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Found 2,493 Skills
Build complete document knowledge bases with PDF text extraction, OCR for scanned documents, vector embeddings, and semantic search. Use this for creating searchable document libraries from folders of PDFs, technical standards, or any document collection.
Use when adding multi-format RAG ingest, chunk, embed, and retrieval pipelines; pair with architect-python-uv-batch or architect-python-uv-fastapi-sqlalchemy.
Detect abnormal access patterns in AWS S3, GCS, and Azure Blob Storage by analyzing CloudTrail Data Events, GCS audit logs, and Azure Storage Analytics. Identifies after-hours bulk downloads, access from new IP addresses, unusual API calls (GetObject spikes), and potential data exfiltration using statistical baselines and time-series anomaly detection.
Knowledge Base RAG implements the complete Retrieval-Augmented Generation pipeline: document ingestion, intelligent chunking, embedding generation, vector store indexing, semantic retrieval, and grounded response generation.
Build GraphRAG retrieval pipelines on Neo4j using the neo4j-graphrag Python package (formerly neo4j-genai). Covers retriever selection (VectorRetriever, HybridRetriever, VectorCypherRetriever, HybridCypherRetriever, Text2CypherRetriever), retrieval_query Cypher fragments, query_params, pipeline wiring (GraphRAG + LLM), embedder setup, index creation, and LangChain/LlamaIndex integration. Does NOT handle KG construction from documents — use neo4j-document-import-skill. Does NOT handle plain vector search — use neo4j-vector-index-skill. Does NOT handle GDS analytics — use neo4j-gds-skill. Does NOT handle agent memory — use neo4j-agent-memory-skill.
Retrieval-Augmented Generation patterns including chunking, embeddings, vector stores, and retrieval optimization Use when: rag, retrieval augmented, vector search, embeddings, semantic search.
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.
Implements drag-and-drop and sortable interfaces with React/TypeScript including kanban boards, sortable lists, file uploads, and reorderable grids. Use when building interactive UIs requiring direct manipulation, spatial organization, or touch-friendly reordering.
Docs as QA: audit doc coverage and freshness, validate runbooks, and maintain documentation quality gates for APIs, services, events, and operational workflows. Includes AI-assisted audits, observability patterns, and automated coverage tracking.
bkend.ai file storage expert skill. Covers single/multiple/multipart file upload via Presigned URL, file download (CDN vs Presigned), 4 visibility levels (public/private/protected/shared), bucket management, and file metadata. Triggers: file upload, download, presigned, bucket, storage, CDN, image, 파일 업로드, 다운로드, 버킷, 스토리지, 이미지, ファイルアップロード, ダウンロード, バケット, ストレージ, 文件上传, 下载, 存储桶, 存储, carga de archivos, descarga, almacenamiento, cubo, telechargement, televersement, stockage, seau, Datei-Upload, Download, Speicher, Bucket, caricamento file, scaricamento, archiviazione, bucket Do NOT use for: database operations (use bkend-data), authentication (use bkend-auth).
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.
Manage cloud storage buckets and objects using the S3-compatible Telnyx Storage API. This skill provides Java SDK examples.