search▌

Category: test

Web search with LLM-optimized results, content snippets, and relevance scores. \n \n Supports four search depths (ultra-fast, fast, basic, advanced) with configurable result counts up to 20, plus domain filtering and time-range constraints \n Returns structured JSON output with content snippets, relevance scores, and metadata optimized for LLM consumption \n Includes specialized search modes for news and finance topics, with optional AI-generated answers and full page content extraction \n Integ

Search Mathlib for lemmas by type signature pattern.

This skill provides tips and best practices for writing effective semantic search queries.

Systematize research-before-coding by searching existing tools, libraries, and patterns before writing custom code. \n \n Provides a five-phase workflow: need analysis, parallel search across npm/PyPI/MCP/GitHub, evaluation, decision (adopt/extend/compose/build), and implementation \n Includes a decision matrix to score candidates on functionality, maintenance, community, docs, license, and dependencies \n Offers search shortcuts organized by category (development tooling, AI/LLM integration, da

Category: provider

$21

Web search with optional full-page content extraction from results. \n \n Returns real search results as JSON with optional --scrape flag to fetch complete page markdown for each result, avoiding redundant fetches \n Supports filtering by source type (web, images, news), category (GitHub, research, PDF), time range (past hour/day/week/month/year), location, and country \n Use --limit to control result count and --scrape-formats to customize output formats when extracting full content \n Part of

Search and navigate large codebases with semantic search, grep patterns, and file discovery. \n \n Supports three search modes: semantic search for conceptual queries, grep for exact text and regex patterns, and glob for file discovery by type or naming convention \n Includes workflow guidance for common scenarios like tracing function calls, understanding feature implementations, locating bugs, and performing impact analysis \n Provides language-specific patterns for Python, JavaScript, TypeScr

Combine vector and keyword search for improved retrieval in RAG systems and search engines. \n \n Provides four fusion methods: Reciprocal Rank Fusion (RRF) for general use, linear combination for tunable balance, cross-encoder reranking for highest quality, and cascade filtering for efficiency \n Includes production-ready templates for PostgreSQL with pgvector, Elasticsearch with dense vectors, and custom Python pipelines with parallel search execution \n Handles score normalization, metadata f