Agent skill

Continuous Learning V2

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

基于本能(Instinct)的学习系统,通过钩子(hooks)观察会话,创建带有置信度评分的原子本能,并将其演化为技能(Skills)、命令(Commands)或智能体(Agents)。v2.1 版本增加了项目作用域(project-scoped)的本能,以防止跨项目污染。

MITAuto-check passedDevelopment

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/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
~2.1k tokens
SKILL.md length
378 words
Files
8 (incl. scripts)
Skills in repo
78
Repo updated
First seen
Licence
MIT

At a glance

基于本能(Instinct)的学习系统,通过钩子(hooks)观察会话,创建带有置信度评分的原子本能,并将其演化为技能(Skills)、命令(Commands)或智能体(Agents)。v2.1 版本增加了项目作用域(project-scoped)的本能,以防止跨项目污染。

  • Works in 3 steps: 启用观察钩子(Observation Hooks) → 初始化目录结构 → 使用本能命令
  • Development work in your project
  • SKILL.md covers 何时激活, v2.1 的新特性, v2 的新特性(对比 v1) and 本能模型(The Instinct Model), plus 13 more sections
  • Runs Shell and Python scripts from its folder; calls python3 and git

What it does

Continuous Learning V2 is an agent skill from xu-xiang/everything-claude-code-zh. 基于本能(Instinct)的学习系统,通过钩子(hooks)观察会话,创建带有置信度评分的原子本能,并将其演化为技能(Skills)、命令(Commands)或智能体(Agents)。v2.1 版本增加了项目作用域(project-scoped)的本能,以防止跨项目污染。

Its SKILL.md is about 2.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts (for example `agents/observer.md`, `agents/start-observer.sh` and `config.json`).

It sits in Development. It works with Git. 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.

When your agent uses it

  • Development work in your project

Example prompts

  • “/continuous-learning-v2”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

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

  1. 启用观察钩子(Observation Hooks)
  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

    Ships 3 files in scripts/ (Shell and Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • git

    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 2.1k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 378 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~2.1k

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); the scripts in this folder 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). 378 words, ~2,146 tokens.

Download SKILL.mdSave it as .claude/skills/continuous-learning-v2/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
continuous-learning-v2
description
基于本能(Instinct)的学习系统,通过钩子(hooks)观察会话,创建带有置信度评分的原子本能,并将其演化为技能(Skills)、命令(Commands)或智能体(Agents)。v2.1 版本增加了项目作用域(project-scoped)的本能,以防止跨项目污染。
origin
ECC
version
2.1.0

持续学习(Continuous Learning)v2.1 - 基于本能(Instinct)的架构

一个先进的学习系统,通过原子级“本能(Instincts)”——带有置信度评分的小型学习行为,将你的 Claude Code 会话转化为可复用的知识。

v2.1 增加了 项目作用域本能(project-scoped instincts) —— React 模式保留在你的 React 项目中,Python 约定保留在你的 Python 项目中,而通用模式(如“始终验证输入”)则在全局共享。

何时激活

  • 设置从 Claude Code 会话中自动学习
  • 配置通过钩子(hooks)提取基于本能的行为
  • 调整学习行为的置信度阈值
  • 查看、导出或导入本能库
  • 将本能演化为完整的技能(Skills)、命令(Commands)或智能体(Agents)
  • 管理项目作用域与全局本能
  • 将本能从项目作用域提升(Promote)到全局作用域

v2.1 的新特性

特性v2.0v2.1
存储全局 (~/.claude/homunculus/)项目作用域 (projects/<hash>/)
作用域所有本能应用于所有地方项目作用域 + 全局
检测无git remote URL / 仓库路径
提升不适用当在 2 个以上项目中看到时,由项目 → 全局
命令4 个 (status/evolve/export/import)6 个 (+promote/projects)
跨项目存在污染风险默认隔离

v2 的新特性(对比 v1)

特性v1v2
观察Stop 钩子(会话结束)PreToolUse/PostToolUse (100% 可靠)
分析主上下文后台智能体 (Haiku)
粒度完整技能原子级“本能”
置信度无0.3-0.9 加权
演化直接到技能本能 -> 聚类 -> 技能/命令/智能体
分享无导出/导入本能

本能模型(The Instinct Model)

本能是一个小型学习行为:

yaml
---
id: prefer-functional-style
trigger: "when writing new functions"
confidence: 0.7
domain: "code-style"
source: "session-observation"
scope: project
project_id: "a1b2c3d4e5f6"
project_name: "my-react-app"
---

# 偏好函数式风格

## 动作(Action)
在适当时使用函数式模式而非类(classes)。

## 证据(Evidence)
- 观察到 5 次偏好函数式模式的实例
- 用户在 2025-01-15 将基于类的方案纠正为函数式

属性:

  • 原子性(Atomic) —— 一个触发器,一个动作
  • 置信度加权(Confidence-weighted) —— 0.3 = 尝试性, 0.9 = 几乎确定
  • 领域标签(Domain-tagged) —— code-style, testing, git, debugging, workflow 等
  • 证据支持(Evidence-backed) —— 追踪哪些观察结果创建了它
  • 作用域感知(Scope-aware) —— project(默认)或 global

工作原理

会话活动 (在 git 仓库中)
      |
      | 钩子(Hooks)捕获提示词 + 工具使用 (100% 可靠)
      | + 检测项目上下文 (git remote / 仓库路径)
      v
+---------------------------------------------+
|  projects/<project-hash>/observations.jsonl  |
|   (提示词, 工具调用, 结果, 项目)              |
+---------------------------------------------+
      |
      | 观察者智能体读取 (后台, Haiku)
      v
+---------------------------------------------+
|                模式检测                      |
|   * 用户纠正 -> 本能                         |
|   * 错误解决 -> 本能                         |
|   * 重复工作流 -> 本能                       |
|   * 作用域决策:项目还是全局?                |
+---------------------------------------------+
      |
      | 创建/更新
      v
+---------------------------------------------+
|  projects/<project-hash>/instincts/personal/ |
|   * prefer-functional.yaml (0.7) [project]   |
|   * use-react-hooks.yaml (0.9) [project]     |
+---------------------------------------------+
|  instincts/personal/  (全局 GLOBAL)          |
|   * always-validate-input.yaml (0.85) [global]|
|   * grep-before-edit.yaml (0.6) [global]     |
+---------------------------------------------+
      |
      | /evolve 聚类 + /promote 提升
      v
+---------------------------------------------+
|  projects/<hash>/evolved/ (项目作用域)       |
|  evolved/ (全局)                             |
|   * commands/new-feature.md                  |
|   * skills/testing-workflow.md               |
|   * agents/refactor-specialist.md            |
+---------------------------------------------+

项目检测(Project Detection)

系统会自动检测你当前的项目:

  1. CLAUDE_PROJECT_DIR 环境变量 (最高优先级)
  2. git remote get-url origin —— 通过哈希创建可移植的项目 ID (不同机器上的同一仓库获得相同的 ID)
  3. git rev-parse --show-toplevel —— 使用仓库路径作为回退方案 (机器特定)
  4. 全局回退 —— 如果未检测到项目,本能将进入全局作用域

每个项目获得一个 12 位的哈希 ID (例如 a1b2c3d4e5f6)。位于 ~/.claude/homunculus/projects.json 的注册文件将 ID 映射到人类可读的名称。

快速开始

1. 启用观察钩子(Observation Hooks)

添加到你的 ~/.claude/settings.json。

如果作为插件安装 (推荐):

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

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

系统在首次使用时会自动创建目录,但你也可以手动创建:

bash
# 全局目录
mkdir -p ~/.claude/homunculus/{instincts/{personal,inherited},evolved/{agents,skills,commands},projects}

# 项目目录会在钩子首次在 git 仓库中运行时自动创建
3. 使用本能命令
bash
/instinct-status     # 显示已学习的本能 (项目 + 全局)
/evolve              # 将相关的本能聚类为技能/命令
/instinct-export     # 将本能导出到文件
/instinct-import     # 从他人处导入本能
/promote             # 将项目本能提升到全局作用域
/projects            # 列出所有已知的项目及其本能数量

命令

命令描述
/instinct-status显示所有本能 (项目作用域 + 全局) 及其置信度
/evolve将相关的本能聚类为技能/命令,并建议提升
/instinct-export导出本能 (可按作用域/领域过滤)
/instinct-import <file>带有作用域控制地导入本能
/promote [id]将项目本能提升到全局作用域
/projects列出所有已知的项目及其本能数量

配置

编辑 config.json 以控制后台观察者:

json
{
  "version": "2.1",
  "observer": {
    "enabled": false,
    "run_interval_minutes": 5,
    "min_observations_to_analyze": 20
  }
}
键名默认值描述
observer.enabledfalse启用后台观察者智能体
observer.run_interval_minutes5观察者分析观察结果的频率
observer.min_observations_to_analyze20分析运行前最少需要的观察次数

其他行为 (观察捕获、本能阈值、项目作用域划分、提升标准) 通过 instinct-cli.py 和 observe.sh 中的代码默认值进行配置。

文件结构

~/.claude/homunculus/
+-- identity.json           # 你的个人资料,技术水平
+-- projects.json           # 注册表:项目哈希 -> 名称/路径/远程地址
+-- observations.jsonl      # 全局观察结果 (回退方案)
+-- instincts/
|   +-- personal/           # 全局自动学习的本能
|   +-- inherited/          # 全局导入的本能
+-- evolved/
|   +-- agents/             # 全局生成的智能体
|   +-- skills/             # 全局生成的技能
|   +-- commands/           # 全局生成的命令
+-- projects/
    +-- a1b2c3d4e5f6/       # 项目哈希 (来自 git remote URL)
    |   +-- observations.jsonl
    |   +-- observations.archive/
    |   +-- instincts/
    |   |   +-- personal/   # 项目特定的自动学习
    |   |   +-- inherited/  # 项目特定的导入
    |   +-- evolved/
    |       +-- skills/
    |       +-- commands/
    |       +-- agents/
    +-- f6e5d4c3b2a1/       # 另一个项目
        +-- ...
Show full SKILL.md (161 more words)Show less

作用域决策指南

模式类型作用域示例
语言/框架约定项目 (project)"使用 React hooks", "遵循 Django REST 模式"
文件结构偏好项目 (project)"测试文件位于 __tests__/", "组件位于 src/components/"
代码风格项目 (project)"使用函数式风格", "偏好数据类 (dataclasses)"
错误处理策略项目 (project)"使用 Result 类型处理错误"
安全实践全局 (global)"验证用户输入", "清理 SQL (Sanitize SQL)"
通用最佳实践全局 (global)"先写测试", "始终处理错误"
工具工作流偏好全局 (global)"修改前先 Grep", "写入前先读取"
Git 实践全局 (global)"约定式提交", "小型专注的提交"

本能提升 (项目 -> 全局)

当同一个本能在多个项目中以高置信度出现时,它是提升到全局作用域的候选者。

自动提升标准:

  • 相同的本能 ID 出现在 2 个以上项目中
  • 平均置信度 >= 0.8

如何提升:

bash
# 提升特定的本能
python3 instinct-cli.py promote prefer-explicit-errors

# 自动提升所有符合条件的本能
python3 instinct-cli.py promote

# 预览而不应用更改
python3 instinct-cli.py promote --dry-run

/evolve 命令也会建议提升候选者。

置信度评分

置信度随时间演化:

分数含义行为
0.3尝试性建议但不强制执行
0.5中等在相关时应用
0.7强自动批准应用
0.9几乎确定核心行为

置信度增加 当:

  • 模式被重复观察到
  • 用户未纠正建议的行为
  • 来自其他来源的类似本能达成一致

置信度降低 当:

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

为什么使用钩子(Hooks)而非技能(Skills)进行观察?

"v1 依赖技能进行观察。技能是概率性的 —— 基于 Claude 的判断,它们大约有 50-80% 的触发率。"

钩子(Hooks)是 100% 触发 的,具有确定性。这意味着:

  • 每一个工具调用都被观察到
  • 不会遗漏任何模式
  • 学习是全面的

向后兼容性

v2.1 完全兼容 v2.0 和 v1:

  • ~/.claude/homunculus/instincts/ 中现有的全局本能仍作为全局本能工作
  • v1 中现有的 ~/.claude/skills/learned/ 技能仍可工作
  • Stop 钩子仍运行 (但现在也向 v2 提供数据)
  • 平滑迁移:并行运行两者

隐私

  • 观察结果保存在你的机器 本地
  • 项目作用域本能按项目隔离
  • 只有 本能 (模式) 可以被导出 —— 而不是原始观察结果
  • 不会分享实际的代码或对话内容
  • 你可以控制哪些内容被导出和提升

相关内容


基于本能的学习:教会 Claude 你的模式,一次一个项目。

© 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 7 other files (scripts) in skills/continuous-learning-v2 of xu-xiang/everything-claude-code-zh.

  • SKILL.md
  • agents/observer.md
  • agents/start-observer.sh
  • config.json
  • hooks/observe.sh
  • scripts/detect-project.sh
  • scripts/instinct-cli.py
  • scripts/test_parse_instinct.py

Open the folder on GitHubat commit dfbf946

Compare with similar skills

Continuous Learning V2 next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Continuous Learning V2 compared with similar skills
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Code Review ChecklistshareAI-lab/learn-claude-code78k5 repos~1.1kAutomated safety check: PassMIT
Code Design Rationale Investigatorcursor/plugins10k9 repos~2.6kAutomated safety check: PassNone
Contributor-First PR MergeHKUDS/OpenHarness16k1 repos~847Automated safety check: PassMIT
Finishing A Development Branchfarm-fe/farm5.6k34 repos~1.8kAutomated safety check: PassMIT

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  • Backend Patterns

    xu-xiang/everything-claude-code-zh

    后端架构模式、API 设计、数据库优化以及针对 Node.js、Express 和 Next.js API 路由的服务端最佳实践。

    2k GitHub stars~3.2k tokensUpdated 7 mo ago
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  • Clickhouse Io

    xu-xiang/everything-claude-code-zh

    ClickHouse 数据库模式、查询优化、分析以及高性能分析工作负载的数据工程最佳实践. An agent skill from xu-xiang/everything-claude-code-zh.

    2k GitHub stars~2.1k tokensUpdated 7 mo ago
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Questions about Continuous Learning V2

What does Continuous Learning V2 do?

基于本能(Instinct)的学习系统,通过钩子(hooks)观察会话,创建带有置信度评分的原子本能,并将其演化为技能(Skills)、命令(Commands)或智能体(Agents)。v2.1 版本增加了项目作用域(project-scoped)的本能,以防止跨项目污染。. Continuous Learning V2 is an agent skill from xu-xiang/everything-claude-code-zh.

When should I use Continuous Learning V2?

Continuous Learning V2 fits situations like: development work in your project.

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 (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 (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?

Going by SKILL.md and its folder, Continuous Learning V2 needs a shell and Python for the scripts in its folder and the command-line tools its instructions call (python3 and git). Our summary lists: Python 3; A Bash shell.

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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

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 2.1k tokens (SKILL.md is roughly 8.6k 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: Finishing a Development Branch (obra/superpowers, 297k stars), Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars), Code Design Rationale Investigator (cursor/plugins, 10k stars) and Contributor-First PR Merge (HKUDS/OpenHarness, 16k 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.