Reasoningbank Intelligence
技能 活跃Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
To provide AI agents with a self-learning capability that allows them to improve their performance over time by learning from past task outcomes and optimizing their strategies.
功能
- Adaptive learning system (ReasoningBank)
- Pattern recognition from task data
- Strategy optimization and recommendation
- Continuous learning and meta-learning
- Integration with AgentDB for persistence
使用场景
- Building self-learning agents
- Optimizing AI agent workflows
- Implementing meta-cognitive systems
- Improving strategy selection based on historical data
非目标
- Performing the core tasks the agent is designed for (e.g., coding, analysis)
- Replacing the underlying LLM or agent framework
- Managing external system integrations beyond data persistence
Trust
- warning:Issues AttentionThere are 68 open issues and 373 closed issues in the last 90 days, indicating a high volume of activity but potentially slow resolution times for open issues.
安装
npx skills add ruvnet/ruflo通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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97Sustained neutral pattern recognition across systems without urgency or intervention. Maps naturalist field study methodology to AI reasoning: framing the observation target, witnessing with sustained attention, recording patterns, categorizing findings, generating hypotheses, and archiving a pattern library for future reference. Use when a system's behavior is unclear and action would be premature, when debugging an unknown root cause, when a codebase change needs its effects witnessed before further changes, or when auditing own reasoning patterns for biases or recurring errors.
Teach
96AI knowledge transfer calibrated to learner level and needs. Models the learner's mental state, scaffolds from known to unknown using Vygotsky's Zone of Proximal Development, employs Socratic questioning to verify understanding, and adapts explanations based on feedback signals. Use when a user asks "how does X work?" and needs graduated explanation, when their questions reveal a conceptual gap, when previous explanations have not landed, or when teaching a concept that depends on prerequisites the learner may not yet have.
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