What problem does it solve?
AI agents working in OpenClaw lose context between tasks, repeat mistakes, and cannot distinguish trustworthy memories from unverified ones. This Skill enforces disciplined recall before work and compact, provenance-labeled write-back after work so agents share a persistent, reviewable memory layer.
Core Features & Use Cases
- Pre-Task Recall: Query OB1 memory with task type, entities, scope, and sensitivity controls, then apply use-policy rules (instruction, evidence, or requires-confirmation) to returned memories.
- Post-Task Write-Back: Store compact decisions, lessons, constraints, failures, and next steps while excluding transcripts, secrets, and raw code dumps.
- Specialized Modes: Dedicated workflows for code review memory (repo conventions, recurring bug patterns) and long-running TaskFlow work logs that let the next agent continue without reading full transcripts.
- Use Case: During a pull request review, recall prior review comments and maintainer preferences, then write back recurring issue patterns and false positives so future reviews improve.
Quick Start
Ask the agent to recall relevant OB1 memory for this task before starting and write back a compact summary of decisions and lessons when finished.