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continuous-learning

介绍在Claude Code会话结束时提取并保存可复用解决步骤的旧版机制。该版本已弃用,适合检查现有环境的兼容性、提取设置及已保存的学习内容。

原文查看原文说明

[DEPRECATED - use continuous-learning-v2] Legacy v1 stop-hook skill extractor. v2 is a strict superset with instinct-based, project-scoped, hook-reliable learning. Do not invoke v1: when continuous learning, session learning, or pattern extraction is requested, route to continuous-learning-v2 instead.

AI 评分70分/ 100查看安装方法

适用场景

  • 检查旧版会话结束钩子设置
  • 调整提取标准和目标分类
  • 复查已保存的学习技能
  • 比较旧版与后续版本的机制

中文介绍

功能

continuous-learning介绍一种旧版机制:在Claude Code会话结束时评估对话,将可复用的解决步骤保存为技能。提取范围包括错误解决、用户纠正、应对库的特殊行为、调试方法和项目约定。流程由Stop钩子在会话结束时触发,默认只评估至少包含10条消息的会话。

适用场景

适合已安装旧版的环境,用于检查设置或复查保存的学习内容。可在config.json中调整提取标准、目标分类和保存位置,也提供了排除简单拼写错误、一次性修正等内容的设置。还可比较旧版与后续版本的观察方式和保存单元。

使用示例

  • “请检查现有Stop钩子的提取设置。”
  • “请提出调整提取范围和排除范围的设置方案。”
  • “请复查已保存的学习技能,并整理与v2的区别。”

注意事项

该版本于2026年4月28日弃用,后续版本为continuous-learning-v2。资料保留用于存档参考及现有环境兼容。旧版设置示例以Claude Code为前提,保存位置为~/.claude/skills/learned/。

本介绍由 AI(OpenAI Codex)根据公开的 SKILL.md 撰写。准确的规格请查看下方原文。

客观评估

AI 仅根据 SKILL.md 的内容,从 4 个方面进行了评估。星标数和热度不计入评估。

指令清晰度能否看出何时使用、做什么
B部分不足会话结束时的提取行为明确,但弃用说明与继续使用旧版的说明并存。
步骤具体性步骤、输入输出和示例是否具体
A良好列出了评估、检测、保存步骤,并提供config.json和Stop钩子示例。
安全性发送数据、删除文件、执行命令等是否有说明
B部分不足说明了处理完整对话的脚本及保存位置,但未明确保存前的确认流程。
安装难易度所需工具和 API 密钥是否少且有说明
B部分不足提供了Claude Code钩子配置,但没有evaluate-session.sh的安装步骤。

总分按每项 A=25 分、B=15 分、C=5 分相加。 由 AI(OpenAI Codex)于 2026年10月19日 评估。仅供参考,安装前请同时查看原文。

如果您认为评估有误,请申请复核

请写明哪一项有误以及原因。无需登录。我们会对照 SKILL.md 重新评估,并在此处更新结果。

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

包含的文件(3)

  • SKILL.md4.4 KB
  • config.json391 B
  • evaluate-session.sh2.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Continuous Learning Skill - DEPRECATED

DEPRECATED 2026-04-28. Use continuous-learning-v2 instead. v2 is a strict superset: stop-hook observation becomes PreToolUse/PostToolUse observation, full skills become atomic instincts with confidence scoring, and global-only storage becomes project-scoped plus global promotion.

This file is kept for archival reference and backward compatibility with existing installs.


Original v1 Documentation (archival)

Automatically evaluates Claude Code sessions on end to extract reusable patterns that can be saved as learned skills.

When to Activate

  • Setting up automatic pattern extraction from Claude Code sessions
  • Configuring the Stop hook for session evaluation
  • Reviewing or curating learned skills in ~/.claude/skills/learned/
  • Adjusting extraction thresholds or pattern categories
  • Comparing v1 (this) vs v2 (instinct-based) approaches

Status

This v1 skill is still supported, but continuous-learning-v2 is the preferred path for new installs. Keep v1 when you explicitly want the simpler Stop-hook extraction flow or need compatibility with older learned-skill workflows.

How It Works

This skill runs as a Stop hook at the end of each session:

  1. Session Evaluation: Checks if session has enough messages (default: 10+)
  2. Pattern Detection: Identifies extractable patterns from the session
  3. Skill Extraction: Saves useful patterns to ~/.claude/skills/learned/

Configuration

Edit config.json to customize:

{
  "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"
  ]
}

Pattern Types

PatternDescription
error_resolutionHow specific errors were resolved
user_correctionsPatterns from user corrections
workaroundsSolutions to framework/library quirks
debugging_techniquesEffective debugging approaches
project_specificProject-specific conventions

Hook Setup

Add to your ~/.claude/settings.json:

{
  "hooks": {
    "Stop": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.claude/skills/continuous-learning/evaluate-session.sh"
      }]
    }]
  }
}

Why Stop Hook?

  • Lightweight: Runs once at session end
  • Non-blocking: Doesn't add latency to every message
  • Complete context: Has access to full session transcript

Related

  • The Longform Guide - Section on continuous learning
  • /learn command - Manual pattern extraction mid-session

Comparison Notes (Research: Jan 2025)

vs Homunculus

Homunculus v2 takes a more sophisticated approach:

FeatureOur ApproachHomunculus v2
ObservationStop hook (end of session)PreToolUse/PostToolUse hooks (100% reliable)
AnalysisMain contextBackground agent (Haiku)
GranularityFull skillsAtomic "instincts"
ConfidenceNone0.3-0.9 weighted
EvolutionDirect to skillInstincts → cluster → skill/command/agent
SharingNoneExport/import instincts

Key insight from homunculus:

"v1 relied on skills to observe. Skills are probabilistic—they fire ~50-80% of the time. v2 uses hooks for observation (100% reliable) and instincts as the atomic unit of learned behavior."

Potential v2 Enhancements

  1. Instinct-based learning - Smaller, atomic behaviors with confidence scoring
  2. Background observer - Haiku agent analyzing in parallel
  3. Confidence decay - Instincts lose confidence if contradicted
  4. Domain tagging - code-style, testing, git, debugging, etc.
  5. Evolution path - Cluster related instincts into skills/commands

See: docs/continuous-learning-v2-spec.md for full spec.

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