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Found 206 Skills
Expert blueprint for GDSkills skill discovery and indexing system. Enables AI agents to find relevant skills by topic/keyword. Use when building skill libraries OR implementing search functionality. Keywords skill discovery, indexing, search, metadata, skill registry.
Semantic search, context management, and document indexing via OpenViking. Use when the user asks to: index/import documents or files into a knowledge base, perform semantic search across indexed content, browse or explore indexed resources, get summaries/overviews of indexed documents, manage an OpenViking instance, or integrate structured context retrieval into workflows. Also use when sub-agents need to retrieve relevant context from a large document collection.
Skills covering Upstash Search quick starts, core concepts, and TypeScript/JavaScript SDK usage. Use when a user asks how to get started, how indexing works, or how to use the TS client.
Google Analytics 4, Search Console, and Indexing API toolkit. Analyze website traffic, page performance, user demographics, real-time visitors, search queries, and SEO metrics. Use when the user asks to: check site traffic, analyze page views, see traffic sources, view user demographics, get real-time visitor data, check search console queries, analyze SEO performance, request URL re-indexing, inspect index status, compare date ranges, check bounce rates, view conversion data, or get e-commerce revenue. Requires a Google Cloud service account with GA4 and Search Console access.
This skill should be used when code search is needed (whether explicitly requested or as part of completing a task), when indexing the codebase after changes, or when the user asks about ccc, cocoindex-code, or the codebase index. Trigger phrases include 'search the codebase', 'find code related to', 'update the index', 'ccc', 'cocoindex-code'.
Reviews PostgreSQL code for indexing strategies, JSONB operations, connection pooling, and transaction safety. Use when reviewing SQL queries, database schemas, JSONB usage, or connection management.
MongoDB query optimization and indexing strategies. Use when writing queries, creating indexes, building aggregation pipelines, or debugging slow operations. Triggers on "slow query", "create index", "optimize query", "aggregation pipeline", "explain output", "COLLSCAN", "ESR rule", "compound index", "partial index", "TTL index", "text search", "geospatial", "$indexStats", "profiler".
Use when designing databases for data-heavy applications, making schema decisions for performance, choosing between normalization and denormalization, selecting storage/indexing strategies, planning for scale, or evaluating OLTP vs OLAP trade-offs. Also use when encountering N+1 queries, ORM issues, or concurrency problems.
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
Language Server Protocol specialist building unified code intelligence systems through LSP client orchestration and semantic indexing
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
Spatial indexing and world streaming for Three.js building games with thousands of pieces. Use when optimizing building games, implementing spatial queries, chunk loading, or profiling performance. Includes spatial hash grids, octrees, chunk managers, and benchmarking tools.