Skills Feature Overview

This article introduces what skills are, how skills help AI assistants handle complex tasks, and the differences between skills and tools

What Is a Skill?

A skill is like a professional certification for an AI assistant — each skill learned gives the AI assistant an additional professional capability. If tools are the "screwdrivers in the AI assistant's toolbox," then skills are "a complete set of Standard Operating Procedures (SOPs)" that not only tell the AI assistant what tools to use, but also when to use them, how to use them, and how to respond after using them.

Imagine you're running a customer service center:

Without skills:

• Customer: "I want to return an item"

• AI agent: "Sure, what's your order number?" (can only handle basic Q&A)

With skills:

• Customer: "I want to return an item"

• AI agent: "Sure!" ( automatically activates the return process skill) → Query order → Verify return eligibility → Generate return form → "Your return request has been created. The return number is RT-20260324, and processing is expected to complete within 3-5 business days."

Core Components of a Skill

Each skill consists of three parts:

Skill = Instructions + Attached Tools + Resource Files
           │              │                │
           ▼              ▼                ▼
      SOP Process     Callable         Reference
     (How to do it)  External          Attachments
                     Capabilities

• Instructions: Detailed instructions in Markdown format, like an SOP that guides the AI assistant through task completion step by step

• Attached Tools: Tools required by the skill (MCP tools, API tools, etc.) that enable the AI assistant to perform actual operations

• Resource Files: Attachment data that the skill can reference during execution (only available for skills created via upload)

How Skills Work

A complete skill workflow is as follows:

1. Create a skill:

   • Define the skill's name and description (the description determines when the AI triggers this skill)

   • Write detailed instructions (SOP in Markdown format)

   • Bind the required tools

2. Bind to an AI assistant:

   • Select the skills to use in the AI assistant's settings

   • An AI assistant can have multiple skills bound to it

3. User asks a question:

   • The user makes a request to the AI assistant in natural language

   • Example: "Check how many vacation days I have left"

4. AI assistant evaluates and activates the skill:

   • The AI assistant matches the user's question against the descriptions of bound skills

   • Determines which skill should handle the question

   • Automatically expands the complete instructions for that skill

5. Execute instructions and call tools:

   • The AI assistant follows the steps in the skill instructions one by one

   • Automatically calls the skill's attached tools when needed (querying databases, calling APIs, etc.)

6. Generate the final response:

   • The AI assistant consolidates the results returned by tools

   • Generates a structured response according to the reply format defined in the skill instructions

Key Advantages of Skills

Standardize Complex Tasks

• Without skills: The AI assistant can only answer based on general knowledge, resulting in inconsistent quality

• With skills: Every execution follows the SOP, ensuring consistent response quality

Precise Triggering and Task Division

• Through trigger conditions in descriptions, the AI assistant can automatically determine when to use which skill

• Different skills handle different tasks, achieving modular capability division

Reusable and Shareable

• A single skill can be bound to multiple AI assistants

• Skills can be exported as .skill files and shared with other organizations

Encapsulated Instructions and Tools

• Bundle "what to do" (instructions) and "what to use" (tools) together

• No need to repeatedly write extensive process details in the AI assistant's role instructions

Differences Between Skills and Tools

Comparison	Tools	Skills
Nature	A single external capability (API, MCP)	A complete execution workflow
Contents	API endpoint / MCP server	Instructions + Tools + Resources
Analogy	Screwdriver	Assembly manual + Screwdriver
Trigger method	AI decides on its own whether to call it	Matched based on trigger conditions in the description
Use cases	Single actions (check weather, send email)	Multi-step workflows (return processing, vacation calculation)

Skills and tools are complementary: skills invoke tool capabilities through "attached tools," while tools provide the actual execution capabilities that skills need.

Practical Application Scenarios

Enterprise Customer Service

Skill: Return Processing Workflow
Trigger: When a customer requests a return or exchange
Steps: Query order → Verify return eligibility → Create return form → Reply with processing result
Attached tools: Order Query API, Return System API

Government Agencies

Skill: Vacation Day Calculation
Trigger: When a user asks about vacation days or annual leave days
Steps: Confirm personnel category → Search regulatory examples → Compare and calculate → Produce result
Attached tools: Knowledge base search

E-commerce Platforms

Skill: Customer Data Collection
Trigger: When a customer expresses purchase intent or requests a quote
Steps: Guide them to provide name/company/contact information → Write to CRM
Attached tools: Google Sheet API, CRM tool

Marketing Teams

Skill: Email Sending Service
Trigger: When a user requests to send an email or contact a customer
Steps: Confirm recipient → Compose email content → Send
Attached tools: Gmail MCP tool