Prompt Engineering

A comprehensive prompt engineering skill that teaches advanced techniques like chain-of-thought, few-shot learning, structured outputs, and prompt optimization. Includes reference documentation, template libraries, and a Python script for A/B testing prompts to improve LLM performance.

A comprehensive plugin for building production-ready LLM applications, including RAG systems with vector databases, AI agents using LangGraph, advanced prompt engineering patterns, and embedding strategies. It provides templates and best practices for integrating with Pinecone, Qdrant, pgvector, and other vector stores.

A detailed reference skill for designing agent-native software architectures. Provides patterns for tool design, file-based workspaces, mobile execution, self-modification with safety guardrails, and progressive disclosure of agent capabilities. Use when building apps where features are outcomes achieved by agents operating in loops.

Create optimized prompts for Claude-to-Claude pipelines with research, planning, and execution stages. Use when building prompts that produce outputs for other prompts to consume, or when running multi-stage workflows (research -> plan -> implement).

A comprehensive guide for building AI-first applications using LLMs. Covers project structure, typed LLM client patterns, prompt versioning, testing strategies (mocks, fixtures, evaluations), CI/CD integration for LLM tests, and cost/performance tracking. Focuses on the principle of using LLMs for logic and traditional code for infrastructure.

Use this skill when you writing commands, hooks, skills for Agent, or prompts for sub agents or any other LLM interaction, including optimizing prompts, improving LLM outputs, or designing production prompt templates.

Prompt Coach analyzes your Claude Code session logs stored locally on your machine to help you become a better AI-native engineer. It provides insights on token usage and costs, prompt quality scoring with real examples, tool usage patterns, productivity time analysis, and actionable recommendations for improvement.