Agent Skills: Reusable AI Capability Modules

MCP connects AI to external tools. Skills solve a different problem: how do you package domain knowledge and SOPs so agents load them automatically?

Three Often-Confused Concepts

Concept	Essence	Analogy
System prompt	One-shot instruction text	Verbal briefing
MCP server	Standard tool/data interface	USB device
Skill	Structured knowledge + behavior	Training manual + SOP

Skills don’t replace MCP—they tell the agent when and how to use MCP and what best practices apply.

What a Cursor Skill Looks Like

A SKILL.md in the right directory. The agent reads it when relevant:

# Git Commit Skill

## When to Use
When the user asks to create a git commit.

## Steps
1. Run git diff to see changes
2. Classify change (feat/fix/refactor/docs)
3. Write message in Conventional Commits format
4. Do not commit unrelated files

## Format
feat(scope): short description

## Examples
feat(auth): add JWT token refresh endpoint
fix(ui): resolve button alignment on mobile

The agent follows this instead of you repeating rules every time.

Skill vs MCP vs Prompt

Need                          Use
─────────────────────────────────────────
"Let AI query the database"   → MCP server
"How/when/what to query"      → Skill
"This task's special ask"     → user prompt
"Baseline behavior always"    → system prompt

Typical stack:

System prompt (baseline)
  + Skills (domain SOPs, on demand)
  + MCP servers (tools and data)
  + user prompt (current task)

Cursor Skill Ecosystem

Official and community skills include:

• create-skill — how to author skills
• create-rule — Cursor rules
• sdk — Cursor SDK guide
• babysit — PR merge-ready workflow

Good skills share traits:

1. Clear when to use
2. Executable steps, not vague principles
3. Examples of input/output

Writing a Good Skill

✅ Good:
  - title states scope
  - "when to use" is specific ("creating git commit" not "writing code")
  - numbered steps
  - anti-patterns ("don't do this")
  - real examples

❌ Weak:
  - "You are an excellent programmer" (system prompt territory)
  - no trigger condition
  - "ensure quality" without how

Loading in Cursor

1. User request
2. Agent infers task type
3. Selects relevant skills (not all skills at once)
4. Executes with skill + MCP + codebase context

Like RAG for procedures—retrieve the right playbook, not the whole library.

Comparison Across Products

Product	Similar idea	Format
Cursor	Skill	SKILL.md
Claude	Projects + custom instructions	text + files
OpenAI	GPTs / custom actions	UI + OpenAPI
GitHub Copilot	Instructions	.github/copilot-instructions.md

Cursor’s structured triggers and steps suit engineering teams standardizing agent behavior.

Summary

Skill = reusable, on-demand domain SOP. MCP gives hands; Skills give the playbook. Together agents execute reliably.

Next: the layer that wires everything—Agent Harness.