nbj-ob1-agent-memory-openclaw

Recall and write back provenance-labeled agent memory through OpenClaw plugin tools.

4.5k|872|Updated Mar 11, 2026
One-click install
npx skills add https://github.com/NateBJones-Projects/OB1 --skill nbj-ob1-agent-memory-openclaw
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: nbj-ob1-agent-memory-openclaw
Source: https://github.com/NateBJones-Projects/OB1/tree/main/integrations/openclaw-agent-memory/plugin/skills/openclaw-agent-memory
Command: npx skills add https://github.com/NateBJones-Projects/OB1 --skill nbj-ob1-agent-memory-openclaw

SYSTEM DOCUMENTATION & REQUIREMENTS

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.

Frequently Asked Questions about nbj-ob1-agent-memory-openclaw

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I give an OpenClaw agent persistent memory across tasks?

Use the OB1 agent memory workflow: call openbrain_recall before meaningful work with task type, query, and scope, then call openbrain_writeback after completion with compact decisions, lessons, and next steps. This lets later tasks or agents continue without rereading transcripts.

How does agent memory provenance and use policy work?

Each memory carries a use_policy: can_use_as_instruction memories guide behavior directly, can_use_as_evidence memories inform reasoning only, and requires_user_confirmation memories must be surfaced before reliance. Agent-written memory starts as evidence and becomes instruction only after human confirmation or trusted import.

What should an agent write back to memory after a task?

Write back compact categories: decisions, outputs, lessons, constraints, unresolved questions, next steps, failures, and artifacts. Never store raw transcripts, reasoning traces, secrets, credentials, large code blocks, or private data dumps; store summaries and source references instead.

What happens if the OpenClaw memory tools are unavailable?

The agent continues the task normally and notes that no OB1 recall or write-back occurred. It must not invent remembered facts or fabricate prior context when the openbrain tools cannot be reached.

How is memory handled for code review tasks?

For pull request reviews, the agent recalls repo conventions, prior review comments, recurring bug patterns, and maintainer preferences, then writes back recurring issue patterns, false positives, and reusable repo lessons. Full diffs are never stored; only artifact references to PRs, commits, or files.