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Found 1,211 Skills
Use when "DSPy", "declarative prompting", "automatic prompt optimization", "Stanford NLP", or asking about "optimizing prompts", "prompt compilation", "modular LLM programming", "chain of thought", "few-shot learning"
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
Hub skill for requirements elicitation. Provides technique selection, orchestration guidance, LLMREI patterns, and autonomy level configuration. Use when gathering requirements from stakeholders, conducting elicitation sessions, or preparing requirements for specification.
Optimize LLM prompts, tools, and agents in Opik using standardized optimizer workflows (prompt optimization, tool optimization, and parameter tuning), dataset/metric wiring, and result interpretation.
Reduces LLM costs and improves response times through caching, model selection, batching, and prompt optimization. Provides cost breakdowns, latency hotspots, and configuration recommendations. Use for "cost reduction", "performance optimization", "latency improvement", or "efficiency".
Build a structured taxonomy of failure modes from open-coded trace annotations. Use this skill whenever the user has freeform annotations from reviewing LLM traces and wants to cluster them into a coherent, non-overlapping set of binary failure categories (axial coding). Also use when the user mentions "failure modes", "error taxonomy", "axial coding", "cluster annotations", "categorize errors", "failure analysis", or wants to go from raw observation notes to structured evaluation criteria. This skill covers the full pipeline: grouping open codes, defining failure modes, re-labeling traces, and quantifying error rates.
Extract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection.
Help the user systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.
Use when you want rubric based LLM quality scoring on generated outputs; pair with addon-deterministic-eval-suite.
Compress documents for LLM token efficiency while preserving semantic content. Use when asked to compress, compact, shrink, or optimize a document, CLAUDE.md, system prompt, skill file, or any text for fewer tokens. Also use when the user mentions token count, token budget, context window limits, or wants to make prompts shorter for cost savings.
Agent tracing CLI for inspecting agent execution snapshots. Use when user mentions 'agent-tracing', 'trace', 'snapshot', wants to debug agent execution, inspect LLM calls, view context engine data, or analyze agent steps. Triggers on agent debugging, trace inspection, or execution analysis tasks.
Terminal tool that detects your hardware and recommends which LLM models will actually run well on your system