Compare agent skills before installing.

Guides designing retrieval-augmented generation (RAG) pipelines: document chunking, embedding, indexing, retrieval, and grounding LLM output in retrieved content. Use when a user asks to "build a RAG pipeline," "the agent hallucinates facts it should know from our docs," "improve retrieval relevance," "chunk documents for embedding," or needs to ground an agent's answers in a private/internal knowledge base rather than a chatbot with an open-book connection to arbitrary web content.

Bypass an LLM's safety/guardrails to make it produce restricted output or ignore its policy. Load when testing an AI product's content controls, "jailbreak", "guardrail bypass", refusal testing, or safety evals. Signals: a chatbot/assistant with a usage policy, refusals to test, content filters.

Unbounded-consumption / denial-of-wallet attacks on LLM apps — force runaway tokens, cost, or latency. Load when testing an LLM product's limits/billing, on "LLM DoS", cost amplification, or resource exhaustion. Signals: user-controlled prompts/max_tokens, agent loops, no rate/'cost caps'.