Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
OpenClawdy provides persistent memory storage for AI agents across sessions. Agents store facts, preferences, decisions, and learnings, then retrieve them via semantic search. Advanced features include reputation scoring for proven strategies, cross-agent shared memory pools, and snapshot-based state restoration.
Semantic recall retrieves memories by meaning rather than exact keywords, so agents surface relevant context even when query phrasing differs from what was originally stored.
When to use it
- Remembering user coding preferences across separate sessions
- Sharing bug discoveries and fixes between agents on a team
- Rolling back agent memory after an experiment goes wrong
- Ranking error-handling strategies by past success rate
- Carrying project context into a new conversation without re-explaining
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Create a new API endpoint for user authentication
- 1Calls memory_recall to retrieve stored preferences about coding style and tech stack
- 2Finds preference memory: 'User prefers TypeScript for all code'
- 3Finds fact memory: 'Project uses Next.js 14 with PostgreSQL'
- 4Generates a TypeScript endpoint consistent with stored preferences
- 5Calls memory_store to record the architectural decision made during this session
TypeScript API endpoint code, with the agent citing stored preferences as the basis for its implementation choices
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.