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Functional Analysis Optimizer

by Jack-xun · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install functional-analysis-optimizer
Description
功能分析法优化器。当用户提到"优化"、"功能分析"、"FAST图"、"拆解问题"、"拆解一切问题"时激活。适用场景:优化某个产品、流程、系统或任意对象(如外卖保温袋、周报流程、App功能、网页设计),要求按功能拆解的方式找到改进路径。
README (SKILL.md)

功能分析法优化器

基于《拆解一切问题》中的功能分析法,将"优化某物"拆为六步:锚定 → 功能建模 → 诊断 → 创新 → 收敛 → 落地

快速开始

用户提供待优化对象和目标后,按下方六步模板引导用户逐步分析。

第一步:锚定范围

输出格式:

🎯 优化对象:\x3Ctarget>
📈 核心目标:\x3Cmain_objective>

一句话确认:"我们要优化的是『{target}』,核心指标是『{objective}』,对吗?"


第二步:功能建模(动词+名词)

总功能 → 一级子功能 → 二级子功能 三层结构建模。

输出格式:

总功能:\x3C动词+名词,如'保持温度'>

├─ 子功能1:\x3C动词+名词>
│   └─ 二级:\x3C动词+名词>
├─ 子功能2:\x3C动词+名词>
└─ 子功能3:\x3C动词+名词>

每个功能节点必须是"动词+名词"结构(如"隔绝热传导"而非"热传导")。

追问提示(选一个未展开的节点追问):

"这个子功能还能再拆吗?比如'隔绝热传导'可以拆成'阻止直接接触'和'减少空气对流'——对吗?"


第三步:现状诊断

对照功能树,标注每个节点的状态:

状态 含义 标记
过载 🔴 功能过剩、成本过高或用户不需要 🔴
缺失 🟡 本该有却没有 🟡
低效 🟠 实现方式笨拙,有更好的替代 🟠
正常 🟢 当前状态可接受 🟢

输出格式:

总功能:\x3C总功能描述> [🟢]
├─ 子功能1:\x3C描述> [🔴 过载 — 说明原因]
├─ 子功能2:\x3C描述> [🟡 缺失 — 说明缺什么]
└─ 子功能3:\x3C描述> [🟠 低效 — 说明为什么笨拙]

第四步:创新发散

从诊断结果中选最痛的一个节点,提出核心问题:

"这个功能,还能用什么完全不同的方式实现?"

输出格式:

痛点节点:\x3C选定的节点>
当前方案:\x3C现有实现方式>

创新路径 A:\x3C替代方案1>
创新路径 B:\x3C替代方案2>
创新路径 C:\x3C替代方案3>

每个路径说明:

  • 核心原理:用什么不同机制实现同一功能
  • 预估成本:改动幅度(高/中/低)
  • 潜在优势:相比原方案好在哪

第五步:方案收敛

三问筛掉不靠谱选项:

问题 筛选标准
①能否100%满足必需功能? 不满足任何必需功能 → 淘汰
②改动收益>成本? 成本明显大于收益 → 淘汰
③与现有系统兼容吗? 严重冲突且无法适配 → 淘汰

输出格式:

创新路径 A:[通过/淘汰] — \x3C理由>
创新路径 B:[通过/淘汰] — \x3C理由>
创新路径 C:[通过/淘汰] — \x3C理由>

✅ 入选方案:\x3C通过三问的方案,简述核心改动>

第六步:落地切片

给出最小可试验版本

输出格式:

最小可试错切片:
- 改动范围:只改\x3C具体子功能节点>
- 验证方式:\x3C一次循环/一周试用/A-B测试>
- 核心指标:\x3C如何量化证明有效>
- 下一步决策:如果\x3C条件>则扩大推广,否则\x3C替代方案>

参考资源

  • FAST 功能分析法的详细说明与案例 → references/fast-guide.md
  • 功能建模的常见错误与修正 → references/common-mistakes.md
Usage Guidance
This skill is an offline, instruction-only consultant for FAST-style problem decomposition and optimization — it does not require credentials or install code, so technical risk is low. Consider: (1) verify any sensitive or proprietary details you share with the skill before sending them, (2) review suggested changes before implementing operationally, and (3) if you do not want the agent to call the skill autonomously, disable model invocation or require explicit user invocation in your agent settings.
Capability Analysis
Type: OpenClaw Skill Name: functional-analysis-optimizer Version: 1.0.0 The skill bundle is a purely instructional framework for an AI agent to perform functional analysis (FAST method) for process optimization. It contains no executable code, network requests, or instructions to access sensitive system data, and its content is entirely aligned with its stated purpose across SKILL.md and the reference documentation.
Capability Assessment
Purpose & Capability
The name and description describe a FAST-based optimization helper. The skill is instruction-only, requests no binaries, env vars, or installs, and the included reference files are documentation about FAST — all proportional to the declared purpose.
Instruction Scope
SKILL.md contains step-by-step prompts and output templates for walking a user through six FAST stages (anchor → model → diagnose → ideate → converge → pilot). It does not instruct the agent to read system files, access environment variables, call external endpoints, or exfiltrate data; it only asks for user-provided target and objectives.
Install Mechanism
No install spec or code files are provided (instruction-only). This is low-risk: nothing is downloaded or written to disk by the skill itself.
Credentials
The skill requires no credentials, config paths, or environment variables. Its guidance and reference files do not rely on or request secrets — proportional for a facilitation/consulting skill.
Persistence & Privilege
Flags: always=false (not force-included) and disable-model-invocation=false (normal — allows autonomous invocation). There is no request to modify other skills or system-wide config. Privilege level is appropriate for its function.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install functional-analysis-optimizer
  3. After installation, invoke the skill by name or use /functional-analysis-optimizer
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- 首次发布功能分析法优化器,支持通过六步法(锚定、建模、诊断、创新、收敛、落地)系统优化任意对象。 - 自动激活于“优化”、“功能分析”、“FAST图”、“拆解问题”等相关需求场景。 - 按动词+名词方式进行功能拆解与诊断,并输出可执行的创新和收敛路径。 - 提供明确输出格式和追问提示,便于逐步引导用户完成优化流程。
Metadata
Slug functional-analysis-optimizer
Version 1.0.0
License MIT-0
All-time Installs 0
Active Installs 0
Total Versions 1
Frequently Asked Questions

What is Functional Analysis Optimizer?

功能分析法优化器。当用户提到"优化"、"功能分析"、"FAST图"、"拆解问题"、"拆解一切问题"时激活。适用场景:优化某个产品、流程、系统或任意对象(如外卖保温袋、周报流程、App功能、网页设计),要求按功能拆解的方式找到改进路径。 It is an AI Agent Skill for Claude Code / OpenClaw, with 71 downloads so far.

How do I install Functional Analysis Optimizer?

Run "/install functional-analysis-optimizer" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Functional Analysis Optimizer free?

Yes, Functional Analysis Optimizer is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Functional Analysis Optimizer support?

Functional Analysis Optimizer is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Functional Analysis Optimizer?

It is built and maintained by Jack-xun (@jack-xun); the current version is v1.0.0.

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