Results for “faceted-search”

12 skills

search-term-matrices

Strategic search planning for agent-driven research. Generates structured search-term matrices with tiered fallback strategies, engine-specific operators, and grading criteria before executing any searches. Use this skill whenever research requires more than a single search query — comparing technologies, verifying claims across sources, surveying a landscape, investigating a multi-faceted question, or building evidence for a decision. Do NOT use for quick factual lookups, fetching a single known URL, or questions answerable from a single source. Covers tech, academic, regulatory, and general domains. Think of it as "research planning" — the matrix is the plan, execution comes after.

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qdrant-search-strategies

Guides selection of Qdrant search strategies including hybrid search, reranking, relevance feedback, MMR, and discovery APIs to improve retrieval quality.

qdrant-search-quality

Diagnoses and improves Qdrant search relevance by isolating embedding model, configuration, or query strategy issues.

agent-voice-search

Expert en optimisation recherche vocale (structured data, featured snippets, queries conversationnelles)

orchestra-research

Enables fast similarity search and clustering of dense vectors using FAISS, supporting billions of vectors, GPU acceleration, and various index types.

10.4k · bundle

search-specialist

Expert web researcher using advanced search techniques and synthesis. Masters search operators, result filtering, and multi-source verification. Handles competitive analysis and fact-checking. Use PROACTIVELY for deep research, information gathering, or trend analysis.

orchestra-research

qdrant-vector-search

Build production RAG and semantic search systems with a high-performance vector database written in Rust, supporting hybrid search, filtering, and horizontal scaling.

10.4k · bundle

lucassantana-dev

Semantic-search personal knowledge (memory, plans, handoffs, skills, Codex rules) via the local RAG index at ~/.claude/rag-index/. Use when a query is fuzzy or cross-file ("how did we fix X", "what did we decide about Y", "which skill handles Z"). Complements grep (exact) and Serena (code symbols). If the user asks a recall question that doesn't map to a specific known file, reach here first.

Manages vector embeddings with Pinecone for semantic search, recommendation, and RAG pipelines.

Karpathy's LLM Wiki: build/query interlinked markdown KB.

Routes a research topic through a structured two-phase workflow: generate an extensible outline, then fan out parallel web-search agents to investigate each item into validated JSON, producing a complete markdown report with table of contents.