Agent skill

Continuous Learning V2

by xu-xiang in xu-xiang/everything-claude-code-zh

基于本能的学习系统,通过钩子观察会话,创建具有置信度评分的原子本能,并将其进化为技能/命令/代理. An agent skill from xu-xiang/everything-claude-code-zh.

MITAuto-check passed

Install Continuous Learning V2

skills CLI
$ npx skills add xu-xiang/everything-claude-code-zh --skill continuous-learning-v2 -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install xu-xiang/everything-claude-code-zh continuous-learning-v2 --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/xu-xiang/everything-claude-code-zh.git skills-src && mkdir -p .claude/skills && cp -r skills-src/docs/zh-CN/skills/continuous-learning-v2 .claude/skills/continuous-learning-v2 && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
continuous-learning-v2
GitHub stars
2k
Token cost
~1.6k tokens
SKILL.md length
178 words
Files
2
Skills in repo
78
Repo updated
First seen
Licence
MIT

At a glance

基于本能的学习系统,通过钩子观察会话,创建具有置信度评分的原子本能,并将其进化为技能/命令/代理. An agent skill from xu-xiang/everything-claude-code-zh.

  • Works in 3 steps: 启用观察钩子 → 初始化目录结构 → 使用本能命令
  • SKILL.md covers 何时激活, v2 的新特性, 本能模型 and 工作原理, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Continuous Learning V2 is an agent skill from xu-xiang/everything-claude-code-zh. 基于本能的学习系统,通过钩子观察会话,创建具有置信度评分的原子本能,并将其进化为技能/命令/代理。

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/observer.md`).

The repository describes itself as: everything-claude-code 中文翻译项目:完整的 Claude Code 配置集合(agents, skills, hooks, commands, rules, MCPs)。源自 Anthropic 黑客松获胜者的实战配置,助力中文工程师高效理解与使用 Claude Code。 The licence is MIT.

Example prompts

  • “/continuous-learning-v2”

Requirements

  • Python 3

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. 启用观察钩子
  2. 初始化目录结构
  3. 使用本能命令

What it can do on your machine

Read from SKILL.md and the folder at commit dfbf946. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json, bash and yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • skill-creator.app
    • x.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Continuous Learning V2 loads about 1.6k tokens when it runs. Until then it costs about 18 tokens; SKILL.md has 178 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~18
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from xu-xiang/everything-claude-code-zh at commit dfbf946, republished under its MIT licence (© xu-xiang). 178 words, ~1,574 tokens.

Download SKILL.mdSave it as .claude/skills/continuous-learning-v2/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
continuous-learning-v2
description
基于本能的学习系统,通过钩子观察会话,创建具有置信度评分的原子本能,并将其进化为技能/命令/代理。
origin
ECC
version
2.0.0

持续学习 v2 - 基于本能的架构

一个高级学习系统,通过原子化的“本能”——带有置信度评分的小型习得行为——将你的 Claude Code 会话转化为可重用的知识。

部分灵感来源于 humanplane (credit: @humanplane) 的 Homunculus 项目。

何时激活

  • 设置从 Claude Code 会话中自动学习时
  • 通过钩子配置基于本能的行为提取时
  • 调整学习行为的置信度阈值时
  • 审查、导出或导入本能库时
  • 将本能进化为完整技能、命令或代理时

v2 的新特性

特性v1v2
观察停止钩子(会话结束)工具使用前/后(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 → 聚类 → 技能/命令/代理
共享无导出/导入本能

本能模型

一个本能是一个小型习得行为:

yaml
---
id: prefer-functional-style
trigger: "when writing new functions"
confidence: 0.7
domain: "code-style"
source: "session-observation"
---

# Prefer Functional Style

## Action
Use functional patterns over classes when appropriate.

## Evidence
- Observed 5 instances of functional pattern preference
- User corrected class-based approach to functional on 2025-01-15

属性:

  • 原子性 — 一个触发条件,一个动作
  • 置信度加权 — 0.3 = 尝试性的,0.9 = 近乎确定
  • 领域标记 — 代码风格、测试、git、调试、工作流等
  • 证据支持 — 追踪是哪些观察创建了它

工作原理

Session Activity
      │
      │ Hooks capture prompts + tool use (100% reliable)
      ▼
┌─────────────────────────────────────────┐
│         observations.jsonl              │
│   (prompts, tool calls, outcomes)       │
└─────────────────────────────────────────┘
      │
      │ Observer agent reads (background, Haiku)
      ▼
┌─────────────────────────────────────────┐
│          PATTERN DETECTION              │
│   • User corrections → instinct         │
│   • Error resolutions → instinct        │
│   • Repeated workflows → instinct       │
└─────────────────────────────────────────┘
      │
      │ Creates/updates
      ▼
┌─────────────────────────────────────────┐
│         instincts/personal/             │
│   • prefer-functional.md (0.7)          │
│   • always-test-first.md (0.9)          │
│   • use-zod-validation.md (0.6)         │
└─────────────────────────────────────────┘
      │
      │ /evolve clusters
      ▼
┌─────────────────────────────────────────┐
│              evolved/                   │
│   • commands/new-feature.md             │
│   • skills/testing-workflow.md          │
│   • agents/refactor-specialist.md       │
└─────────────────────────────────────────┘

快速开始

1. 启用观察钩子

添加到你的 ~/.claude/settings.json 中。

如果作为插件安装(推荐):

json
{
  "hooks": {
    "PreToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/hooks/observe.sh pre"
      }]
    }],
    "PostToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "${CLAUDE_PLUGIN_ROOT}/skills/continuous-learning-v2/hooks/observe.sh post"
      }]
    }]
  }
}

如果手动安装到 ~/.claude/skills:

json
{
  "hooks": {
    "PreToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh pre"
      }]
    }],
    "PostToolUse": [{
      "matcher": "*",
      "hooks": [{
        "type": "command",
        "command": "~/.claude/skills/continuous-learning-v2/hooks/observe.sh post"
      }]
    }]
  }
}
2. 初始化目录结构

Python CLI 会自动创建这些目录,但你也可以手动创建:

bash
mkdir -p ~/.claude/homunculus/{instincts/{personal,inherited},evolved/{agents,skills,commands}}
touch ~/.claude/homunculus/observations.jsonl
3. 使用本能命令
bash
/instinct-status     # Show learned instincts with confidence scores
/evolve              # Cluster related instincts into skills/commands
/instinct-export     # Export instincts for sharing
/instinct-import     # Import instincts from others

命令

命令描述
/instinct-status显示所有已习得的本能及其置信度
/evolve将相关本能聚类为技能/命令
/instinct-export导出本能用于共享
/instinct-import <file>从他人处导入本能

配置

编辑 config.json:

json
{
  "version": "2.0",
  "observation": {
    "enabled": true,
    "store_path": "~/.claude/homunculus/observations.jsonl",
    "max_file_size_mb": 10,
    "archive_after_days": 7
  },
  "instincts": {
    "personal_path": "~/.claude/homunculus/instincts/personal/",
    "inherited_path": "~/.claude/homunculus/instincts/inherited/",
    "min_confidence": 0.3,
    "auto_approve_threshold": 0.7,
    "confidence_decay_rate": 0.05
  },
  "observer": {
    "enabled": true,
    "model": "haiku",
    "run_interval_minutes": 5,
    "patterns_to_detect": [
      "user_corrections",
      "error_resolutions",
      "repeated_workflows",
      "tool_preferences"
    ]
  },
  "evolution": {
    "cluster_threshold": 3,
    "evolved_path": "~/.claude/homunculus/evolved/"
  }
}

文件结构

~/.claude/homunculus/
├── identity.json           # Your profile, technical level
├── observations.jsonl      # Current session observations
├── observations.archive/   # Processed observations
├── instincts/
│   ├── personal/           # Auto-learned instincts
│   └── inherited/          # Imported from others
└── evolved/
    ├── agents/             # Generated specialist agents
    ├── skills/             # Generated skills
    └── commands/           # Generated commands

与技能创建器的集成

当你使用 技能创建器 GitHub 应用 时,它现在会生成两者:

  • 传统的 SKILL.md 文件(用于向后兼容)
  • 本能集合(用于 v2 学习系统)

来自仓库分析的本能带有 source: "repo-analysis" 标记,并包含源仓库 URL。

置信度评分

置信度随时间演变:

分数含义行为
0.3尝试性的建议但不强制执行
0.5中等的相关时应用
0.7强烈的自动批准应用
0.9近乎确定的核心行为

置信度增加当:

  • 模式被反复观察到
  • 用户未纠正建议的行为
  • 来自其他来源的相似本能一致

置信度降低当:

  • 用户明确纠正该行为
  • 长时间未观察到该模式
  • 出现矛盾证据

为什么用钩子而非技能进行观察?

“v1 依赖技能进行观察。技能是概率性的——它们基于 Claude 的判断,大约有 50-80% 的概率触发。”

钩子100% 触发,是确定性的。这意味着:

  • 每次工具调用都被观察到
  • 不会错过任何模式
  • 学习是全面的

向后兼容性

v2 与 v1 完全兼容:

  • 现有的 ~/.claude/skills/learned/ 技能仍然有效
  • 停止钩子仍然运行(但现在也输入到 v2)
  • 渐进式迁移路径:并行运行两者

隐私

  • 观察数据保留在你的本地机器上
  • 只有本能(模式)可以被导出
  • 不会共享实际的代码或对话内容
  • 你控制导出的内容

相关链接

  • 技能创建器 - 从仓库历史生成本能
  • Homunculus - 启发了 v2 基于本能的架构的社区项目(原子观察、置信度评分、本能进化管道)
  • 长篇指南 - 持续学习部分

基于本能的学习:一次一个观察,教会 Claude 你的模式。

© xu-xiang, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in docs/zh-CN/skills/continuous-learning-v2 of xu-xiang/everything-claude-code-zh.

  • SKILL.md
  • agents/observer.md

Open the folder on GitHubat commit dfbf946

Compare with similar skills

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Continuous Learning V2 compared with similar skills
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Continuetelegramdesktop/tdesktop33k2 repos~9.4kAutomated safety check: PassGPL-3.0
Continuous Learningaffaan-m/ECC276k—~1.1kAutomated safety check: PassMIT
Continuous Learningaffaan-m/ECC276k—~1.2kAutomated safety check: PassMIT
Continuityparcadei/Continuous-Claude-v33.9k1 repos~292Automated safety check: NotesMIT

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Questions about Continuous Learning V2

What does Continuous Learning V2 do?

基于本能的学习系统,通过钩子观察会话,创建具有置信度评分的原子本能,并将其进化为技能/命令/代理. An agent skill from xu-xiang/everything-claude-code-zh. Continuous Learning V2 is an agent skill from xu-xiang/everything-claude-code-zh.

How do I install Continuous Learning V2 in Claude Code?

Run `npx skills add xu-xiang/everything-claude-code-zh --skill continuous-learning-v2 -a claude-code`. Or copy the skill folder (docs/zh-CN/skills/continuous-learning-v2 in xu-xiang/everything-claude-code-zh) into .claude/skills/continuous-learning-v2 in your project. Claude Code loads it when a task matches its description.

How do I install Continuous Learning V2 in Codex?

Run `npx skills add xu-xiang/everything-claude-code-zh --skill continuous-learning-v2 -a codex`. Or copy the skill folder (docs/zh-CN/skills/continuous-learning-v2 in xu-xiang/everything-claude-code-zh) into .agents/skills/continuous-learning-v2 in your project. Codex loads it when a task matches its description.

Can I use Continuous Learning V2 in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xu-xiang/everything-claude-code-zh --skill continuous-learning-v2 -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/continuous-learning-v2, .gemini/skills/continuous-learning-v2, .github/skills/continuous-learning-v2 and .opencode/skills/continuous-learning-v2 in your project.

What does Continuous Learning V2 need to run?

SKILL.md names no scripts, command-line tools or credentials: Continuous Learning V2 is instructions for the agent only. Our summary lists: Python 3.

Does Continuous Learning V2 access the network?

SKILL.md names 2 domains. As links in the text: skill-creator.app and x.com. This is read from the text; nothing was executed.

Is Continuous Learning V2 safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Continuous Learning V2 use?

Continuous Learning V2 is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Continuous Learning V2 use?

About 1.6k tokens (SKILL.md is roughly 6.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Continuous Learning V2?

Skills that share tags, products or a category with Continuous Learning V2: Continuous Learning V2 (affaan-m/ECC, 276k stars), Continue (telegramdesktop/tdesktop, 33k stars), Continuous Learning (affaan-m/ECC, 276k stars) and Continuous Learning (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Continuous Learning V2?

xu-xiang (a GitHub user) maintains it in xu-xiang/everything-claude-code-zh, which has 1,976 GitHub stars. The repository holds 78 skills in this directory. The repository was last updated on March 5, 2026.

Source: xu-xiang/everything-claude-code-zh on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.