Skills

Overview

Skills are self-contained directories that provide agents with domain-specific instructions, guidelines, and reference material. Each skill is defined by a SKILL.md file with YAML frontmatter and a markdown body.

When activated, a skill’s instructions are injected directly into the agent’s task prompt — giving the agent expertise without requiring any code changes.

Note

Skills are NOT tools. This is the most common point of confusion.

• Skills inject instructions and context into the agent’s prompt. They tell the agent how to think about a problem.
• Tools give the agent callable functions to take action (search, read files, call APIs).

You often need both: skills for expertise, tools for action. They are configured independently and complement each other.

---

Quick Start

1. Create a Skill Directory

skills/
└── code-review/
    ├── SKILL.md          # Required — instructions
    ├── references/       # Optional — reference docs
    │   └── style-guide.md
    └── scripts/          # Optional — executable scripts

2. Write Your SKILL.md

---
name: code-review
description: Guidelines for conducting thorough code reviews with focus on security and performance.
metadata:
  author: your-team
  version: "1.0"
---

## Code Review Guidelines

When reviewing code, follow this checklist:

1. **Security**: Check for injection vulnerabilities, auth bypasses, and data exposure
2. **Performance**: Look for N+1 queries, unnecessary allocations, and blocking calls
3. **Readability**: Ensure clear naming, appropriate comments, and consistent style
4. **Testing**: Verify adequate test coverage for new functionality

### Severity Levels
- **Critical**: Security vulnerabilities, data loss risks → block merge
- **Major**: Performance issues, logic errors → request changes
- **Minor**: Style issues, naming suggestions → approve with comments

3. Attach to an Agent

from crewai import Agent
from crewai_tools import GithubSearchTool, FileReadTool

reviewer = Agent(
    role="Senior Code Reviewer",
    goal="Review pull requests for quality and security issues",
    backstory="Staff engineer with expertise in secure coding practices.",
    skills=["./skills"],                          # Injects review guidelines
    tools=[GithubSearchTool(), FileReadTool()],   # Lets agent read code
)

The agent now has both expertise (from the skill) and capabilities (from the tools).

---

Skills + Tools: Working Together

Here are common patterns showing how skills and tools complement each other:

Pattern 1: Skills Only (Domain Expertise, No Actions Needed)

Use when the agent needs specific instructions but doesn’t need to call external services:

agent = Agent(
    role="Technical Writer",
    goal="Write clear API documentation",
    backstory="Expert technical writer",
    skills=["./skills/api-docs-style"],  # Writing guidelines and templates
    # No tools needed — agent writes based on provided context
)

Pattern 2: Tools Only (Actions, No Special Expertise)

Use when the agent needs to take action but doesn’t need domain-specific instructions:

from crewai_tools import SerperDevTool, ScrapeWebsiteTool

agent = Agent(
    role="Web Researcher",
    goal="Find information about a topic",
    backstory="Skilled at finding information online",
    tools=[SerperDevTool(), ScrapeWebsiteTool()],  # Can search and scrape
    # No skills needed — general research doesn't need special guidelines
)

Pattern 3: Skills + Tools (Expertise AND Actions)

The most common real-world pattern. The skill provides how to approach the work; tools provide what the agent can do:

from crewai_tools import SerperDevTool, FileReadTool, CodeInterpreterTool

analyst = Agent(
    role="Security Analyst",
    goal="Audit infrastructure for vulnerabilities",
    backstory="Expert in cloud security and compliance",
    skills=["./skills/security-audit"],   # Audit methodology and checklists
    tools=[
        SerperDevTool(),                  # Research known vulnerabilities
        FileReadTool(),                   # Read config files
        CodeInterpreterTool(),            # Run analysis scripts
    ],
)

Pattern 4: Skills + MCPs

Skills work alongside MCP servers the same way they work with tools:

agent = Agent(
    role="Data Analyst",
    goal="Analyze customer data and generate reports",
    backstory="Expert data analyst with strong statistical background",
    skills=["./skills/data-analysis"],           # Analysis methodology
    mcps=["https://data-warehouse.example.com/sse"],  # Remote data access
)

Pattern 5: Skills + Apps

Skills can guide how an agent uses platform integrations:

agent = Agent(
    role="Customer Support Agent",
    goal="Respond to customer inquiries professionally",
    backstory="Experienced support representative",
    skills=["./skills/support-playbook"],  # Response templates and escalation rules
    apps=["gmail", "zendesk"],             # Can send emails and update tickets
)

---

Crew-Level Skills

Skills can be set on a crew to apply to all agents:

from crewai import Crew

crew = Crew(
    agents=[researcher, writer, reviewer],
    tasks=[research_task, write_task, review_task],
    skills=["./skills"],  # All agents get these skills
)

Agent-level skills take priority — if the same skill is discovered at both levels, the agent’s version is used.

---

SKILL.md Format

---
name: my-skill
description: Short description of what this skill does and when to use it.
license: Apache-2.0                    # optional
compatibility: crewai>=0.1.0          # optional
metadata:                              # optional
  author: your-name
  version: "1.0"
allowed-tools: web-search file-read   # optional, experimental
---

Instructions for the agent go here. This markdown body is injected
into the agent's prompt when the skill is activated.

Frontmatter Fields

Field	Required	Description
name	Yes	1–64 chars. Lowercase alphanumeric and hyphens. Must match directory name.
description	Yes	1–1024 chars. Describes what the skill does and when to use it.
license	No	License name or reference to a bundled license file.
compatibility	No	Max 500 chars. Environment requirements (products, packages, network).
metadata	No	Arbitrary string key-value mapping.
allowed-tools	No	Space-delimited list of pre-approved tools. Experimental.

---

Directory Structure

my-skill/
├── SKILL.md            # Required — frontmatter + instructions
├── scripts/            # Optional — executable scripts
├── references/         # Optional — reference documents
└── assets/             # Optional — static files (configs, data)

The directory name must match the name field in SKILL.md. The scripts/, references/, and assets/ directories are available on the skill’s path for agents that need to reference files directly.

---

Pre-loading Skills

For more control, you can discover and activate skills programmatically:

from pathlib import Path
from crewai.skills import discover_skills, activate_skill

# Discover all skills in a directory
skills = discover_skills(Path("./skills"))

# Activate them (loads full SKILL.md body)
activated = [activate_skill(s) for s in skills]

# Pass to an agent
agent = Agent(
    role="Researcher",
    goal="Find relevant information",
    backstory="An expert researcher.",
    skills=activated,
)

---

How Skills Are Loaded

Skills use progressive disclosure — only loading what’s needed at each stage:

Stage	What’s loaded	When
Discovery	Name, description, frontmatter fields	discover_skills()
Activation	Full SKILL.md body text	activate_skill()

During normal agent execution (passing directory paths via skills=["./skills"]), skills are automatically discovered and activated. The progressive loading only matters when using the programmatic API.

---

Skills vs Knowledge

Both skills and knowledge modify the agent’s prompt, but they serve different purposes:

Aspect	Skills	Knowledge
What it provides	Instructions, procedures, guidelines	Facts, data, information
How it’s stored	Markdown files (SKILL.md)	Embedded in vector store (ChromaDB)
How it’s retrieved	Entire body injected into prompt	Semantic search finds relevant chunks
Best for	Methodology, checklists, style guides	Company docs, product info, reference data
Set via	skills=["./skills"]	knowledge_sources=[source]

Rule of thumb: If the agent needs to follow a process, use a skill. If the agent needs to reference data, use knowledge.

---

Common Questions

Do I need to set skills AND tools?

It depends on your use case. Skills and tools are independent — you can use either, both, or neither.

• Skills alone: When the agent needs expertise but no external actions (e.g., writing with style guidelines)
• Tools alone: When the agent needs actions but no special methodology (e.g., simple web search)
• Both: When the agent needs expertise AND actions (e.g., security audit with specific checklists AND ability to scan code)

Do skills automatically provide tools?

No. The allowed-tools field in SKILL.md is experimental metadata only — it does not provision or inject any tools. You must always set tools separately via tools=[], mcps=[], or apps=[].

What happens if I set the same skill on both an agent and its crew?

The agent-level skill takes priority. Skills are deduplicated by name — the agent’s skills are processed first, so if the same skill name appears at both levels, the agent’s version is used.

How large can a SKILL.md body be?

There’s a soft warning at 50,000 characters, but no hard limit. Keep skills focused and concise for best results — large prompt injections can dilute the agent’s attention.