Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Turn text into embedding vectors with Forge (Voxell's hosted embedding API) for semantic search, RAG retrieval, clustering, dedup, or similarity. Use when the user wants to embed text/documents/queries, build or query a vector index, find similar or duplicate text, or pick an embedding model and dimension. Provides…
A skill that sends a search query to a script and receives text from a retrieval-augmented generation system, which finds relevant source material before producing an answer.
Expert guide for building applications with Qdrant Edge — the embedded, offline-capable vector search engine for edge devices (robots, kiosks, mobile phones, IoT, home assistants). Use this skill whenever the user mentions Qdrant Edge, qdrant-edge-py, EdgeShard, on-device vector search, offline vector search, embedded…
Qdrant MCP Server management and diagnostics skill. Use whenever working with the qmcp (QDrant MCP Server) project for semantic code search. Activates on queries about: Qdrant database state, collections, or indexed content Cleaning up vector database (orphans, stale vectors, missing files) Understanding what's…
Use when you need to store, retrieve, search, or manage documents in a local knowledge base with semantic search and hybrid (vector + full-text) retrieval. Also use when interacting with the documentation server web interface, managing uploads, or performing AI-powered document analysis. Use this instead of MCP-native…
Use a self-hosted knowledge-engine MCP for structured project orientation, text and image RAG, source verification, file contributions and durable work closeout. Applies to the configured deployment, both personal and collaborative.
Search, retrieve, version, and persist prompts, documents, and collections through the hosted Context Repo MCP. Use this skill whenever the user mentions Context Repo, saved prompts, prompt templates, stored documents, collections, semantic search, hierarchical document navigation, cited answers over stored documents…
A Chinese-language skill for saving, searching, updating, and deleting information across conversations through a Feishu memory service. Feishu is a collaboration platform, and RAG is a way to retrieve relevant stored documents or notes for an agent.
Consolidate the brain — refresh the index, review sessions since the last reflection, distill durable facts into memory files, compact the store, propose skills, commit. Use when asked to "reflect" or "consolidate memory"; also invoked headless by the nightly job.
Set up an accreta knowledge base over a corpus — pick a preset, write the config and a source declaration, generate the constitution, wire up MCP, and verify the first ingest. Use when someone wants an agent-maintained knowledge base with provenance and drift detection over a codebase, a document collection, or a…
Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community…
Work with a WeKnora knowledge base through its REST API — list and inspect knowledge bases, search one or many of them, import files, URLs, or Markdown, confirm an import finished parsing, read and edit chunks, and ask questions with a streamed answer. Use when the task targets a user's own WeKnora instance and its…
Skill da REST API do Base de Conhecimento na MCP.AI: 7 endpoints em /api/kb. Sua base de conhecimento privada: suba documentos (.md, .txt, .docx, PDF, imagens), a plataforma indexa tudo semanticamente e o agente busca o contexto relevante na hora. Ideal pra escritórios. Cada base é isolada, o conteúdo fica…
Hybrid semantic search (dense vectors + BM25) over local markdown/code knowledge bases. Indexes files into Qdrant via LlamaIndex. Exposed as a CLI (sage) and an MCP server (sage-mcp-mcp). Agents inside an MCP-capable host should prefer MCP tools — shared persistent connection, no subprocess overhead, no Qdrant…
Search, ingest, expand chunk context, or manage local documents via a local RAG MCP server (tools: querydocuments, readchunkneighbors, ingestfile, ingestdata, deletefile, listfiles). Use when user says "search my docs", "save this page", "read around that chunk", "what did I save about X", or invokes npx local-rag.
A skill for operating the RAG-Anything MCP service, which lets an agent index and search information in files such as PDFs, documents, spreadsheets, web pages, and code.
Launch or reuse the loopback Memex Workspace UI when the user asks to browse conversations, manage facts, inspect taxonomy or pipeline health, or explore the knowledge map.
Ingest a source path into a specified collection and report file, chunk, vector, and BM25 counts.
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At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: