Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
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What This Skill Does
Indexes workspace files into Telnyx Storage with AI-generated embeddings, enabling semantic search and RAG-powered Q&A over memory, knowledge, and skills directories. Files are chunked by document type, synced incrementally, and retrieved with multi-signal reranking before LLM generation.
Uses Telnyx's native embedding, similarity search, and inference APIs in one pipeline, removing the need to wire together a separate vector database, embedding service, and LLM provider.
When to use it
- Asking questions about past meeting decisions stored in knowledge files
- Searching personal notes and preferences by meaning, not keyword
- Querying documentation for deployment or onboarding steps
- Auto-syncing workspace memory on a cron schedule for always-current retrieval
- Retrieving project history from indexed Slack message exports
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: What is the porting process?
- 1Runs ./sync.py to upload changed workspace files to Telnyx Storage with smart chunking
- 2Triggers ./sync.py --embed to generate vector embeddings for uploaded chunks
- 3Calls ./ask.py with the query, retrieving the top 20 candidate chunks
- 4Reranks chunks by semantic similarity, keyword overlap, and memory priority
- 5Calls Telnyx Inference LLM with the top 8 context chunks and returns the answer with source citations
Natural language answer identifying the porting process steps, with references to the source files
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.