Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
Auto-hook wrapper for OpenClaw that queries Whisper Context before each agent generation and ingests the completed turn after. Defaults to delta compression and caching to minimize token use. Works with any stable user_id/session_id pair, with Telegram as the primary target.
Delta compression with locally persisted context hashes prevents re-sending unchanged memory on every turn, so token use stays lower than with full-history approaches.
When to use it
- Persisting user context across Telegram bot sessions
- Recalling past decisions without replaying full chat history
- Injecting compressed long-term memory into agent prompts
- Reducing token costs on high-volume conversational agents
- Running a memory proxy for frameworks that always send full conversation history
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: What did we decide about the project timeline last week?
- 1Calls get_whisper_context with user_id, session_id, and the current query
- 2Receives compressed context string from Whisper Context API
- 3Prepends context to the prompt as 'Relevant long-term memory: ...'
- 4Generates response using the injected memory
- 5Calls ingest_whisper_turn to store the user and assistant messages
Agent responds using past context; the turn is stored for future recall
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.