embedding-strategies

29.9k

wshobson

Select 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.

rag-engineer

21.8k

davila7

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

rag-implementation

21.8k

davila7

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.

agent-memory-systems

21.8k

davila7

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

embedding-strategies

18.0k

sickn33

Select 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.

google-gemini-file-search

572

jezweb

Build document Q&A with Gemini File Search - fully managed RAG with automatic chunking, embeddings, and citations. Upload 100+ file formats, query with natural language. Use when: document Q&A, searchable knowledge bases, semantic search. Troubleshoot: document immutability, storage quota (3x), chunking config, metadata limits (20 max), polling timeouts, displayName dropped (Blob uploads), grounding lost (JSON mode), tool conflicts (googleSearch + fileSearch).

gtts

571

benchflow-ai

Google Text-to-Speech (gTTS) for converting text to audio. Use when creating audiobooks, podcasts, or speech synthesis from text. Handles long text by chunking at sentence boundaries and concatenating audio segments with pydub.

rag-chunking-strategy

376

a5c-ai

Document chunking with multiple strategies including semantic, recursive, and fixed-size chunking

using-vector-databases

296

ancoleman

Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.

embedding-optimization

296

ancoleman

Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.

ai-data-engineering

296

ancoleman

Data pipelines, feature stores, and embedding generation for AI/ML systems. Use when building RAG pipelines, ML feature serving, or data transformations. Covers feature stores (Feast, Tecton), embedding pipelines, chunking strategies, orchestration (Dagster, Prefect, Airflow), dbt transformations, data versioning (LakeFS), and experiment tracking (MLflow, W&B).

building-rag-systems

150

panaversity

Build production RAG systems with LangChain orchestration and Qdrant vector store. Covers 8 RAG architectures (Simple, HyDE, CRAG, Self-RAG, Agentic), document processing, semantic chunking, retrieval chains, and evaluation with LangSmith/RAGAS. Use when implementing RAG pipelines, semantic search, or AI knowledge systems. NOT for simple keyword search.

chunking-strategy

128

giuseppe-trisciuoglio

Provides optimal chunking strategies in RAG systems and document processing pipelines. Use when building retrieval-augmented generation systems, vector databases, or processing large documents that require breaking into semantically meaningful segments for embeddings and search.

ai-seo-optimization

100

geekskai

Optimize content and websites for AI search engines (ChatGPT, Perplexity, Google AI Overviews) using GEO principles, content chunking, structured data, and brand visibility strategies. Use when working on SEO, AI visibility, content optimization, GEO, getting brand mentioned in AI, or implementing technical SEO for LLM search.

rag-implementation

44

applied-artificial-intelligence

Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.

project-management-guru-adhd

43

curiositech

Expert project manager for ADHD engineers managing multiple concurrent projects. Specializes in hyperfocus management, context-switching minimization, and parakeet-style gentle reminders. Activate on 'ADHD project management', 'context switching', 'hyperfocus', 'task prioritization', 'multiple projects', 'productivity for ADHD', 'task chunking', 'deadline management'. NOT for neurotypical project management, rigid waterfall processes, or general productivity advice without ADHD context.

ai-rag

34

vasilyu1983

Complete RAG and search engineering skill. Covers chunking strategies, hybrid retrieval (BM25 + vector), cross-encoder reranking, query rewriting, ranking pipelines, nDCG/MRR evaluation, and production search systems. Modern patterns for retrieval-augmented generation and semantic search.

docling

33

existential-birds

Docling document parser for PDF, DOCX, PPTX, HTML, images, and 15+ formats. Use when parsing documents, extracting text, converting to Markdown/HTML/JSON, chunking for RAG pipelines, or batch processing files. Triggers on DocumentConverter, convert, convert_all, export_to_markdown, HierarchicalChunker, HybridChunker, ConversionResult.

rag-architecture

31

melodic-software

Retrieval-Augmented Generation (RAG) system design patterns, chunking strategies, embedding models, retrieval techniques, and context assembly. Use when designing RAG pipelines, improving retrieval quality, or building knowledge-grounded LLM applications.

semantic-search

29

omer-metin

Build production-ready semantic search systems using vector databases, embeddings, and retrieval-augmented generation (RAG). Covers vector DB selection (Pinecone/Qdrant/Weaviate), embedding models (OpenAI/Voyage/Cohere), chunking strategies, hybrid search, and reranking for high-quality retrieval. Use when ", vector-search, embeddings, rag, pinecone, qdrant, weaviate, llama-index, langchain, hybrid-search, reranking" mentioned.

rag-engineer

29

omer-metin

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when "building RAG, vector search, embeddings, semantic search, document retrieval, context retrieval, knowledge base, LLM with documents, chunking strategy, pinecone, weaviate, chromadb, pgvector, rag, embeddings, vector-database, retrieval, semantic-search, llm, ai, langchain, llamaindex" mentioned.

RAG Implementation

29

omer-metin

Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization. Use when "rag, retrieval augmented, vector search, embeddings, semantic search, document qa, rag, retrieval, embeddings, vector, search, llm" mentioned.

bknd-bulk-operations

28

cameronapak

Use when performing bulk insert, update, or delete operations in Bknd. Covers createMany, updateMany, deleteMany, batch processing with progress, chunking large datasets, error handling strategies, and transaction-like patterns.

etl-core-patterns

27

majesticlabs-dev

Core ETL reliability patterns including idempotency, checkpointing, error handling, chunking, retry logic, and logging.

google-gemini-file-search

25

ovachiever

Build document Q&A and searchable knowledge bases with Google Gemini File Search - fully managed RAG with automatic chunking, embeddings, and citations. Upload 100+ file formats (PDF, Word, Excel, code), configure semantic search, and query with natural language. Use when: building document Q&A systems, creating searchable knowledge bases, implementing semantic search without managing embeddings, indexing large document collections (100+ formats), or troubleshooting document immutability errors (delete+re-upload required), storage quota issues (3x input size for embeddings), chunking configuration (500 tokens/chunk recommended), metadata limits (20 key-value pairs max), indexing cost surprises ($0.15/1M tokens one-time), operation polling timeouts (wait for done: true), force delete errors, or model compatibility (Gemini 2.5 Pro/Flash only).

building-rag-systems

22

mjunaidca

Build production-grade 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 intended for simple similarity searches without production requirements.

token-budget-advisor

18

Exploration-labs

Proactive token budget assessment and task chunking strategy. Use this skill when queries involve multiple large file uploads, requests for comprehensive multi-document analysis, complex multi-step workflows with heavy research (10+ tool calls), phrases like "complete analysis", "full audit", "thorough review", "deep dive", or tasks combining extensive research with large output artifacts. This skill helps assess token consumption risk early and recommend chunking strategies before beginning work.

pitch-deck-builder

17

Exploration-labs

Comprehensive pitch deck creation with conversational discovery, narrative structuring, and context-aware chunking strategies. This skill should be used when users request help creating pitch decks, investor presentations, fundraising decks, or startup presentations. It guides through narrative development, data collection, and professional slide creation.

Task Breakdown Specialist

17

daffy0208

Break large tasks into ADHD-friendly micro-tasks that minimize activation energy and maximize momentum. Use when you feel overwhelmed, can’t start a task, or need to make progress visible. Optimized for ADHD developers with 15-minute task chunking and dopamine-driven ordering.

banana-sync-to-notion

14

TreyDong

Sync local Markdown files to Notion with full formatting support. Use when user wants to backup, sync, or migrate files to Notion, mentions uploading to Notion, or says sync to Notion. Features include: (1) Recursive directory structure preservation, (2) Complete Markdown formatting (bold, italic, code, links, lists, tables, callouts), (3) Automatic emoji icons based on filenames, (4) Duplicate detection for incremental syncs, (5) Smart chunking for large files, (6) Relative link conversion to Notion page links. Requires Notion API token and target page ID configured in .env file.

grepai-chunking

14

yoanbernabeu

Configure code chunking in GrepAI. Use this skill to optimize how code is split for embedding.

rag-pipeline-builder

12

patricio0312rev

Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".

embedding-pipeline-builder

12

patricio0312rev

Builds document embedding pipelines with text chunking, embedding generation, indexing, and retrieval optimization. Use when users request "embedding pipeline", "document indexing", "text chunking", "RAG preprocessing", or "semantic indexing".

rag-infrastructure

10

BagelHole

Build and operate Retrieval-Augmented Generation (RAG) infrastructure with vector stores, embedding pipelines, and hybrid search. Covers ingestion, chunking strategies, reranking, and production deployment patterns.

Timeout Prevention

9

jackspace

Prevent request timeouts in Claude Code sessions by chunking long operations, implementing progress checkpoints, using background processes, and optimizing tool usage patterns. Use when performing bulk operations, processing large datasets, running long-running commands, downloading multiple files, or executing tasks that exceed 2-minute timeout limits. Implements strategies like task decomposition, incremental processing, parallel execution, checkpoint-resume patterns, and efficient resource management to ensure reliable completion of complex workflows.

crawl4ai

9

tao3k

Use when crawling web pages, extracting markdown content, or scraping website data with intelligent chunking and skeleton planning. Use when the user provides a URL or link to fetch or crawl.

Google Gemini Embeddings

9

jackspace

This skill provides complete coverage of Google Gemini embeddings API (gemini-embedding-001) for building RAG systems, semantic search, document clustering, and similarity matching. Use when implementing vector search with Google's embedding models, integrating with Cloudflare Vectorize, or building retrieval-augmented generation systems. Covers SDK usage (@google/genai), fetch-based Workers implementation, batch processing, 8 task types (RETRIEVAL_QUERY, RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY, etc.), dimension optimization (128-3072), and cosine similarity calculations. Prevents 8+ embedding-specific errors including dimension mismatches, incorrect task types, rate limiting issues (100 RPM free tier), vector normalization mistakes, text truncation (2,048 token limit), and model version confusion. Includes production-ready RAG patterns with Cloudflare Vectorize integration, chunking strategies, and caching patterns. Token savings: ~60%. Production tested. Keywords: gemini embeddings, gemini-embedding-001, google embeddings, semantic search, RAG, vector search, document clustering, similarity search, retrieval augmented generation, vectorize integration, cloudflare vectorize embeddings, 768 dimensions, embed content gemini, batch embeddings, embeddings api, cosine similarity, vector normalization, retrieval query, retrieval document, task types, dimension mismatch, embeddings rate limit, text truncation, @google/genai

session-timeout-handler

9

jackspace

Build timeout-resistant Claude Code workflows with chunking strategies, checkpoint patterns, progress tracking, and resume mechanisms to handle 2-minute tool timeouts and ensure reliable completion of long-running operations.

chunking-strategies

8

jpoutrin

Document chunking strategies for RAG systems. Use when implementing document processing pipelines to determine optimal chunking approaches based on document type and retrieval requirements.

form-ux-patterns

7

Bbeierle12

UX patterns for complex forms including multi-step wizards, cognitive chunking (5-7 fields max), progressive disclosure, and conditional fields. Use when building checkout flows, onboarding wizards, or forms with many fields.

Context Window Expert

7

willsigmon

Context optimization - token management, chunking, compression, RAG strategies

refactor-structural-duplication

6

JordanGunn

Identify structurally duplicate logic (pipeline-spine duplication) across semantically distinct modules. Use when multiple implementations share the same orchestration skeleton (e.g., bounds→shape→chunking→reduction→wrap), and unification may require subtle abstraction. Produces a duplication map and a safe extraction/refactor plan.

rag-engineer

5

agent-skills-hub

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.

embedding-strategies

5

agent-skills-hub

Select 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.

agent-memory-systems

5

agent-skills-hub

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

evaluate-rag

4

hamelsmu

Guides evaluation of RAG pipeline retrieval and generation quality. Use when evaluating a retrieval-augmented generation system, measuring retrieval quality, assessing generation faithfulness or relevance, generating synthetic QA pairs for retrieval testing, or optimizing chunking strategies.

embedding-strategies

4

ngxtm

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific dom...

rag-engineer

4

ngxtm

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, ...