Swt Analysis

Q: What does the Swt Analysis AI skill do?

当用户需要分析企业外部环境、识别行业趋势、寻找竞争对手未发现的 战略机会时调用此技能。典型场景：制定年度战略规划、评估新市场进入、 应对行业结构性变化。不适用于：纯内部运营优化、日常管理决策、 已知信息已足够的情况。关键信号：出现"机会评估""行业趋势""竞争格局" "蓝海""新市场"等词汇。

Community

ace3000chao

swt-analysis

当用户需要分析企业外部环境、识别行业趋势、寻找竞争对手未发现的战略机会时调用此技能。典型场景：制定年度战略规划、评估新市场进入、应对行业结构性变化。不适用于：纯内部运营优化、日常管理决策、已知信息已足够的情况。关键信号：出现"机会评估""行业趋势""竞争格局" "蓝海""新市场"等词汇。

Overview

Publisher	ace3000chao
Repository	`book2startup`
Skill name	`swt-analysis`
Stars	93
Forks	14
Bundled files	1
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ace3000chao on GitHub. Read the source before you install it.

Installation

Install the Swt Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ace3000chao/book2startup/tree/master/ScalingUp-skills/swt-analysis
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/ace3000chao/book2startup.git /tmp/book2startup
mkdir -p .claude/skills
cp -r /tmp/book2startup/ScalingUp-skills/swt-analysis .claude/skills/swt-analysis

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Swt Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Swt Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Swt Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SWT分析（Strengths, Weaknesses, Trends）

R — 原文 (Reading)

"We've augmented SWOT with a tool called SWT — focusing on exploring broader external trends beyond their own industry or geography. It's a powerful tool to spot opportunities before the competition."

— Verne Harnish, 第8章 OPSP

中文翻译： 我们用名为SWT的工具对SWOT进行了增强——专注于探索更广泛的外部趋势，超越自身行业或地理边界。这是一个强大的工具，可以在竞争对手之前发现机会。

I — 方法论骨架 (Interpretation)

SWT分析是对传统SWOT的升级，用"趋势（Trends）"替代"机会"与"威胁"两列，强迫领导者将视线从内部转向外部。

核心逻辑：

传统SWOT容易陷入"内部思维"——优势/劣势都是内部视角，机会/威胁虽然外部，但用户倾向于用内部成功经验推断外部。
SWT直接删除"机会"列——趋势是客观的，机会是从趋势中提炼的。这一步强制用户先看外部，再做判断。
"趋势"涵盖：行业技术演进、政策法规变化、消费者行为迁移、地理市场扩张、供应链重构、竞争对手布局等。
趋势分析要求"跨界对标"——看其他行业、其他地区正在发生什么，再映射回自身。
最终输出：一组可行动的外部趋势，每条趋势对应"谁将被改变"、 "谁将受益"、"我如何借势"。

A1 — 书中的应用 (Past Application)

案例1: 某制造企业的跨行业趋势扫描

问题: 企业高管团队在年度战略会上用传统SWOT分析，所有人聚焦内部运营数据，对外部变化反应迟钝。
方法论的使用: 教练引导团队跳出行业边界，扫描相邻行业（零售、科技、金融）的趋势信号，发现"订阅经济"模式正在B2B领域渗透。
结论: 将产品从"一次性销售"重构为"年度服务合同"，创造75%的客户留存率提升。
结果: 收入可预测性大幅增强，为后续融资奠定基础。

案例2: 地区餐饮连锁的趋势预判

问题: 本地市场份额已触天花板，扩张方向不明。
方法论的使用: 用SWT分析一线城市生活方式趋势（健康轻食、即时配送），向三四线城市迁移的时滞。
结论: 比竞争对手提前18个月布局"健康快餐"品类。
结果: 在竞争尚未饱和的窗口期成为区域品类第一。

案例3: 科技创业公司的技术摩尔陷阱

问题: 团队认为自身技术优势是核心竞争力。
方法论的使用: SWT分析发现头部云厂商正在以 "基础设施免费化"策略向下吞噬市场，技术壁垒窗口期不超过2年。
结论: 放弃纯技术路线，转向"行业Know-how + AI"的应用层壁垒。
结果: 避免了技术护城河被快速填平的风险。

A2 — 触发场景 (Future Trigger) ★

用户会在什么情境下需要这个 skill?

年度战略规划季：团队开始制定下一年战略，需要系统扫描外部环境。
业务增长停滞：现有策略效果减弱，需要新的增长视角。
进入新市场/新产品：需要评估外部条件是否成熟。
竞争对手突然变化：行业出现新玩家或颠覆性动作。
被VC/董事会追问战略：需要展示对外部趋势的理解。

语言信号 (用户的话里出现这些就应激活)

"我们行业的机会在哪里？"
"怎么才能比竞争对手先看到趋势？"
"要不要进入X市场，现在是好时机吗？"
"这个决策要不要考虑外部因素？"
"我们的SWOT分析好像不太对……"

与相邻 skill 的区分

与 f05-seven-strata 的区别: 7 Strata是从内部构建战略壁垒；SWT是外部扫描发现机会入口。 SW是战略构建的前置动作。
与 f07-opsp 的区别: OPSP是战略的执行落地工具；SWT是OPSP中最左侧"外部趋势"列的专项工具，为OPSP提供输入。
与 f17-x-factor 的区别: X-Factor是找10-100倍优势；SWT是找到值得建立X-Factor的外部领域。

E — 可执行步骤 (Execution)

当 skill 被激活后，agent 应按以下步骤执行:

绘制你的"行业边界地图"
- 列出直接竞争对手、替代品、跨界竞争者、相邻行业标杆。
- 完成标准: 至少包含5个外部实体，涵盖至少2个其他行业。
为每个实体扫描3条正在发生的变化
- 技术/产品变化、客群行为迁移、政策/监管动向、资本流向。
- 完成标准: 每条变化记录"信号来源"（不是猜测）。
- 判停条件: 若某外部实体无任何显著变化信号，标记为"低威胁/低机会"，减少精力投入。
将外部变化翻译为"趋势陈述句"并评分
- 格式: "[实体]正在[变化]，[将导致][影响]"
- 评分: 1-5分（对自身业务的冲击程度）
- 输出: 按评分排序的前3条趋势，进入战略决策。
- 完成标准: 每条趋势附带"谁将受益/谁将受损"和"我如何借势"。

B — 边界 (Boundary) ★

不要在以下情况使用此 skill

日常运营决策：如排班、采购价谈判等不涉及外部环境的操作决策。
信息严重不足：对外部趋势完全没有数据来源，强行分析会产生"幻觉趋势"。
情绪压力场景：领导者在焦虑中试图"找机会"， SWT要求冷静客观的数据输入，情绪化分析适得其反。

作者在书中警告的失败模式

用内部成功经验投射外部现实（"我们以前这样做成了，所以趋势一定朝这个方向"）。
只看直接竞争对手，忽视替代品和跨界竞争。
趋势识别后不行动，沦为"年度分析表演"。

作者的盲点 / 时代局限

2014年写就，未充分覆盖AI/平台经济的网络效应分析框架。
趋势扫描依赖定性判断，缺乏量化评分工具（可补充使用 Amara定律：过早起源于炒作期 vs. 真正爆发期）。

容易混淆的邻近方法论

PEST分析：更宏观（政治/经济/社会/技术），SWT更聚焦行业具体的微观趋势；PEST是桌面研究，SWT要求跨界对标。
Porter五力：分析竞争结构（供应商/买家/新进入者等），是静态的；SWT是动态的趋势扫描。

审计信息

验证通过: V1 ✓ / V2 ✓ / V3 ✓
测试通过率: 88% (详见 test-prompts.json)
蒸馏时间: 2026-04-26

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

test-prompts.json3 KB

Frequently asked questions

What does the Swt Analysis AI skill do?

Why use Swt Analysis on TypingMind?

Because you install it once and use it with any model. Swt Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Swt Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ace3000chao/book2startup/tree/master/ScalingUp-skills/swt-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Swt Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Swt Analysis?

As many as you like. As long as a model supports skills, you can use Swt Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Swt Analysis AI skill free?

Yes. It is published on GitHub by ace3000chao under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Swt Analysis

Overview