This article focuses on two questions:

• How should SKILL.md be written and structured?
• How do we create high-quality, maintainable, reusable Skills?

1. SKILL.md Specification

SKILL.md is the core description file of a skill. It usually contains two parts:

• YAML front matter: defines skill metadata.
• Markdown body: defines execution guidance and practical workflow.

1.1 Front Matter Example

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---
# === Required fields ===
name: skill-name
# Unique skill identifier; kebab-case is recommended

description: >
  Brief but precise description of:
  1) What this skill does
  2) When it should be used
  3) What its core value is
# Note: description is typically the key basis for skill selection

# === Optional fields ===
version: 1.0.0
allowed_tools: [tool1, tool2]
required_context: [context_item1]
license: MIT
author: Your Name <email@example.com>
tags: [database, analysis, sql]
---

1.2 Recommended Body Structure

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# Skill Title

## Overview
(Skill summary, use cases, technical background)

## Prerequisites
(Runtime environment, dependencies, required permissions)

## Workflow
(Step-by-step guidance: input, process, output)

## Best Practices
(Experience-based tips, caveats, common pitfalls)

## Examples
(Typical cases for faster adoption)

## Troubleshooting
(Common issues and fixes)

2. Principles for Writing High-Quality Skills

Based on official documentation and community practice, the following four principles are recommended.

2.1 Keep Description Precise

description is the key entry point for skill matching. It should:

• Clearly define scope; avoid vague wording like “helps with data processing.”
• Include trigger keywords to match user intent.
• Explain unique value and boundaries from other skills.

Weak example:

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description: Handle database queries

Stronger example:

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description: >
  Convert Chinese business questions into SQL queries and analyze the MySQL employees sample database.
  Suitable for employee info lookup, salary statistics, department analysis, and role-change history.
  Use this skill when users ask about employee, salary, or department data.

2.2 Modular Design and Single Responsibility

A skill should focus on one clearly defined task domain. If a single skill tries to cover too much, it often leads to:

• Broader descriptions and lower matching precision.
• Longer instructions and heavier context load.
• Higher maintenance and iteration costs.

Instead of one “general analysis” skill, split into specialized skills, for example:

• mysql-employees-analysis
• sales-data-analysis
• user-behavior-analysis

2.3 Determinism First

For complex tasks requiring precision, prefer scripted execution over pure LLM generation.

For example, in data export scenarios, instead of generating Excel binary content directly with an LLM, use a dedicated script. SKILL.md should only define when and how to invoke it.

2.4 Progressive Disclosure

Layer information by importance and frequency to reduce unnecessary context usage:

• SKILL.md body: core workflow and common patterns
• Supplementary docs (for example, advanced.md): advanced usage and edge cases
• Data files: large reference data accessed on demand via scripts

Summary

The goal of high-quality Skills is not writing more, but writing clearer boundaries, better triggers, stable execution, and sustainable maintenance.

Start with a standardized SKILL.md, then combine single responsibility with progressive disclosure to build a more efficient skill system.