| name | document-search |
| description | Index and search documents using semantic search (RAG). Automatically indexes files in the project's documents/ folder and enables natural language document retrieval. |
Document Search (RAG)
This skill enables semantic document search using Retrieval-Augmented Generation (RAG). Documents are chunked, embedded, and stored in a vector database for fast natural language retrieval.
Three Document Libraries
Documents are organized into libraries (scopes) that control where content is indexed and searched:
Project Library (default)
- Scope:
project_<project_name> (e.g., project_my-app)
- What: Documents specific to a single project
- Where: Files in
documents/ folder are auto-indexed here
- Use when: "Search my project docs", "Find the architecture document"
Global Library
- Scope:
global
- What: Knowledge shared across all projects
- Where: Manually indexed from any project
- Use when: "Search across all projects", "Find any document about compliance"
Domain Library
- Scope:
domain_<name> (e.g., domain_legal, domain_engineering)
- What: Topic-specific collections that span projects
- Where: Manually indexed by topic
- Use when: "Search all legal documents", "Find engineering specs across projects"
When to Activate This Skill
Trigger this skill when the user:
- Asks to search or find documents: "I'm looking for a document about...", "Find the report on...", "Search for..."
- Asks to index or learn a document: "Index this file", "Add this to the knowledge base"
- Places files in the
documents/ directory (auto-indexed via event rule)
- Asks about cross-project or domain-specific searches
Available MCP Tools
rag_index_document
Index a file for semantic search. Supports PDF, Word, Excel, PowerPoint, and text/markdown files.
scope_name: "project_<name>" | "global" | "domain_<name>"
document_path: "documents/report.pdf" (relative to project root)
rag_index_text
Index a short text snippet (up to 2000 characters). Use for notes, extracted content, or quick knowledge capture.
scope_name: "project_<name>" | "global" | "domain_<name>"
text_part: "The authentication system uses JWT tokens with..."
rag_index_search
Search indexed documents using natural language. Returns the top matching chunks with similarity scores.
scope_name: "project_<name>" | "global" | "domain_<name>"
search_query: "How does authentication work?"
Automatic Document Indexing
When this skill is provisioned, an event rule is created that watches for new files in the documents/ directory. When a file is added:
- The file watcher detects the new file
- The event rule triggers the
rag_index_document tool
- The document is automatically indexed into the project library
Supported file formats for auto-indexing:
- Text:
.md, .txt, .csv, .tsv, .json, .yaml, .xml
- PDF:
.pdf (including scanned PDFs with OCR)
- Office:
.docx, .xlsx, .pptx, .doc, .xls, .ppt, .odt, .ods, .odp
Workflow
Indexing a document manually
User: "Index the file documents/architecture.pdf"
Agent: Calls rag_index_document with scope_name="project_<current_project>"
and document_path="documents/architecture.pdf"
Agent: "Indexed architecture.pdf — 12 chunks stored in your project library."
Searching for documents
User: "I'm looking for a document about the authentication flow"
Agent: Calls rag_index_search with scope_name="project_<current_project>"
and search_query="authentication flow"
Agent: "Found 3 relevant documents: ..."
Cross-project search
User: "Search across all projects for compliance guidelines"
Agent: Calls rag_index_search with scope_name="global"
and search_query="compliance guidelines"
Domain-specific indexing
User: "Add this legal document to the legal domain library"
Agent: Calls rag_index_document with scope_name="domain_legal"
and document_path="documents/terms-of-service.pdf"
File & Folder Conventions
- Place documents to index in
workspace/<project>/documents/
- Subdirectories within
documents/ are supported
- Binary files (PDF, Office) are automatically parsed to text using liteparse
- Text files are read directly
