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Found 1,654 Skills
Facebook's library for efficient similarity search and clustering of dense vectors. Supports billions of vectors, GPU acceleration, and various index types (Flat, IVF, HNSW). Use for fast k-NN search, large-scale vector retrieval, or when you need pure similarity search without metadata. Best for high-performance applications.
Implementing providers for Beluga AI v2 registries. Use when creating LLM, embedding, vectorstore, voice, or any other provider.
Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.
Search, query, and manage Weaviate vector database collections. Use for semantic search, hybrid search, keyword search, natural language queries with AI-generated answers, collection management, data exploration, filtered fetching, data imports from CSV/JSON/JSONL files, create example data and collection creation.
Write Playwright tests for Hyvä themes with Alpine.js components. This skill should be used when writing e2e tests, creating page objects, or debugging selector issues in Playwright tests for Hyvä Magento storefronts. Trigger phrases include "write playwright test", "playwright alpine", "test hyva page", "e2e test", "playwright selector".
End-to-end testing scenarios for Supabase - complete workflow tests from project creation to AI features, validation scripts, and comprehensive test suites. Use when testing Supabase integrations, validating AI workflows, running E2E tests, verifying production readiness, or when user mentions Supabase testing, E2E tests, integration testing, pgvector testing, auth testing, or test automation.
Vector embeddings configuration and semantic search
Semantic and multi-modal search across documents using LanceDB vector embeddings. Use when searching knowledge bases, finding information semantically, ingesting documents for RAG, or performing vector similarity search. Triggers on "search documents", "semantic search", "find in knowledge base", "vector search", "index documents", "LanceDB", or RAG/embedding operations.
SIMD intrinsics skill for x86 (SSE/AVX) and ARM (NEON) vectorization. Use when reading auto-vectorization reports, writing SSE2/AVX2/NEON intrinsics, checking CPU feature flags at runtime, choosing between compiler builtins and raw intrinsics, or diagnosing why auto-vectorization failed. Activates on queries about SIMD, SSE2, AVX2, NEON, intrinsics, -fopt-info-vec, auto-vectorization, or vectorization failures.
Interactive initialization script that acts as a Plugin Architect. Generates a compliant '.claude-plugin' directory structure and `plugin.json` manifest using diagnostic questioning to ensure proper L4 patterns and Tool Connector schemas.
Patterns for ingesting knowledge into vector databases and RAG systems
Minimize unnecessary React re-renders when consuming external state (XState, @xstate/store, Zustand, Redux, Nanostores, context). Prefer selector-based subscriptions over useState(wholeObject). Use when dealing with external state in React, optimizing re-renders, choosing state patterns, or integrating with these libraries.