Optimize markdown files for LLM consumption by adding YAML front-matter with metadata and TOC, normalizing heading hierarchy, removing noise and redundancy, converting verbose prose to structured...

Defense techniques against prompt injection attacks including direct injection, indirect injection, and jailbreaks - theUse when "prompt injection, jailbreak prevention, input sanitization, llm...

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rotUse when "context window, token limit, context management, context engineering, long...

Scrapes and crawls web pages, converting them to clean markdown or structured JSON for LLM consumption. Use when needing to extract content from URLs, crawl entire websites, map site structure,...

Implement comprehensive observability for LLM applications including tracing (Langfuse/Helicone), cost tracking, token optimization, RAG evaluation metrics (RAGAS), hallucination detection, and...

Prompt engineering and optimization for AI/LLMs. Capabilities: transform unclear prompts, reduce token usage, improve structure, add constraints, optimize for specific models, backward-compatible...

Persistent memory systems for LLM conversations including short-term, long-term, and entity-based memoryUse when "conversation memory, remember, memory persistence, long-term memory, chat history,...

Comprehensive prompt engineering framework for designing, optimizing, and iterating LLM prompts. This skill should be used when users request prompt creation, optimization, or improvement for any...

Patterns for coordinating multiple LLM agents including sequential, parallel, router, and hierarchical architectures—the AI equivalent of microservicesUse when "multi-agent, agent orchestration,...

This skill should be used when identifying, analyzing, and mitigating security risks in Artificial Intelligence systems using the CoSAI (Coalition for Secure AI) Risk Map framework. Use when...

Expert in getting reliable, typed outputs from LLMs. Covers JSON mode, function calling, Instructor library, Outlines for constrained generation, Pydantic validation, and response format...

Genera documentación llms.txt optimizada para LLMs. Usa cuando el usuario diga "crear llms.txt", "documentar para AI", "crear documentación para LLMs", "generar docs para modelos", o quiera hacer...

Create a Mastra project using create-mastra and smoke test the studio in Chrome

>

Help users build effective AI applications. Use when someone is building with LLMs, writing prompts, designing AI features, implementing RAG, creating agents, running evals, or trying to improve...

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots,...

This skill should be used when the user asks to "evaluate a DSPy program", "test my DSPy module", "measure performance", "create evaluation metrics", "use answer_exact_match or SemanticF1",...

Build production-ready LLM applications, advanced RAG systems, and

Build production-ready LLM applications, advanced RAG systems, and

Build production-ready LLM applications, advanced RAG systems, and