From agentmind
Self-learning memory system that makes AI agents understand you better over time.
npx claudepluginhub youhai020616/agentmind --plugin agentmindThis skill uses the workspace's default tool permissions.
Self-learning memory system that makes AI agents understand you better over time.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Guides building MCP servers enabling LLMs to interact with external services via tools. Covers best practices, TypeScript/Node (MCP SDK), Python (FastMCP).
Generates original PNG/PDF visual art via design philosophy manifestos for posters, graphics, and static designs on user request.
Self-learning memory system that makes AI agents understand you better over time. Automatically detects your preferences, workflows, and code style from interactions.
Use when user asks about learned preferences, wants to review agent behavior patterns, or when starting a session that should leverage previously learned knowledge.
/agentmind status — Show learning overview/agentmind instincts — List learned behaviors/agentmind evolve — Trigger evolution cycle/agentmind preferences — Show detected preferences/agentmind approve <id> — Approve a learned behavior/agentmind reject <id> — Reject a learned behaviorUnlike the Claude Code plugin which uses lifecycle hooks, the OpenClaw adaptation works through:
memory/*.md files and conversation history to detect patternsanalyze to discover new patterns from recent interactions# Install as OpenClaw skill
cp -r skills/agentmind-openclaw ~/.openclaw/skills/agentmind/
# Create cron for periodic learning (every 6 hours)
openclaw cron create --name "AgentMind Learn" \
--schedule "0 */6 * * *" \
--task "Run AgentMind analysis on recent memory files"
~/.openclaw/workspace/agentmind/instincts.json~/.openclaw/workspace/agentmind/observations/AgentMind complements OpenClaw's built-in memory_search (pull-based) with
push-based injection — high-confidence instincts are automatically included
in the agent's context without needing to search for them.