Skills are the building blocks that make Claude more capable and useful for your specific needs. Whether you're automating workflows, integrating enterprise systems, or creating specialized assistants, understanding how to build and deploy skills effectively is essential. This comprehensive guide walks you through everything you need to know.
What Are Claude Skills?
Skills are modular capabilities that extend Claude's functionality beyond its base language understanding. Think of them as tools Claude can use to perform specific actions: querying databases, calling APIs, processing files, generating images, or controlling external systems.
Unlike traditional programming where you write explicit logic for every scenario, skills define what Claude can do and how to do it, while Claude figures out when to use each skill based on user intent.
Anatomy of a Skill
Every skill consists of four key components:
1. Metadata
Defines the skill's identity and purpose:
json
{
"name": "database_query",
"version": "1.0.0",
"description": "Query the customer database using natural language",
"category": "data",
"tags": ["sql", "database", "customers"]
}
2. Parameters
Specifies what inputs the skill needs:
json
{
"parameters": {
"query": {
"type": "string",
"description": "Natural language query to convert to SQL",
"required": true
},
"limit": {
"type": "integer",
"description": "Maximum number of results",
"default": 100
}
}
}
3. Implementation
The code that executes when the skill is invoked:
python
import anthropicclass DatabaseQuerySkill:
def __init__(self, db_connection):
self.db = db_connection
self.client = anthropic.Anthropic()
def execute(self, query: str, limit: int = 100):
# Use Claude to convert natural language to SQL
sql = self.generate_sql(query)
# Execute the query
results = self.db.execute(sql, limit=limit)
# Format results
return self.format_results(results)
def generate_sql(self, natural_query: str):
response = self.client.messages.create(
model="claude-opus-4",
messages=[{
"role": "user",
"content": f"Convert to SQL: {natural_query}"
}]
)
return response.content[0].text
4. Documentation
Examples and usage guidance that help Claude understand when and how to use the skill:
markdown
Examples
Query: "Show me customers who signed up last month"
Result: Returns list of customers with signup_date in previous month
Query: "Top 10 customers by revenue"
Result: Returns customers ordered by total_revenue DESC, limited to 10
Best Practices
- Always specify time ranges for date queries
- Use limit parameter for large result sets
- Customer IDs should be exact matches
Skill Development Lifecycle
Phase 1: Design
Start by clearly defining:
- Purpose: What problem does this skill solve?
- Scope: What should it do? (Be specific but not too narrow)
- Integration: What external systems or APIs does it need?
- Security: What data does it access? What permissions are required?
- Error handling: What can go wrong and how should it be handled?
Phase 2: Implementation
Build the skill using our SDK:
python
from claude_skills import Skill, Parameterclass WeatherSkill(Skill):
name = "get_weather"
description = "Get current weather for any location"
parameters = [
Parameter("location", str, "City name or coordinates", required=True),
Parameter("units", str, "Temperature units (celsius/fahrenheit)", default="celsius")
]
async def execute(self, location: str, units: str = "celsius"):
# Implementation here
api_url = f"https://api.weather.com/v1/current"
params = {"location": location, "units": units}
async with self.http_client.get(api_url, params=params) as response:
data = await response.json()
return {
"temperature": data["temp"],
"conditions": data["conditions"],
"humidity": data["humidity"],
"wind_speed": data["wind_speed"]
}
Phase 3: Testing
Thoroughly test your skill:
Unit tests verify individual components:
python
def test_weather_skill():
skill = WeatherSkill()
result = skill.execute("San Francisco", "fahrenheit")
assert "temperature" in result
assert isinstance(result["temperature"], float)
assert result["temperature"] > -100 # Sanity check
Integration tests verify end-to-end functionality:
python
def test_claude_uses_weather_skill():
claude = ClaudeWithSkills(skills=[WeatherSkill()])
response = claude.message("What's the weather in Tokyo?")
assert "temperature" in response.lower()
assert "tokyo" in response.lower()
Human evaluation tests real-world usage:
- Can Claude correctly identify when to use this skill?
- Does it extract parameters accurately from natural language?
- Are the results formatted in a useful way?
- Does it handle edge cases gracefully?
Phase 4: Deployment
Deploy skills to your Claude environment:
bash
Deploy to development
claude-cli deploy-skill weather_skill.py --env devTest in development
claude-cli test-skill weather_skill --env devPromote to production
claude-cli promote-skill weather_skill --from dev --to production
Phase 5: Monitoring
Track skill performance:
- Usage metrics: How often is the skill invoked?
- Success rate: What percentage of invocations complete successfully?
- Latency: How long does execution take?
- Error patterns: What failures occur most frequently?
- User feedback: Are users satisfied with results?
Advanced Patterns
Skill Chaining
Combine multiple skills for complex workflows:
python
class ReportGeneratorSkill(Skill):
def execute(self, report_type: str):
# Use database skill to fetch data
data = self.invoke_skill("database_query",
query=f"Get {report_type} data")
# Use analysis skill to process
analysis = self.invoke_skill("data_analysis",
data=data)
# Use visualization skill to create charts
charts = self.invoke_skill("create_charts",
data=analysis)
return {"data": data, "analysis": analysis, "charts": charts}
Conditional Skills
Skills that adapt based on context:
python
class SmartSearchSkill(Skill):
def execute(self, query: str):
# Determine search type based on query content
if self.is_code_query(query):
return self.search_code_repos(query)
elif self.is_document_query(query):
return self.search_documents(query)
else:
return self.search_web(query)
Stateful Skills
Skills that maintain context across invocations:
python
class ConversationDatabaseSkill(Skill):
def __init__(self):
self.session = {}
def execute(self, action: str, **kwargs):
if action == "filter":
self.session["filters"] = kwargs
return "Filters applied"
elif action == "sort":
self.session["sort_by"] = kwargs["field"]
return f"Sorted by {kwargs["field"]}"
elif action == "fetch":
return self.fetch_with_session_context()
Security Best Practices
- Principle of Least Privilege: Skills should request only the minimum permissions needed
- Input Validation: Always validate and sanitize user inputs
- Rate Limiting: Implement throttling for expensive operations
- Audit Logging: Log all skill invocations and their parameters
- Secrets Management: Never hardcode credentials; use secure secret stores
- Output Sanitization: Ensure skill outputs don't leak sensitive information
Common Pitfalls
Overly Specific Skills: Skills that do one narrow thing are hard to reuse. Aim for the right balance between specificity and flexibility.
Poor Error Messages: When skills fail, Claude needs clear information to explain what went wrong. Return descriptive error messages.
Missing Documentation: Without good examples, Claude may not understand when to use your skill. Invest time in comprehensive documentation.
Blocking Operations: Skills that take >30 seconds create poor user experiences. Use async operations and provide progress updates.
Lack of Versioning: As skills evolve, you need ways to maintain backward compatibility. Always version your skills.
Real-World Examples
E-commerce Company: Built 15 skills covering inventory management, order processing, customer support, and analytics. Claude now handles 60% of routine queries automatically.
Healthcare System: Developed HIPAA-compliant skills for patient record lookup, appointment scheduling, and lab result retrieval. Reduced administrative burden on clinical staff by 40%.
Financial Institution: Created skills for portfolio analysis, risk assessment, and regulatory reporting. Analysts can now perform complex queries in natural language instead of writing SQL.
Getting Started
Ready to build your first skill?
- Install the SDK:
pip install claude-skills-sdk - Review the docs: comprehensive.anthropic.com/skills
- Start with a template:
claude-cli create-skill --template basic - Join the community: forum.anthropic.com/skills
Source: https://claude.com/blog/a-complete-guide-to-building-skills-for-claude