Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
- Added a complete backend implementation for Backboard integration, including API routes, models, and services. - Introduced backend startup script and configuration files for easy deployment. - Implemented support for assistants, threads, memories, and document RAG operations via local Flask backend. - No changes to the user-facing skill documentation or instructions.
What This Skill Does
Connects to Backboard.io through a local Flask backend to manage AI assistants, conversation threads, persistent memories, and document RAG. Supports uploading PDFs, DOCX, spreadsheets, and code files for retrieval within conversations.
Keeps assistant state, memories, and document indexes in a single local backend rather than managing them across separate services.
When to use it
- Storing user preferences that persist across chat sessions
- Uploading internal docs and querying them in conversation
- Creating purpose-specific assistants for different teams
- Reviewing conversation history across multiple threads
- Accumulating project context as memories over time
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: 'Upload my project spec and tell me what it covers'
- 1Call backboard_upload_document with the file path
- 2Poll backboard_document_status until processing completes
- 3Call backboard_send_message with memory set to Auto to query the indexed document
- 4Return the summary to the user
Document indexed and summary returned based on retrieved content
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.