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Found 245 Skills
Google Gemini embeddings API (gemini-embedding-001) for RAG and semantic search. Use for vector search, Vectorize integration, or encountering dimension mismatches, rate limits, text truncation.
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.
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).
Use this skill when building NLP pipelines, implementing text classification, semantic search, embeddings, or summarization. Triggers on text preprocessing, tokenization, embeddings, vector search, named entity recognition, sentiment analysis, text classification, summarization, and any task requiring natural language processing.
Provides expertise on Chroma vector database integration for semantic search applications. Use when the user asks about vector search, embeddings, Chroma, semantic search, RAG systems, nearest neighbor search, or adding search functionality to their application.
Complete guide for OpenAI's traditional/stateless APIs: Chat Completions (GPT-5, GPT-4o), Embeddings, Images (DALL-E 3), Audio (Whisper + TTS), and Moderation. Includes both Node.js SDK and fetch-based approaches for maximum compatibility. Use when: integrating OpenAI APIs, implementing chat completions with GPT-5/GPT-4o, generating text with streaming, using function calling/tools, creating structured outputs with JSON schemas, implementing embeddings for RAG, generating images with DALL-E 3, transcribing audio with Whisper, synthesizing speech with TTS, moderating content, deploying to Cloudflare Workers, or encountering errors like rate limits (429), invalid API keys (401), function calling failures, streaming parse errors, embeddings dimension mismatches, or token limit exceeded. Keywords: openai api, chat completions, gpt-5, gpt-5-mini, gpt-5-nano, gpt-4o, gpt-4-turbo, openai sdk, openai streaming, function calling, structured output, json schema, openai embeddings, text-embedding-3, dall-e-3, image generation, whisper api, openai tts, text-to-speech, moderation api, openai fetch, cloudflare workers openai, openai rate limit, openai 429, reasoning_effort, verbosity
Expert guidance for OpenAI API development including GPT models, Assistants API, function calling, embeddings, and best practices for production applications.
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides REST API (curl) examples.
Semantic search using embeddings and vector storage. Search documents semantically using similarity matching.
Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, or semantic nearest-neighbor lookup. Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext/keyword indexes (FULLTEXT INDEX, db.index.fulltext) — use neo4j-cypher-skill. Does NOT handle GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
Vector embeddings configuration and semantic search