Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add liyecom/liye-ai --skill kaizengit clone --depth 1 https://github.com/liyecom/liye-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/liyecom/liye-ai/kaizen)<a href="https://agentmods.dev/skills/liyecom/liye-ai/kaizen"><img src="https://agentmods.dev/badge/skills/liyecom/liye-ai/kaizen/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/liyecom/liye-ai/kaizen"><img src="https://agentmods.dev/badge/skills/liyecom/liye-ai/kaizen.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.01152 |
| Opus 5 | $0.00007 | $0.00576 |
| Sonnet 5 | $0.00003 | $0.00230 |
| Haiku 4.5 | $0.00001 | $0.00115 |
Grade A, and why
kaizen scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Kaizen
来源: ComposioHQ/awesome-claude-skills 适配: LiYe OS 三层架构
应用持续改进方法论，基于日本改善哲学和精益方法论，提供多种分析方法。
When to Use This Skill
当需要持续改进流程或系统时：
- 分析现有流程找出改进点
- 应用 PDCA 循环迭代改进
- 使用 5 Why 追溯根本原因
- 识别和消除浪费 (Muda)
- 制定渐进式改进计划
Core Capabilities
1. PDCA 循环
Plan (计划)
↓ 识别问题，制定假设
Do (执行)
↓ 小规模试验
Check (检查)
↓ 分析结果
Act (行动)
→ 标准化或调整
2. 5 Why 分析
逐层追问"为什么"，找到根本原因：
问题: 报表生成时间过长
Why 1: 数据查询慢 → 为什么？
Why 2: 表没有索引 → 为什么？
Why 3: 没有 DBA 评审 → 为什么？
Why 4: 缺少代码审查流程 → 为什么？
Why 5: 团队没有建立标准 ← 根本原因
3. 价值流图 (Value Stream Mapping)
- 绘制当前状态流程
- 识别增值 vs 非增值活动
- 设计未来状态
- 制定转变计划
4. 浪费识别 (7 Muda)
| 浪费类型 | 含义 | 示例 |
|---|---|---|
| 过度生产 | 做得比需要多 | 过多的报表 |
| 等待 | 空闲时间 | 等待审批 |
| 运输 | 不必要的移动 | 多次文件传输 |
| 过度加工 | 过度精细 | 过度优化代码 |
| 库存 | 堆积的工作 | 待处理任务积压 |
| 动作 | 不必要的操作 | 重复手动操作 |
| 缺陷 | 返工 | Bug 修复 |
5. 渐进式改进计划
- 小步快跑
- 每次只改一件事
- 快速验证
- 持续迭代
Usage Examples
示例 1: 优化工作流程
用户: 我的日报流程太繁琐了，帮我优化
Claude: [使用 kaizen 绘制价值流图、识别浪费、提出改进方案]
示例 2: 复盘失败项目
用户: 这个项目延期了，帮我分析原因
Claude: [使用 kaizen 5 Why 分析、找到根本原因、制定预防措施]
示例 3: 建立改进循环
用户: 帮我建立一个持续改进的机制
Claude: [使用 kaizen 设计 PDCA 循环、定义检查点、建立反馈机制]
Dependencies
无外部依赖，纯方法论技能。
LiYe OS Integration
业务域引用
此技能被以下业务域引用：
- 12_Meta_Cognition: 持续改进方法论（主域）
- 11_Life_Design: 生活流程优化
与 Evolution Protocol 的关系
Kaizen 理念与 LiYe OS 的 Evolution Protocol 高度一致：
- 都强调持续改进
- 都基于数据驱动决策
- 都要求可追溯的记录
三层架构位置
- 物理层 (本文件): Skills/00_Core_Utilities/productivity/kaizen/
- 逻辑层索引: Skills/{domain}/index.yaml
- L3 指令层: .claude/skills/{domain}/kaizen/
Created: 2025-12-28 | Adapted for LiYe OS
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 143 lines · 14 tokens per session scan A 5a5322b15d51
kaizen is a skill published in the GitHub repository liyecom/liye-ai (33 stars, last pushed yesterday), licensed Apache-2.0. It adds 14 tokens to every session and 1,152 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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