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Found 1,204 Skills
Use for "interrogate", "adversarial review", "multi-model review", "challenge this", "stress test this code", "find blind spots", or "tear this apart". Four LLM reviewers challenge changes from independent angles.
Router skill for LLMQuant risk workflows. Use when the user needs fear scoring, VIX regime, hedge design, or research health checks.
Router skill for LLMQuant prediction-market workflows. Use when the user needs event odds, settlement criteria, probability gaps, cross-market pricing, or prediction-market arbitrage review.
Searching internet for technical documentation using llms.txt standard, GitHub repositories via Repomix, and parallel exploration. Use when user needs: (1) Latest documentation for libraries/frameworks, (2) Documentation in llms.txt format, (3) GitHub repository analysis, (4) Documentation without direct llms.txt support, (5) Multiple documentation sources in parallel
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.
Perform 12-Factor Agents compliance analysis on any codebase. Use when evaluating agent architecture, reviewing LLM-powered systems, or auditing agentic applications against the 12-Factor methodology.
Production-grade fault tolerance for distributed systems. Use when implementing circuit breakers, retry with exponential backoff, bulkhead isolation patterns, or building resilience into LLM API integrations.
Design MCP resources to expose content for LLM consumption. Use when creating static or dynamic resources in xmcp.
LLM-based deep iterative search and reasoning service. Specializes in handling complex problems, automatically decomposing queries, conducting multi-round iterative retrieval, evaluating and verifying information, and finally generating comprehensive and structured deep analysis reports.
LLM guardrails with NeMo, Guardrails AI, and OpenAI. Input/output rails, hallucination prevention, fact-checking, toxicity detection, red-teaming patterns. Use when building LLM guardrails, safety checks, or red-team workflows.
Amazon Bedrock Agents for building autonomous AI agents with foundation model orchestration, action groups, knowledge bases, and session management. Use when creating AI agents, orchestrating multi-step workflows, integrating tools with LLMs, building conversational agents, implementing RAG patterns, managing agent sessions, deploying production agents, or connecting knowledge bases to agents.
Core technical documentation writing principles for voice, tone, structure, and LLM-friendly patterns. Use when writing or reviewing any documentation.