Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
What This Skill Does
GuavaMemory adds structured episodic memory to OpenClaw agents. It records task outcomes with Q-value scores, searches past episodes by semantic similarity, and promotes high-success patterns into reusable skill procedures. Anti-patterns from failures are tracked to prevent repeating the same mistakes.
Unlike static notes or documentation, Q-value scoring continuously reweights past approaches by actual outcome, so the most reliable procedures surface first rather than the most recent ones.
When to use it
- Recalling which debugging approach worked on a similar error last week
- Avoiding a deployment sequence that previously caused an outage
- Promoting a repeated file-parsing workflow into a reusable skill procedure
- Surfacing the highest-Q-value episode before starting a new refactor task
- Logging a failed API integration attempt as an anti-pattern for future reference
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: debug the failing authentication test suite
- 1Run memory_search with intent 'debug failing tests' to find related episodes
- 2Filter results by Q-value, select top 3 active episodes
- 3Apply the success pattern from the highest-scoring episode
- 4Complete the task, then write ep_YYYYMMDD_NNN.md with intent, context, outcome, and Q-value
- 5Update memory/episodes/index.json to keep the index in sync
Test suite fixed using a recalled pattern; new episode recorded with reward score and feel tag for future retrieval