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Found 139 Skills
Provides comprehensive guidance for Elasticsearch including indexing, searching, aggregations, mappings, and cluster management. Use when the user asks about Elasticsearch, needs to implement search functionality, work with Elasticsearch queries, or manage Elasticsearch clusters.
Apply quantization to reduce memory by 4-32x. Enable HNSW indexing for 150x faster search. Configure caching strategies and implement batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors. Deploy these optimizations to achieve 12,500x performance gains.
Use when indexing app content for Spotlight search, using NSUserActivity for prediction/handoff, or choosing between CSSearchableItem and IndexedEntity - covers Core Spotlight framework and NSUserActivity integration for iOS 9+
Complete guide for OpenAI's Assistants API v2: stateful conversational AI with built-in tools (Code Interpreter, File Search, Function Calling), vector stores for RAG (up to 10,000 files), thread/run lifecycle management, and streaming patterns. Both Node.js SDK and fetch approaches. ⚠️ DEPRECATION NOTICE: OpenAI plans to sunset Assistants API in H1 2026 in favor of Responses API. This skill remains valuable for existing apps and migration planning. Use when: building stateful chatbots with OpenAI, implementing RAG with vector stores, executing Python code with Code Interpreter, using file search for document Q&A, managing conversation threads, streaming assistant responses, or encountering errors like "thread already has active run", vector store indexing delays, run polling timeouts, or file upload issues. Keywords: openai assistants, assistants api, openai threads, openai runs, code interpreter assistant, file search openai, vector store openai, openai rag, assistant streaming, thread persistence, stateful chatbot, thread already has active run, run status polling, vector store error
Specialized AI assistant for DSPy development with deep knowledge of predictors, optimizers, adapters, and GEPA integration. Provides session management, codebase indexing, and command-based workflows.
Bitcoin rune operations — check rune balances, list rune-bearing UTXOs, and transfer runes between addresses with Runestone OP_RETURN encoding. Uses the Unisat API for indexing.
Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", "google api setup", "real CWV data", "impressions", "clicks", "CTR", "position data", "LCP", "INP", "CLS", "FCP", "TTFB", or "Lighthouse scores".
Designs database schemas, indexing strategies, query optimization, and migration patterns for SQL and NoSQL databases. Use when designing tables, optimizing queries, fixing N+1 problems, planning migrations, or when asked about database performance, normalization, ORMs, or data modeling.
Build production RAG systems with semantic chunking, incremental indexing, and filtered retrieval. Use when implementing document ingestion pipelines, vector search with Qdrant, or context-aware retrieval. Covers chunking strategies, change detection, payload indexing, and context expansion. NOT when doing simple similarity search without production requirements.
Azure SQL Database best practices skill for optimizing T-SQL code, database configuration, indexing strategies, and application patterns. Based on Microsoft SQL Assessment API, SSDT Code Analysis rules, Azure SQL Database performance guidance, and official Microsoft best practices. Use this skill when writing, reviewing, or refactoring code that interacts with Azure SQL Database.
Primary tool for all code navigation and reading in supported languages (Rust, Python, TypeScript, JavaScript, Go). Use instead of Read, Grep, and Glob for finding symbols, reading function implementations, tracing callers, discovering tests, and understanding execution paths. Provides tree-sitter-backed indexing that returns exact source code — full function bodies, call sites with line numbers, test locations — without loading entire files into context. Use for: finding functions by name or pattern, reading specific implementations, answering 'what calls X', 'where does this error come from', 'how does X work', tracing from entrypoint to outcome, and any codebase exploration. Use Read only for config files, markdown, and unsupported languages.
Database indexing strategies and query optimization. Use when user asks to "optimize queries", "create indexes", "database performance", "query analysis", "explain plans", "index selection", "slow queries", "database tuning", "schema optimization", or mentions database performance and query optimization.