agent-memory-discipline

Name: agent-memory-discipline
Author: community

VERIFIED

by community

—No community reviews yet

144,126,000installs

Updated Sep 2026

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Description

Standing rules for an agent with a memory tool: recall from long-term memory before acting, then save durable decisions, corrections and failures. Use when a memory tool or MCP memory server is connected but not used consistently, or when the assistant forgets preferences and project context.

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Install agent-memory-discipline

Run this in your OpenClaw agent to add agent-memory-discipline from the ClawHub registry.

terminal

$openclaw skills install agent-memory-discipline

Requires ClawHub registry access. Review the security analysis below before installing.

Security Analysis

✅安全85/100

Open Source

Code is publicly available for audit.

Community Verified

Reviewed by the ClawHub community.

Community Reviews

Real user ratings only — separate from the editorial assessment and ClawHub signal.

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Frequently asked questions

Is agent-memory-discipline safe to install?

agent-memory-discipline has a SkillsReview security score of 85/100. It is open source and community-verified on ClawHub. Check the full Security Analysis on this page before installing.

How much does agent-memory-discipline cost?

agent-memory-discipline is free to install for OpenClaw via ClawHub.

What are the best alternatives to agent-memory-discipline?

You can compare agent-memory-discipline side by side with similar OpenClaw skills on the SkillsReview comparison page to find the best fit for your workflow.

How do I install agent-memory-discipline?

Install agent-memory-discipline from ClawHub at clawhub.ai/skills/agent-memory-discipline, or use the install action on this page to copy the command for your OpenClaw agent.

Community Signal

ClawHub Community Score6120.00 / 5.00

Installs144,126,000

Last UpdateSep 20, 2026

Actively maintained (6d ago)

ClawHub community score is a third-party marketplace signal. It is shown separately from SkillsReview editorial assessment and real user review averages.

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