AI Skill

What is AI Skill

A Skill is a relatively new concept in the AI Agent field: a capability flow, defined in a structured format like Markdown, that an agent can invoke. It builds on Function Calling but is more advanced than a single Tool: one Skill can orchestrate multiple tools, define execution steps, and include context instructions. The core logic is: an Agent uses the MCP protocol to invoke Skills, which in turn call Tools—and all of this rests on the Function Call capability.

In one sentence: a Tool is a single function; a Skill is a workflow; the Agent decides which Skill to use when.

Skill vs Tool vs MCP

These three are often confused but live at different layers: Function Calling is the foundation that lets an LLM call functions (unstructured → structured); a Tool is a single callable capability (e.g. "check the weather", "read a file"); a Skill is a multi-step flow defined in Markdown that can orchestrate multiple Tools; MCP is a standardized protocol that solves how tools are discovered and connected by an Agent. Their relationship: Agent → Skill → Tool → Function Call, with MCP running throughout to provide a standard interface.

# Layered relationship
Function Calling   # Foundation: the LLM's ability to call functions
    ↑
Tool               # Single capability: read_file(), search_web()
    ↑
Skill              # Workflow orchestration: Markdown defines "how to complete a task"
    ↑                  e.g. "publish a blog post" = write + add images + publish
Agent              # Decision maker: chooses and executes a Skill based on the goal

# MCP provides the standardized communication protocol between the Agent and Tools/Skills

Why Skill Matters

The value of a Skill lies in "turning expert experience into a reusable flow". A traditional Tool only exposes "what it can do"; a Skill also defines "how to do it best". For example, a "code review" Skill can include specific steps and checklists: look at architecture first, then security, then style. This makes the Agent not just call tools but work according to best practices. Skills are defined in Markdown, so flows can be adjusted without changing code—extremely flexible.

Skills evolve an Agent from "able to use tools" to "understands methodology"—the key to an Agent becoming professional.

Examples

Typical Skill applications: Claude Code's SKILL.md files define project-specific workflows; Trae IDE's Skills extension ecosystem provides 50+ platform-channel capabilities; the Hermes Agent framework uses Skills to achieve self-evolution. Developers can write custom Skills for their projects so the AI executes complex tasks according to team conventions, such as "run tests + generate a changelog + open a PR before release".

# A Skill definition example (Markdown format)
---
name: code-review
description: Review code against team conventions
---
## Steps
1. Read the changed files and understand the intent of the change
2. Check architecture: does it follow the layered design
3. Check security: any SQL injection or XSS risks
4. Check style: naming, comments, type annotations
5. Output a structured review report with severity labels

## Rules
- Never use the any type
- All public functions must have JSDoc

Drop a Skill file into the project and the Agent will discover it automatically and work according to this flow.