breaks down algorithms, design patterns, and tricky logic with line-by-line annotations and diagrams.

Creates and explains cron expressions for scheduled tasks. Converts natural language to cron syntax and provides next run times.

Generates comprehensive pull request descriptions by analyzing commits and changes. Includes summary, changes list, testing instructions, and screenshots placeholders.

Generates typed API client code from OpenAPI/Swagger specs or by analyzing existing API endpoints. Creates methods for each endpoint with proper typing.

Creates Docker and docker-compose configurations for development and production environments. Includes proper networking, volumes, health checks, and environment variables.

Creates and explains regular expressions for common patterns. Provides test cases, explains each component, and handles edge cases properly.

Analyzes log files to identify errors, patterns, and anomalies. Summarizes issues, tracks error frequency, and helps identify root causes of problems.

Remove signs of AI-generated writing from text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide maintained by WikiProject AI Cleanup. Detects and fixes 24 patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words (delve, tapestry, landscape), negative parallelisms, sycophantic tone, and generic conclusions.

Generates typed API client code from OpenAPI/Swagger specs or by analyzing existing API endpoints. Creates methods for each endpoint with proper typing.

Generates React components with proper structure including TypeScript types, props interface, hooks, styles, and tests. Follows best practices and project conventions.

Creates Docker and docker-compose configurations for development and production environments. Includes proper networking, volumes, health checks, and environment variables.

Creates and explains regular expressions for common patterns. Provides test cases, explains each component, and handles edge cases properly.

Analyzes log files to identify errors, patterns, and anomalies. Summarizes issues, tracks error frequency, and helps identify root causes of problems.