Llm

Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.

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.

DSPy Ruby is a comprehensive guide for building LLM-powered Ruby applications using the DSPy.rb framework. It provides type-safe signatures, composable modules, multi-provider support (OpenAI, Anthropic, Gemini, Ollama), and patterns for testing, optimization, and production monitoring of AI applications.

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.

This skill provides a quick reference guide for the latest AI models across major providers. It includes model IDs, pricing comparisons, usage examples, and selection recommendations for tasks like text generation, code completion, image generation, voice synthesis, and embeddings.

A comprehensive guide for building autonomous AI agents using Pydantic AI (Python) and Claude Agent SDK (Node.js). Covers agent architecture, tool design, multi-agent coordination, memory management, guardrails, and testing patterns with practical code examples.

A comprehensive reference guide for understanding and optimizing context in LLM agent systems. Covers context anatomy, degradation patterns, multi-agent verification workflows, and optimization techniques like compaction and observation masking.

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.