最近 Skill 这个概念越来越火。无论是 Claude Code、Codex，还是各种 Agent 平台，都在推动 Skill 生态的发展。开发者可以把一套提示词、工具调用逻辑、工作流程和领域知识封装成 Skill，让 AI 在需要的时候自动调用。
这看起来是一个很好的创业机会：既然 Skill 可以复用，是不是就可以像过去的软件插件一样，建立一个 Skill 市场，让开发者出售自己的 Skill？
我认为，这条路存在一个很大的商业化障碍：Skill 很容易被使用，却不一定容易被单独收费。真正有商业价值的，可能不是 Skill 本身，而是基于 Skill 构建出来的 Bot。
一、Skill 的价值很高，但它不是最终交付物
先理解一下 Skill 到底是什么。
Skill 本质上是对 AI 能力的封装。比如，一个程序员可以创建一个代码审查 Skill，里面包含代码审查规范、检查流程、常见问题和处理方法。当 Agent 遇到代码审查任务时，就可以加载这个 Skill，按照预设的方法完成工作。
类似的 Skill 还有很多：SEO 内容创作、竞品分析、数据分析、PPT 制作、财务报表分析、UI 设计等。
Skill 解决的是一个非常重要的问题：如何让 AI 具备可复用的专业能力。
但这里存在一个商业问题：用户购买 Skill，最终还是需要自己去调用 AI、提供上下文、配置工具、处理异常，并检查最终结果。
换句话说，Skill 提供的是一种能力，而不是一个完整的服务。
这就像你卖给用户一个 Excel 模板。模板确实有价值，但用户还需要自己准备数据、填写内容、分析结果。对于懂技术的人来说，这没有太大问题；但对于普通用户来说，使用门槛依然存在。
更重要的是，Skill 的内容往往可以被复制、修改，甚至通过 AI 重新生成。当一个 Skill 的核心价值主要体现在提示词和工作流程上时，它的独立定价能力就会受到挑战。
二、为什么 Skill 很难形成稳定的付费模式？
我认为主要有三个原因。
第一，Skill 的价值难以独立衡量。
用户很难直接判断一个 Skill 值多少钱。一个代码审查 Skill，究竟应该卖 5 美元、20 美元，还是 100 美元？它的价值取决于底层模型、Agent 能力、用户输入的数据以及实际使用场景。
同一个 Skill，在不同模型和不同环境下，可能产生完全不同的结果。用户购买的并不是一个稳定的软件产品，而是一套依赖外部环境运行的能力配置。
第二，Skill 的替代成本越来越低。
过去，开发一个专业工具需要大量工程投入。现在，用户只需要告诉 AI 自己想做什么，AI 就可能帮他生成一套类似的 Skill。
当然，专业 Skill 仍然有知识沉淀、经验和工程质量上的优势，但仅仅依靠提示词和流程定义，很难建立长期竞争壁垒。
第三，Skill 缺少完整的用户交付闭环。
用户真正关心的不是你有没有一个优秀的 Skill，而是你能不能帮我完成工作。
例如，用户想做一次竞品分析，他不想研究应该安装什么 Skill，也不想配置 Agent，更不想自己处理各种工具调用。他只希望输入几个竞争对手的名称，最后得到一份完整的竞品分析报告。
这两种产品形态的商业价值显然不同。
三、Bot 为什么更容易商业化？
这里说的 Bot，并不是简单地套一层聊天界面，而是一个面向特定用户、特定场景，能够独立完成任务的 AI 产品。
例如：
- SEO Bot：输入网站地址，自动分析 SEO 问题，生成优化方案。
- Research Bot：输入一个研究主题，自动搜索资料、分析信息、整理成报告。
- Data Bot：连接数据库，用自然语言提问，自动查询数据并生成可视化报表。
- Design Bot：输入产品需求，自动生成 UI 设计稿和页面代码。
- Marketing Bot：输入产品信息，自动生成营销内容、分发计划，并跟踪运营效果。
你会发现，这些产品和 Skill 最大的区别在于：Skill 交付能力，Bot 交付结果。
用户不需要理解底层用了什么 Skill，也不需要知道 Agent 是如何规划任务的。他只需要知道这个 Bot 能帮自己解决什么问题。
这种产品形态天然更容易建立商业模式。
第一，Bot 有明确的付费理由。
用户不会因为你使用了多么复杂的 Prompt 而付费，但会因为你帮他节省了 10 个小时而付费；不会因为你有多少个 Skill 而付费，但会因为你每天帮他完成一份竞品分析报告而付费。
商业化的关键不是技术有多先进，而是用户能否感知到价值。
第二，Bot 可以形成持续性收入。
Skill 通常是一次性购买或者免费安装，后续用户是否继续使用，很难形成稳定的收入。
但 Bot 不一样。
如果一个 Bot 能够持续承担用户的工作，比如每天监控市场、每周生成数据报告、持续运营社交媒体，那么它就具备了订阅收费的基础。
甚至可以按照任务次数、执行量、交付结果收费。
第三，Bot 更容易建立竞争壁垒。
单个 Skill 很容易被复制，但一个真正好用的 Bot，背后可能包含多个 Skill、MCP 工具、知识库、业务数据、用户记忆、任务调度和执行反馈。
这些能力组合起来，才构成完整的产品。
尤其是当 Bot 开始积累用户数据、业务上下文和执行历史之后，后来者想要复制的就不再是一份 SKILL.md，而是一套完整的业务系统。
四、Skill 和 Bot，其实是两种不同的商业定位
我更愿意把它们理解成 AI 时代的两层生态。
Bot：面向用户的商业产品
解决问题、交付结果、建立用户关系、持续收费
能力组合与任务编排
Skill
专业能力
MCP
外部工具
Memory
上下文记忆
模型与 Agent Runtime
推理、规划、执行、调度
Skill 负责沉淀专业经验，MCP 负责连接外部工具，Memory 负责保存上下文，Agent Runtime 负责调度和执行，而 Bot 把这些能力组合成一个可以交付的产品。
这也意味着，未来的商业机会不一定是建立一个庞大的 Skill 市场，而是围绕具体行业和场景，打造真正能够解决问题的 Bot。
当然，Skill 也不是完全没有商业化机会。专业领域的高质量 Skill、私有知识和持续维护的工作流，仍然可以收费。只是它更适合作为 Bot 的能力组件，或者面向开发者的基础设施，而不是所有 Skill 都能直接成为独立商品。
五、未来的创业机会：不要卖 Skill，要卖 Bot
如果今天让我选择一个 AI 创业方向，我会优先考虑 Bot，而不是单纯做 Skill 市场。
原因很简单：Skill 市场解决的是供给问题，Bot 产品解决的是需求问题。
开发者希望分享自己的能力，用户希望有人替自己完成工作。这两个需求并不完全相同。
举个例子，如果你想进入跨境电商领域，可以有两种思路：
第一种，开发一个跨境电商 Skill，帮助用户分析日本商品、生成商品描述、整理采购信息。然后把它发布到 Skill 市场，等待用户购买。
第二种，直接开发一个跨境电商采购 Bot。用户输入商品需求，Bot 自动搜索日本供应商、比较价格、整理商品信息、生成采购清单，必要时再由人工确认交易。
前者卖的是工具，后者卖的是服务。
后者不仅可以按月订阅，还可以按照采购订单、交易金额或者实际服务结果收费。用户甚至不需要知道背后究竟使用了多少个 Skill。
这才是我认为更有想象空间的商业模式。
当然，Bot 并不意味着套一层聊天界面就能收费。真正有价值的 Bot 必须具备可靠的执行能力、明确的交付标准、成本控制和异常处理机制。特别是涉及资金、订单和企业数据时，还必须具备权限控制和人工确认机制。
六、AI 时代，真正值得思考的是谁拥有用户入口
回顾过去的软件发展，我们经历了几个阶段。
Web 时代，大家卖网站和软件；移动互联网时代，大家卖 App 和服务；AI 时代，用户可能不再关心自己使用的是哪个软件，而是直接告诉 AI 自己想完成什么任务。
这意味着产品的形态可能发生变化。
过去，用户需要打开 ERP、CRM、Excel，自己操作软件完成工作。未来，用户可能只需要告诉一个 Bot：帮我分析这个月的销售数据，找出业绩下降的原因，并生成一份报告。
软件逐渐从一个需要用户操作的工具，变成一个能够替用户执行任务的服务。
在这个过程中，Skill 是能力的标准化，Bot 是服务的产品化。
谁能把 AI 能力包装成稳定、可靠、可持续交付的服务，谁就更有机会获得商业价值。
我并不是认为 Skill 没有未来，而是认为 Skill 的价值更多体现在能力层，Bot 的价值则体现在业务层。
未来可能会有大量免费的 Skill，但真正能够持续赚钱的，是那些深入具体业务场景、掌握用户入口、能够持续交付结果的 Bot。
不要只想着开发一个更好的 Skill，而应该思考：能不能用这些 Skill，替用户完成一份真正有价值的工作？
这可能才是 AI Agent 商业化最值得关注的方向。
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Recently, the Skill concept is getting hotter. Claude Code, Codex, and Agent platforms are driving Skyll ecology. Developer can encapsulate a set of prompt, tool call logic, workflow and field knowledge into Skill, allowing AI to call automatically when needed.
This looks like a good opportunity to start a business: since Skill can be reused, can you build a Skill market like the old software plugin that allows developers to sell their Skill?
I think there's a big barrier to commercialization: Skyll is easy to use, but not necessarily single-charged. What really has commercial value may not be Skill itself, but based on Skill built Bot.
I. Skill is valuable, but it is not final delivery Objects
Let's see what Skill is.
Skill is essentially an AI capacity envelope. For example, a programmer could create a code review, Skill, which would include code review specifications, check processes, frequently asked questions and handling methods. When Agent encounters a code review task, you can load this Skill to do the job according to a predefined method.
There are many similar Skills: SEO content creation, competition analysis, data analysis, PPT production, financial statement analysis, UI design, etc.
Skill solves a very important question: how to get AI to have reusable expertise.
But there is a business problem: users buy Skill, and eventually they have to call AI, provide context, configure tools, deal with anomalies and check the final results.
In other words, Skill offers a capacity, not a complete service.
It's like selling a Excel template to a user. The template does have value, but users also need to prepare their own data, fill in their content and analyse the results. This is not a problem for those who understand technology; however, for ordinary users, the threshold for use remains.
More importantly, the content of Skill can often be copied, modified, or even regenerated through AI. When the core value of a Skill is reflected mainly in the prompt and workflow process, its independent pricing capacity is challenged.
ii. Why is it difficult for Skill to form a stable paid model?
I think there are three main reasons.
First, the value of Skill is difficult to measure independently.
It's hard for a user to judge directly how much a Skill is worth. A code review, Skill, should we sell $5, $20, or $100? Its value depends on the bottom model, the Agent capabilities, the data entered by users and the actual use of the scene.
The same Skill, in different models and environments, may produce completely different results. Users do not buy a stable software product, but a set of capabilities that depend on the outside environment.
Second, Skyll's replacement costs are getting lower.
In the past, the development of a professional tool required considerable engineering input. Now, users just have to tell AI what they want to do, and AI could help him generate a similar Skill.
Of course, professional Skill still has the advantage of knowledge retention, experience and engineering quality, but it is difficult to establish long-term barriers to competition by prompt and process definition alone.
Third, Skill lacks a complete user delivery loop.
The real concern of the users is not whether you have a good Skill, but whether you can help me with my work.
For example, the user wanted to do a competitive analysis, he did not want to study what to install, he didn't want to configure Agent, and he didn't want to deal with tool calls. He would only like to enter the names of several competitors, and eventually get a full analysis of the competition.
The commercial value of the two product forms is clearly different.
Why is it easier to commercialize?
Bot here is not simply a chat interface, but an AI product that is user-specific, context-specific and able to perform tasks independently.
For example:
- SEO Bot: Enter the address of the website, analyse the SEO problem automatically and generate an optimisation scheme.
- Research Bot: Enter a research theme, automatically search for information, analyse information and organize reports.
- Data Bot: connect databases, ask questions in natural languages, automatically query data and generate visualized reports.
- Design Bot: Enter product demand, automatically generate UI designs and page codes.
- Marking Bot: Enter product information, automatically generate marketing content, distribution plans and track operational results.
You'll find that the biggest difference between these products and Skyll is that Skyll delivers, Bot delivers.
Users don't need to understand what the bottom uses, Skill, or how Agent planned the mission. He just needs to know what Bot can do for himself.
This product pattern is naturally easier to establish business models.
First, Bot has a clear reason for paid.
Users don't use paid because of how complicated you are with Prompt, but because you saved him 10 hours and Z105Z; not because of how many Skills you have and Z105Z, but because you help him complete a competition analysis every day.
The key to commercialization is not how advanced technology is, but whether the user can sense the value.
Second, Bot can generate sustained income.
Skill is usually a one-time purchase or free installation, and it is difficult to generate a stable income for subsequent users to continue using.
But Bot is different.
If a Bot can sustain the work of users, such as daily market monitoring, weekly data reporting and continuous operation of social media, it has the basis for subscription fees.
They can even be charged according to the number of assignments, the volume of implementation and the results of delivery.
Third, Bot is easier to build barriers to competition.
A single Skill is easy to copy, but a really good Bot may contain multiple Skill, MCP tools, knowledge base, business data, user memory, tasking and implementation feedback.
These capabilities are combined to constitute a complete product.
In particular, when Bot began to accumulate user data, business context and implementation history, what they wanted to replicate was no longer a SKILL.md, but a complete business system.
Four, Skill and Bot are actually two different commercial locations.
I'd rather read them into two layers of the AI era.
Bot: Commercial products for users
Problem resolution, delivery results, establishment of user relationships, ongoing fees
Portfolio and tasking
Skill
Professional competence
MCP
External tools
Memory
context Memory
Model with Agent Runtime
Inference, planning, execution, movement control
Skill is in charge of sedimenting professional experience, MCP is in charge of connecting external tools, Memoory is in charge of preserving context, Agent Runtme is in charge of dispatching and execution, and Bot combines these capabilities into a deliverables.
This also means that future business opportunities need not necessarily be to create a large Skill market, but rather to create Bot that can really solve problems around specific industries and scenarios.
Of course, Skill wasn't completely out of business. High-quality Skill, private knowledge and workflow for continuous maintenance in the field of expertise are still chargeable. It's just better suited to be a capacity component of Bot, or an infrastructure for developers, rather than all Skylls being a stand-alone commodity.
V. FUTURE OPPORTUNITY: NOT SKILL, TO BOT
If I were to choose an AI entrepreneurial direction today, I would give priority to Bot, not just the Skill market.
The reason is simple: the Skill market addresses supply and the Bot product addresses demand.
Developers want to share their capabilities and users want someone to do their job for themselves. The two needs are not identical.
For example, if you want to enter the field of cross-border electricians, there are two ideas:
The first is the development of a cross-border electrician, Skill, to help users analyse Japanese commodities, generate commodity descriptions and collate procurement information. Then take it publish to the Skill market, waiting for the user to buy it.
Second, direct development of a cross-border electrician procurement Bot. Users enter commodity demand, Bot automatically search Japanese suppliers, compare prices, collate commodity information, generate procurement lists and then trade with human confirmation as necessary.
The former sells tools, while the latter sells services.
The latter can not only be subscribed on a monthly basis, but may also be charged on the basis of purchase orders, transaction amounts or actual service results. Users do not even need to know how many Skills are being used behind them.
This is the business model that I think is more imaginative.
Of course, Bot doesn't mean a chat interface can charge. A truly valuable Bot must have a reliable implementation capability, clear delivery standards, cost control and anomalous mechanisms. In particular, permission controls and human confirmation mechanisms must also be in place for data on funds, orders and businesses.
VI. THE ISAI TIME, THE REAL READY READY WHO HAS A UTILIZATION
Looking back at past software developments, we have gone through several stages.
In the Web era, people sell websites and software; in the mobile Internet age, people sell Apps and services; and in the AI era, users may no longer care which software they use, but rather tell AI what they want to do.
This means that the shape of the product may change.
In the past, users needed to open ERP, CRM, Excel and run their own software. In the future, the user may need only tell one Bot: help me analyse the sales data for this month, identify the reasons for the decline and generate a report.
The software has gradually evolved from a tool that requires user-driven operations to a service that can perform tasks on behalf of users.
In the process, Skyll is the standardization of capabilities, Bot is the product of services.
Those who are able to package AI competencies as services delivered in a stable, reliable and sustainable manner have greater access to commercial value.
I don't think that Skill has no future, but I think that the value of Skill is more at the competency level, and the value of Bot is at the business level.
There may be a large number of free Skills in the future, but what is really sustainable is those Bots that go deep into specific business scenarios, have access to users and can deliver results on a sustainable basis.
Instead of thinking about developing a better Skill, let's think: Can we use this Skill to do a really valuable job for users?
This is probably the most interesting direction for the commercialization of AI Agent.
Auto-fetched and translated by AI Atlas. All rights belong to the original author.