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自动从Claude Code会话中提取可重复使用的模式，并将其保存为学习到的技能以供将来使用。
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
Based on SOC occupation classification
Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents. v2.1 adds project-scoped instincts to prevent cross-project contamination.
Share durable, inspectable context and handoffs between Claude, Codex, Hermes, Cursor, OpenCode, and other agents through the local ECC Memory Vault. Use when an agent must save work state, transfer context, resume another agent's task, or search shared project knowledge.
Share durable, inspectable context and handoffs between Claude, Codex, Hermes, Cursor, OpenCode, and other agents through the local ECC Memory Vault. Use when an agent must save work state, transfer context, resume another agent's task, or search shared project knowledge.
Share durable, inspectable context and handoffs between Claude, Codex, Hermes, Cursor, OpenCode, and other agents through the local ECC Memory Vault. Use when an agent must save work state, transfer context, resume another agent's task, or search shared project knowledge.
Use when multiple consumers and providers must evolve an API or event schema without field drift, integration surprises, or one side silently redefining the interface.
Query live GPU inventory, submit an authenticated Itô fixed-rate RFQ, inspect RFQ or procurement status, and run explicitly gated node qualification through the separately installed canonical CLI. Use when a user asks to find H100/H200 capacity, request a fixed compute rate, check Itô compute status, or validate GPU nodes.
| name | continuous-learning |
| description | 自动从Claude Code会话中提取可重复使用的模式，并将其保存为学习到的技能以供将来使用。 |
| origin | ECC |
自动评估 Claude Code 会话的结尾，以提取可重用的模式，这些模式可以保存为学习到的技能。
~/.claude/skills/learned/ 中审查或整理已学习的技能此技能作为 停止钩子 在每个会话结束时运行：
~/.claude/skills/learned/编辑 config.json 以进行自定义：
{
"min_session_length": 10,
"extraction_threshold": "medium",
"auto_approve": false,
"learned_skills_path": "~/.claude/skills/learned/",
"patterns_to_detect": [
"error_resolution",
"user_corrections",
"workarounds",
"debugging_techniques",
"project_specific"
],
"ignore_patterns": [
"simple_typos",
"one_time_fixes",
"external_api_issues"
]
}
| 模式 | 描述 |
|---|---|
error_resolution | 特定错误是如何解决的 |
user_corrections | 来自用户纠正的模式 |
workarounds | 框架/库特殊性的解决方案 |
debugging_techniques | 有效的调试方法 |
project_specific | 项目特定的约定 |
添加到你的 ~/.claude/settings.json 中：
{
"hooks": {
"Stop": [{
"matcher": "*",
"hooks": [{
"type": "command",
"command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
}]
}]
}
}
/learn 命令 - 在会话中手动提取模式Homunculus v2 采用了更复杂的方法：
| 功能 | 我们的方法 | Homunculus v2 |
|---|---|---|
| 观察 | 停止钩子（会话结束时） | PreToolUse/PostToolUse 钩子（100% 可靠） |
| 分析 | 主上下文 | 后台代理 (Haiku) |
| 粒度 | 完整技能 | 原子化的“本能” |
| 置信度 | 无 | 0.3-0.9 加权 |
| 演进 | 直接到技能 | 本能 → 集群 → 技能/命令/代理 |
| 共享 | 无 | 导出/导入本能 |
来自 homunculus 的关键见解：
"v1 依赖技能来观察。技能是概率性的——它们触发的概率约为 50-80%。v2 使用钩子进行观察（100% 可靠），并以本能作为学习行为的原子单元。"
参见：docs/continuous-learning-v2-spec.md 以获取完整规范。