Self-Improving Skill

让任何 writing style skill 自动从人类修改中学习。 只需要两个数据点：AI 原稿 (original) 和人类最终版 (final)。 自动 diff → 提取规则 → 更新目标 SKILL.md。 兼容 Claude Code (~/.claude/skills/) 和 OpenClaw (...

Security Scan

The skill's code and instructions are mostly coherent with its goal (learn from original→final edits and update a target SKILL.md), but it has several practical and security-relevant issues — notably it will send user content to external LLM CLIs, can automatically overwrite SKILL.md, and contains some buggy/inconsistent path-handling — so proceed with caution and review before use.

Purpose & Capability

The skill's name/description match the included scripts: observe.py records original/final pairs and improve.py extracts proposals and can update a target SKILL.md. Some minor inconsistencies exist: registry metadata lists no required binaries but improve.py will try to invoke external LLM CLIs (claude/llm or IMPROVE_LLM_CMD). Path detection logic is duplicated/inconsistent (two different base-detection strategies), which is sloppy but explainable.

Instruction Scope

The runtime instructions and scripts read and write arbitrary SKILL.md files, create logs/backups under home directories, and send the assembled prompt (including SKILL.md excerpts and edit summaries) to whatever LLM CLI is available. That means user content and parts of SKILL.md may be transmitted to third-party LLM services; auto mode can also apply P0 proposals without manual review. These behaviors are within the stated purpose but have significant data-exposure and integrity implications that users must accept explicitly.

Install Mechanism

No external installers or arbitrary downloads are used — this is an instruction-only skill with included Python scripts. Risk from the install mechanism itself is low. The scripts are written to disk when the skill is copied/installed, which is expected.

Credentials

The skill doesn't request secrets or specific env vars up front, and declares optional IMPROVE_LLM_CMD. However, it will invoke external LLM CLIs which may be configured to talk to remote services — recorded content (original/final and SKILL.md excerpts) will be included in LLM prompts and thus could be exposed to those services. The absence of explicit required credentials is consistent with purpose, but users should treat recorded content as potentially exfiltrated to whatever LLM backend they configure.

Persistence & Privilege

The skill does not set always:true and is user-invocable. It can autonomously be run (platform default) and supports cron/auto mode which will auto-apply P0 proposals; combined with the ability to modify a target SKILL.md this grants the skill moderate persistence/impact over the writing-skill it targets. This is plausible for its purpose but worth caution: auto-apply should be used only after governance checks.

What to consider before installing

What to check before installing/use: 1) Understand data flow — observe.py stores full original/final texts locally; improve.py sends excerpts (and the target SKILL.md) to whichever LLM CLI you have (claude/llm or whatever IMPROVE_LLM_CMD points to), so sensitive content can be transmitted to external services. 2) Prefer manual review — use extract/show/apply workflow and avoid 'auto' until you trust proposals; backups and rollback exist but verify they work in your environment. 3) Configure storage paths to a secure location (set SKILL_BASE_DIR / SKILL_LOG_DIR / SKILL_TARGET_PATH) and restrict file permissions. 4) Inspect the scripts before use — there are a few sloppy/incomplete bits (e.g., inconsistent base-path logic and an apparent bug/truncation in auto_improve that may crash), so test on a copy of a non-critical SKILL.md first. 5) If you must analyze private data, run with a local/private LLM or disable automatic networked LLM CLI usage. 6) If you want stronger safety, require human confirmation for all proposals (avoid cron auto apply) and audit proposals before applying.

Self-Improving Skill Framework v1.0

让你的 writing style skill 越用越准。

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🎯 核心概念

AI 写初稿 → 人类改到满意 → 对比两版 → 提取规则 → 更新 skill
     ↑                                              ↓
     └──────── 下次写作自动应用新规则 ←──────────────┘

只有两个数据点：

• original: AI 生成的第一版（不管用了什么 prompt/skill）
• final: 人类最终确认的版本（不管中间改了几轮、怎么改的）

中间过程不记录。Google Doc 来回改了 10 轮？无所谓，只比较首尾。

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📋 使用流程

第一步：配置目标 skill

在你的 writing style skill 目录下创建 self-improving.yaml：

# ~/your-skill/self-improving.yaml
target_skill: ~/path/to/your-writing-style/SKILL.md
log_dir: ~/clawd/memory/skill-runs/your-skill-name/
proposal_dir: ~/clawd/memory/skill-proposals/your-skill-name/
backup_dir: ~/clawd/memory/skill-backups/your-skill-name/

或者用环境变量 / 命令行参数覆盖（见下文）。

第二步：Agent 写完内容后，记录原稿

python3 ~/clawd/skills/self-improving-skill/scripts/observe.py record-original <file> \
  --skill ~/path/to/your-writing-style/

或直接传文本：

python3 observe.py record-original --text "AI 生成的内容..."

输出：

✅ 记录原稿: a3f8c2e1
📝 字数: 1234

记住这个 hash（a3f8c2e1），后面配对用。

第三步：人类确认最终版后，记录 final

python3 observe.py record-final <file> --match a3f8c2e1

如果人类没改直接用了？ 也 record-final，脚本自动检测内容一致 → 标记为无修改（正反馈）。

第四步：提取改进规则

# 手动提取
python3 ~/clawd/skills/self-improving-skill/scripts/improve.py extract --days 7

# 或自动模式（适合 cron）：提取 + 自动应用 P0 规则
python3 improve.py auto

第五步：查看 / 应用 / 回滚

python3 improve.py show        # 查看所有提案
python3 improve.py apply <id>  # 应用某个提案
python3 improve.py rollback    # 回滚上次应用

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⚙️ 自动化（推荐）

集成到你的内容创作流程

在你的 contentgen / writing skill 中加入：

## 写完内容后必须做的事

1. 写完初稿 → `observe.py record-original <file>`
2. 人类确认最终版 → `observe.py record-final <file> --match <hash>`

Cron Job

# 每晚自动提取 + 应用 P0 规则
# schedule: 0 23 * * * (每晚 11pm)
python3 ~/clawd/skills/self-improving-skill/scripts/improve.py auto \
  --skill ~/path/to/your-writing-style/

OpenClaw 用户可以用内置 cron：

cron add --name "skill-daily-extract" --schedule "0 23 * * *" --tz "America/New_York" \
  --payload "运行 improve.py auto 自动提取写作风格改进"

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📂 数据结构

~/clawd/memory/
├── skill-runs/your-skill/
│   └── YYYY-MM-DD.jsonl        # 每日观察日志
│       ├── {type: "original", content_hash, content, context}
│       └── {type: "final", content_hash, original_content, final_content, no_change}
│
├── skill-proposals/your-skill/
│   └── YYYYMMDD-HHMMSS.md     # 改进提案（P0/P1/P2 分级）
│
└── skill-backups/your-skill/
    └── SKILL-YYYYMMDD-HHMMSS.md  # apply 前自动备份

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🔄 循环原理

为什么只看 original vs final？

1. 中间过程有噪音 — 人类可能改了又改回来，中间状态不代表最终偏好
2. 指令不等于规则 — "把开头改一下"是一次性指令，不是通用规则。但如果 final 里开头确实更直接，diff 能自动捕捉
3. 简单就是可靠 — 两个数据点不会出错，复杂流程容易断

什么算好的学习数据？

数据	价值
AI 原稿 2000 字 → final 1800 字（删了废话）	⭐⭐⭐ 高
AI 原稿 → final 完全不变（直接用）	⭐⭐ 正反馈
AI 原稿 500 字 → final 2000 字（大幅扩写）	⭐ 低（扩写靠 prompt 不靠 style）
AI 原稿 → final 只改了一个错别字	⭐ 低（不是风格问题）

P0 / P1 / P2 规则分级

• P0: 高置信度（多次出现同一模式），cron 自动应用
• P1: 中置信度，需要人工确认后应用
• P2: 低置信度（只出现 1 次），存档观察

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🛡 安全机制

1. 每次 apply 前自动备份 SKILL.md → skill-backups/
2. 一键回滚: improve.py rollback
3. auto 模式只应用 P0 — P1/P2 需要人工确认
4. 提案可审核 — 所有提案以 markdown 保存，可读可编辑

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📊 CLI 参考

observe.py

命令	功能
record-original <file>	记录 AI 原稿
record-original --text "..."	直接传文本
record-final <file> --match <hash>	记录最终版
pending	查看待配对原稿
stats	总体统计

improve.py

命令	功能
extract	提取改进建议（默认今天）
extract --days 7	最近 7 天
auto	自动提取 + 应用 P0（cron 用）
show	查看所有提案
apply <id>	应用指定提案
rollback	回滚上次应用

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💡 适用场景

这个 framework 不限于写作风格，任何 "AI 生成 → 人类修改" 的循环都适用：

• Writing style skill（推文、文章、小红书）
• Code review rules（AI 写代码 → 人类 review）
• Email drafting（AI 写邮件 → 人类调整语气）
• Translation style（AI 翻译 → 人类润色）

核心不变：记录 original + final，自动 diff 提取规则。

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🔧 安装

Claude Code 用户

# 方式 1: 直接复制到 skills 目录
cp -r self-improving-skill ~/.claude/skills/

# 方式 2: 项目级 skill
cp -r self-improving-skill ./your-project/.claude/skills/

数据自动存储在 ~/.claude/memory/skill-runs/ 下。

OpenClaw / ClawHub 用户

# 方式 1: ClawHub 安装
npx clawhub@latest install self-improving-skill

# 方式 2: 手动复制
cp -r self-improving-skill ~/clawd/skills/

数据自动存储在 ~/clawd/memory/skill-runs/ 下。

LLM 依赖

improve.py extract/auto 需要一个 LLM CLI 来分析 diff。自动检测：

CLI	安装	说明
claude	Claude Code 自带	优先使用
llm	pip install llm	Simon Willison 的通用 CLI
自定义	IMPROVE_LLM_CMD=...	任意接受 stdin 的命令

observe.py 不依赖任何 LLM，纯 Python，零依赖。

自定义存储路径

# 环境变量
export SKILL_BASE_DIR=~/my-custom-path/memory

# 或命令行参数
python3 observe.py stats --log-dir ~/my-path/skill-runs/my-skill/