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Skills Won't Sell; Bots Will: Why the AI Agent Business Is About Delivering Results, Not Reusable Prompt Packages

By 前端小小栈 ·
Read original on juejin.cn ↗ Google Translate ↗ Alt translation

The distinction between selling a Skill and selling a Bot maps directly to the difference between a feature and a service; Western teams that price their AI offerings as reusable prompt packages will struggle to find buyers, while those that wrap capabilities into outcome-delivering Bots can charge subscription and usage fees.

Summary

Skills are the hot new packaging for AI: a set of prompts, tool calls, workflows, and domain knowledge that an Agent loads on demand. But the economics work against a Skill marketplace. Users still have to invoke the AI, feed it context, configure tools, and judge the output, so a Skill feels like an Excel template, not a product. Its value is hard to price. Copying and regenerating Skills with AI is cheap. And there is no delivery loop: the user does the final job.

Bots fix that by owning the whole job. An SEO Bot takes a website URL and returns an optimization plan; a Research Bot takes a topic and returns a report. Users pay for the saved hours, not the underlying prompts. Bots support subscription pricing, per-task charges, or pay-per-result. They also build a moat: combining multiple Skills, MCP tools, knowledge bases, user memory, and execution history turns into a complete business system that cannot be copied by downloading a SKILL.md.

The argument lands on a strategic split: Skills are the capability layer, Bots are the business layer. The next business opportunity is not a Skill marketplace, but vertical Bots that solve real problems and keep the user entrance. For teams building agent products, the lesson is to sell outcomes, not components.

Takeaways
— A Skill encapsulates prompts, tool-calling logic, workflows, and domain knowledge for AI to call automatically.
— Skills are hard to price independently because their value depends on the underlying model, Agent capabilities, user input, and environment.
— The replacement cost of Skills is dropping: users can ask AI to generate a similar Skill instead of buying one.
— Skills lack a user delivery loop; the buyer still has to invoke AI, provide context, configure tools, handle exceptions, and check results.
— A Bot is an AI product that targets a specific user and scenario and independently completes tasks, delivering results rather than capabilities.
— Bots provide a clear reason to pay: users pay for saved time or completed deliverables, not for complex prompts or a count of Skills.
— Bots can generate recurring revenue via subscriptions, per-task pricing, or charges based on execution volume or delivered results.
— Bots build competitive barriers by combining multiple Skills, MCP tools, knowledge bases, business data, user memory, task scheduling, and execution feedback.
— Accumulated user data, business context, and execution history make a Bot hard to replicate; a competitor would need to copy a whole business system, not a SKILL.md.
— Skills are positioned as capability components or developer infrastructure; Bots are user-facing commercial products that charge continuously.
— A practical example: a cross-border e-commerce Skill is a tool to sell, while a cross-border purchasing Bot that searches suppliers, compares prices, and generates purchase lists is a service that can be charged by subscription, order, or transaction amount.
— Valuable Bots require reliable execution, clear delivery standards, cost control, exception handling, and for money/order/data scenarios, permission control and manual confirmation.
— The product form of software is shifting from user-operated tools to services that execute tasks on the user's behalf, making the user entry point more important than the underlying software.
Conclusions

The argument that Skills are hard to commercialize mirrors the old library-versus-service split in software: reusable code rarely generates revenue, while managed services do.

The moat the article attributes to Bots (accumulated user data, business context, execution history) is essentially the same moat that SaaS companies have always relied on, suggesting AI agents will not rewrite the economics of software business.

By saying users do not want to install Skills or configure Agents, the article makes a real usability claim: the target customer is an end user who cares about the report, not the infrastructure.

The recommendation to sell Bots over Skills implicitly concedes that AI commoditizes the low-level capability layer, so differentiation must move up to the service layer where relationships and data accumulate.

The article's example of a cross-border purchasing Bot with manual confirmation for transactions points to a hybrid automation model, which may be the realistic pattern for agent commercialization in high-stakes domains.

Concepts & terms
Skill (AI)
A reusable package of prompts, tool-calling logic, workflows, and domain knowledge that an AI Agent can load on demand to perform a specialized task, such as code review or SEO analysis.
Bot (AI product)
An AI product designed for a specific user and scenario that independently executes a complete task and delivers a finished result, such as an SEO Bot that generates an optimization plan from a website URL.
MCP (Model Context Protocol)
A protocol used to connect AI Agents with external tools and data sources, enabling the Agent to call external functions as part of its task execution.
Agent Runtime
The execution environment that handles an AI Agent's inference, planning, execution, and scheduling, coordinating how and when Skills, tools, and memory are used.
Memory (AI context)
A component that stores and recalls user context, business data, and history across sessions, allowing an Agent or Bot to maintain continuity and personalization.
From the discussion
Featured comments
汪汪队首席上单

How to make a BOT?

前端小小栈

Grok bot, or something similar.

See top comments, translated →
Source: juejin.cn ↗ Google Translate ↗ Backup ↗