Search Results for "search"
concept-workflow
★ 66.0kEnd-to-end workflow for creating complete JavaScript concept documentation, orchestrating all skills from research to final review
seo-review
★ 66.0kPerform a focused SEO audit on JavaScript concept pages to maximize search visibility, featured snippet optimization, and ranking potential
embedding-strategies
★ 24.2kSelect and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.
hybrid-search-implementation
★ 24.2kCombine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
rag-implementation
★ 24.2kBuild Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
similarity-search-patterns
★ 24.2kImplement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
vector-index-tuning
★ 24.2kOptimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
lead-research-assistant
★ 17.4kIdentifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
content-research-writer
★ 17.4kAssists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
guidance
★ 15.4kControl LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework
chroma
★ 15.4kOpen-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.
pinecone
★ 15.4kManaged vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
n8n-mcp-tools-expert
★ 15.4kExpert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, or using any n8n-mcp tool. Provides tool selection guidance, parameter formats, and common patterns.
faiss
★ 15.4kFacebook'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.
sentence-transformers
★ 15.4kFramework for state-of-the-art sentence, text, and image embeddings. Provides 5000+ pre-trained models for semantic similarity, clustering, and retrieval. Supports multilingual, domain-specific, and multimodal models. Use for generating embeddings for RAG, semantic search, or similarity tasks. Best for production embedding generation.
huggingface-tokenizers
★ 15.4kFast tokenizers optimized for research and production. Rust-based implementation tokenizes 1GB in <20 seconds. Supports BPE, WordPiece, and Unigram algorithms. Train custom vocabularies, track alignments, handle padding/truncation. Integrates seamlessly with transformers. Use when you need high-performance tokenization or custom tokenizer training.
transformer-lens-interpretability
★ 15.4kProvides guidance for mechanistic interpretability research using TransformerLens to inspect and manipulate transformer internals via HookPoints and activation caching. Use when reverse-engineering model algorithms, studying attention patterns, or performing activation patching experiments.
qdrant-vector-search
★ 15.4kHigh-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
Frequently Asked Questions
What is a Claude Skill?
It is a modular, protocol-based instruction set that enables Claude to perform specific tasks autonomously and efficiently.
How is this different from regular prompts?
Skills use the official Anthropic protocol for better token efficiency and automatic triggering compared to static prompts.
Does it support the new Claude Code CLI?
Yes, we provide specialized one-click installation commands designed specifically for Claude Code and OpenCode.
Can I use these skills in Cursor?
Absolutely, our library is fully compatible with popular IDEs and extensions including Cursor, Codex, and AntiGravity.
Are the skills on the website free to use?
Most skills in our 90,000+ database are free and open-source for the community to download and deploy.
How can I contribute my own skills?
Developers can easily share their work by submitting their own skill repositories through our community portal.