17건 · 관련도순
Manage ChromaDB collections and documents from the command line, and search by meaning rather than exact wording. JSON output supports automated agent workflows, while server checks and an interactive mode help with everyday database operations.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Initialize a new MemPalace for AI memory through a guided setup process. The skill retrieves setup instructions for a configuration that uses ChromaDB as its data storage backend and follows the returned steps.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Assists with storing, searching, and managing vector embeddings using ChromaDB. Use when building RAG pipelines, semantic search engines, or recommendation systems. Trigger words: chromadb, chroma, vector database, embeddings, semantic search, similarity search, vector store, rag.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Manage the ChromaDB vector database that stores the ecosystem's persistent memory. Use when user asks to check memory storage, backup memory, search stored entries, delete entries, or reset the vector database. Do NOT use for general question answering about past sessions (use the memory skill for that).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Chroma로 의미가 비슷한 문서를 찾는 시스템을 구축합니다. 문서와 부가 정보를 저장·갱신하고 조건으로 검색을 좁히며 AI 앱을 시제품으로 만듭니다.
Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build document search and AI retrieval workflows with Chroma, a database for numerical representations of text. Cover document storage, meaning-based search, metadata filters, persistence, and connections to LangChain and LlamaIndex.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Semantic search over ingested documents using RAG (LlamaIndex/ChromaDB or Foundational RAG)
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user sends or generates an image, screenshot, video, audio or file and wants it saved, or asks to find past media (that diagram, the mockup from last week). Ingests and searches with local ChromaDB embeddings.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Implement semantic vector search for construction data. Build AI-powered search using embeddings and vector databases (Qdrant, ChromaDB) for intelligent querying of specifications, standards, and project documents.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Add persistent memory to AI coding agents — file-based, vector, and semantic search memory systems that survive between sessions. Use when a user asks to "remember this", "add memory to my agent", "persist context between sessions", "build a knowledge base for my agent", "set up agent memory", or "make my AI remember things". Covers file-based memory (MEMORY.md), SQLite with embeddings, vector databases (ChromaDB, Pinecone), semantic search, memory consolidation, and automatic context injection.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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 fragmented with inconsistent terminology. We use the CoALA cognitive architecture framework: semantic memory (facts), episodic memory (experiences), and procedural memory (how-to knowledge). Use when "agent memory, long-term memory, memory systems, remember across sessions, memory retrieval, episodic memory, semantic memory, vector store, rag, langmem, memgpt, conversation history, memory, vector-store, rag, retrieval, embedding, episodic, semantic, procedural, langmem, memgpt, pinecone, qdrant, chromadb" mentioned.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Persistent memory across AI sessions using ChromaDB vector database. Stores and retrieves context from past conversations, decisions, and code. Use when user asks to remember something, search past conversations, recall what was done before, save context for later, or find information from previous sessions. Do NOT use for git history or file-based notes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Search and explore an Obsidian vault through the NoteBrain CLI (semantic search, tags, backlinks, connections, hidden links, boosted retrieval). Use it whenever the user mentions their notes, knowledge base, Obsidian vault, semantic search, finding connections or unlinked notes, or asks exploratory questions like "what do I know about X", "find notes related to Y", "what connects to Z", or "summarize my notes on W" — even when they never say NoteBrain, vector search, or ChromaDB.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Erstellt einen portablen KI-Arbeitsbereich auf einem USB-Stick oder beliebigen Laufwerk. RAG-Pipeline mit lokalen LLM-Modellen (Ollama), Vektordatenbank (ChromaDB) und vorkonfigurierten Prompts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Rebuild the DSH node's persistent knowledge state. Runs register-claude.py to (re)create the dome.db schema and upsert agents/skills/tools/stack rows, then runs ingest.py to re-index kb/, logs/, docs, and agent core source into ChromaDB's dome-kb collection, then runs pre-spore-verify.py to confirm all 7 skill modules load and the akashic write+query roundtrip works. Idempotent — safe to run any time.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.