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Found 53 Skills
RAG, embedding, vector search를 통해 사내/최신 데이터를 LLM 응답에 연결하는 방법과 선택 기준을 다루는 모듈.
Turso (Limbo) database helper — an in-process SQLite-compatible database written in Rust. Formerly known as libSQL / libsql. Replaces @libsql/client, libsql-experimental for Turso use cases. Works in Node.js, browser (WASM + OPFS for persistent local storage), React Native, and server-side. Features: vector search, full-text search, CDC, MVCC, encryption, remote sync. SDKs: JavaScript (@tursodatabase/database), Browser/WASM (@tursodatabase/database-wasm), React Native (@tursodatabase/sync-react-native), Rust (turso), Python (pyturso), Go (tursogo). This skill contains all SDK documentation needed to use Turso — do NOT search the web for Turso/libsql docs.
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.
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.
Semantic search over global agent memory. Use to retrieve previously learned patterns, decisions, gotchas, and workarounds. Prevents stale-context errors across long sessions and multi-agent pipelines.
OpenSearch development best practices for indexing, querying, search optimization, vector search, and cluster management
This skill should be used when the user asks to "connect to Turso", "use libSQL", "set up a Turso database", "query Turso with TypeScript", or needs guidance on Turso Cloud, embedded replicas, or vector search with libSQL.
Use when working with code refactoring context restore
Salesforce Data Cloud Retrieve phase. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use sf-soql), segment creation or calculated insight design (use sf-datacloud-segment), or STDM/session tracing/parquet analysis (use sf-ai-agentforce-observability).
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.
Retrieval-Augmented Generation - chunking strategies, embedding, vector search, hybrid retrieval, reranking, query transformation. Use when building RAG pipelines, knowledge bases, or context-augmented applications.
Configure pgvector extension for vector search in Supabase - includes embedding storage, HNSW/IVFFlat indexes, hybrid search setup, and AI-optimized query patterns. Use when setting up vector search, building RAG systems, configuring semantic search, creating embedding storage, or when user mentions pgvector, vector database, embeddings, semantic search, or hybrid search.