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Knowledge management and RAG platform with tree-based document indexing. Use this skill to search, browse, and manage Orchata knowledge bases via MCP tools.
Follow the repository instructions for this skill and your agent. Review the files and dependencies before adding it to your workflow.
SkillHub CLI
skillhub install ehudsn/orchataInspect the source and compatibility requirements, then follow its installation steps.
Give your agent the source link and ask it to inspect instructions and dependencies before explaining the installation steps.
This document describes how to effectively use Orchata, a RAG (Retrieval-Augmented Generation) platform with tree-based document indexing. Load this into your context to interact with Orchata knowledge bases.
Orchata is a knowledge management platform that:
A Space is a container for related documents. Think of it as a folder with semantic search capabilities.
name, description, and optional icon
smart_query to recommend relevant spacesA Document is content within a space. Supported formats include:
Document Status:
| Status | Description |
|---|---|
PENDING |
Uploaded, waiting for processing |
PROCESSING |
Being parsed and indexed |
COMPLETED |
Ready for queries |
FAILED |
Processing error occurred |
Important: Only query documents with status: "COMPLETED". Other statuses won't return results.
Documents are indexed into hierarchical tree structures:
title, summary, startPage, endPage, textContent
Two types of queries are available:
query_spaces - Search document content using tree-bas...These signals come from indexed source analysis and may lag behind changes. Check the current source files.
# Orchata Skills This document describes how to effectively use Orchata, a RAG (Retrieval-Augmented Generation) platform with tree-based document indexing. Load this into your context to interact with Orchata knowledge bases. ## What is Orchata? Orchata is a knowledge management platform that: - **Organizes documents into Spaces** - Logical containers for related content - **Uses tree-based indexing** - Documents are parsed into hierarchical structures with sections, summaries, and page ranges - **Provides semantic search** - Find relevant content using natural language queries - **Exposes MCP tools** - AI assistants can directly manage and query knowledge bases ## Core Concepts ### Spaces A **Space** is a container for related documents. Think of it as a folder with semantic search capabilities. - Each space has a `name`, `description`, and optional `icon` - Descriptions are used by `smart_query` to recommend relevant spaces - Spaces can be archived (soft-deleted) ### Documents A **Document** is content within a space. Supported formats include: - PDF (text-based and scanned with OCR) - Word documents (.docx) - Excel spreadsheets (.xlsx) - PowerPoint presentations (.pptx) - Markdown files (.md) - Plain text files (.txt) - Images (PNG, JPG, etc.) **Document Status:** | Status | Description | | ------ | ----------- | | `PENDING` | Uploaded, waiting for processing | | `PROCESSING` | Being parsed and indexed | | `COMPLETED` | Ready for queries | | `FAILED` | Processing error occurred | **Important:** Only query documents with `status: "COMPLETED"`. Other statuses won't return results. ### Document Trees Documents are indexed into **hierarchical tree structures**: - Each tree has nodes representing sections/chapters - Nodes contain: `title`, `summary`, `startPage`, `endPage`, `textContent` - Trees enable precise navigation of large documents ### Queries Two types of queries are available: 1. **`query_spaces`** - Search document content using tree-bas...
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
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