Loading...
Loading...
Found 65 Skills
Use when the user wants to search, query, extract, transcribe, describe, quote, filter, or aggregate across documents — PDFs, scanned forms / images (`.jpg` `.png` `.tiff`), Office (`.docx` `.pptx`), text (`.html` `.txt`), audio (`.mp3` `.wav` `.m4a`), or video (`.mp4` `.mov`). Prefer this over native Read / Grep for multi-file or non-PDF corpora. Not for: editing files, web browsing, single-file plain-text lookups, fine-tuning.
Build vector retrieval with DashVector using the Python SDK. Use when creating collections, upserting docs, and running similarity search with filters in Claude Code/Codex.
Optimize vector index performance for latency, recall, and memory. Use when tuning HNSW parameters, selecting quantization strategies, or scaling vector search infrastructure.
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
Use OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches. Ideal for RAG and vector retrieval pipelines in Claude Code/Codex.
Generate text embeddings and rerank documents via Together AI. Embedding models include BGE, GTE, E5, UAE families. Reranking via MixedBread reranker. Use when users need text embeddings, vector search, semantic similarity, document reranking, RAG pipeline components, or retrieval-augmented generation.
Guide for using the `paper` CLI tool — a local academic paper management system with AI-powered vector search. Use this skill whenever the user wants to manage academic papers, create knowledge bases, add PDFs to a knowledge base, search papers semantically, configure embedding models, or manage literature metadata and notes. Also trigger when the user mentions "paper" CLI, knowledge bases for research, literature management, or wants to query their paper collection. Even if the user just says something like "add this PDF" or "search my papers" in a project that uses paper-manager, this skill should activate.
Run the Upstash CLI (`upstash`) against the Upstash Developer API for Redis, Vector, Search, QStash, and teams. Use when listing or managing databases, backups, vector/search indexes, QStash instances, team members, stats, or any non-interactive Upstash automation with JSON output and terminal commands.
Vector search with SurrealDB using HNSW indexes, KNN queries, and similarity scoring. Use when creating vector indexes, querying vectors with KNN distance operators, building semantic search or RAG pipelines, tuning HNSW parameters (EFC, M, M0, distance function, type), or implementing recommendation systems with SurrealDB. Triggers: HNSW, vector, embedding, KNN, cosine, euclidean, semantic search, RAG, vector::distance.
Use when working with AliCloud Milvus (serverless) with PyMilvus to create collections, insert vectors, and run filtered similarity search. Optimized for Claude Code/Codex vector retrieval flows.
OpenSearch development best practices for indexing, querying, search optimization, vector search, and cluster management
Review and improve HelixDB query performance and query shape. Use when the task is to optimize a slow Helix query, improve anchor choice, tighten index usage, reduce traversal breadth, slim projections, fix BM25 or vector search scope, or decide between stored and dynamic routes.