In October 2024, Anthropic introduced Agent Skills, fundamentally changing how we extend Claude’s capabilities. Rather than relying solely on prompt engineering or building complex tool integrations, Skills provide a filesystem-based, modular approach to packaging domain expertise. If you’re building AI-powered workflows for enterprise applications, Skills represent the next evolution in making AI systems reliable, repeatable, and production-ready.
This comprehensive guide takes you from understanding the architectural principles behind Skills through building production implementations in Node.js, Python, and C#. We’ll explore real-world use cases, examine the progressive disclosure model that makes Skills efficient, and provide complete code examples for creating custom Skills that solve actual enterprise problems.
What Are Agent Skills? Understanding the Architecture
Agent Skills are organized folders containing instructions, scripts, and resources that Claude loads dynamically when relevant to a task. Unlike traditional function calling or hardcoded capabilities, Skills operate through a sophisticated context injection system where Claude decides when to load specific expertise based on task requirements.
The fundamental innovation lies in how Skills manage context. Traditional approaches to extending LLM capabilities require loading all documentation, examples, and instructions upfront. Skills instead use progressive disclosure, a three-tier loading system that makes them remarkably efficient.
flowchart TD
A[User Request] --> B{Skill Discovery Phase}
B --> C[Load Metadata Only]
C --> D{Skill Relevant?}
D -->|No| E[Standard Response Path]
D -->|Yes| F[Load Full Instructions]
F --> G{Code Execution Needed?}
G -->|No| H[Generate Response]
G -->|Yes| I[Execute Scripts in Secure Container]
I --> J[Return Results with File IDs]
H --> K[Final Response]
J --> K
style C fill:#e1f5ff
style F fill:#ffe1e1
style I fill:#e1ffe1
Here’s how the three-tier system works in practice. When Claude starts a session, it loads only the metadata from each available Skill. This metadata consists of a name and description from the YAML frontmatter, typically consuming just 50-100 tokens per Skill. When you make a request, Claude evaluates which Skills might be relevant based solely on these descriptions.
If Claude determines a Skill is relevant, it loads the full instructions from the SKILL.md file, which might be 2,000-5,000 tokens. Finally, if the Skill includes additional resources like example files, API documentation, or reference materials, Claude only reads those specific files when the task requires them. This means you can bundle comprehensive documentation without paying a context penalty unless it’s actually needed.
Skills vs. MCP: Choosing the Right Tool
One question that comes up frequently is how Skills relate to the Model Context Protocol. Both extend Claude’s capabilities, but they serve different purposes and excel in different scenarios.
Skills are best for packaging reusable expertise and workflows. Think of Skills as teaching Claude how to do something consistently. They excel at document creation following specific standards, data analysis using particular methodologies, code generation following organizational patterns, and any task requiring deterministic, repeatable execution.
MCP shines when you need to integrate external systems and data sources. Use MCP for connecting to APIs and databases, reading from external services in real-time, integrating third-party tools, or accessing live data that changes frequently.
The power comes from combining both. You might use MCP to pull data from your CRM, then use a Skill to format that data into a standardized report following your company’s brand guidelines. Skills and MCP are complementary technologies, not competing ones.
Anatomy of a Skill: File Structure and Components
Every Skill starts with a directory containing at minimum a SKILL.md file. This markdown file must begin with YAML frontmatter containing required metadata. The simplest valid Skill looks like this.
---
name: example-skill
description: Brief description of what this skill does and when to use it
---
# Example Skill
Detailed instructions for Claude to follow when this skill is active.
## Usage Guidelines
- Guideline 1
- Guideline 2
## Examples
Example 1: Show a concrete example of the skill in action
Example 2: Demonstrate edge cases or advanced usage
The name field must be lowercase with hyphens, no spaces, and under 64 characters. It cannot use reserved words like “anthropic” or “claude”. The description should be concise, under 1024 characters, and clearly explain both what the Skill does and when it should be used. This description is critical because it’s what Claude sees during the discovery phase.
Beyond the basic SKILL.md file, you can include additional resources. Python scripts for deterministic operations, reference documentation that Claude might need, example files or templates, and configuration files or schemas. These files are only loaded when Claude determines they’re needed for the specific task.
Building Your First Skill: Step by Step
Let’s build a practical Skill that solves a real enterprise problem. We’ll create a Skill for generating API documentation from OpenAPI specifications, with implementations in Node.js, Python, and C#.
The Basic Skill Structure
First, create the directory structure and SKILL.md file.
---
name: api-docs-generator
description: Generate comprehensive API documentation from OpenAPI/Swagger specifications. Use when the user requests API documentation, needs to document REST endpoints, or mentions OpenAPI or Swagger specs.
---
# API Documentation Generator
This skill generates professional API documentation from OpenAPI specifications, creating markdown documentation with code examples in multiple languages.
## Capabilities
- Parse OpenAPI 3.0 and Swagger 2.0 specifications
- Generate endpoint documentation with request/response examples
- Create authentication guides
- Include error handling documentation
- Generate client code examples in Node.js, Python, and C#
## Usage
Provide the OpenAPI specification as JSON or YAML. The skill will analyze the spec and generate comprehensive documentation including:
1. Overview and authentication methods
2. Detailed endpoint documentation
3. Request/response schemas with examples
4. Error code reference
5. Client implementation examples
## Output Format
The skill generates structured markdown suitable for static site generators, with proper heading hierarchy and code blocks properly formatted for syntax highlighting.
Node.js Implementation
Create a script that handles the OpenAPI parsing and documentation generation. Save this as generate_docs.js in the skill directory.
const fs = require('fs');
const yaml = require('js-yaml');
class OpenAPIDocGenerator {
constructor(specPath) {
this.spec = this.loadSpec(specPath);
}
loadSpec(specPath) {
const content = fs.readFileSync(specPath, 'utf8');
if (specPath.endsWith('.yaml') || specPath.endsWith('.yml')) {
return yaml.load(content);
}
return JSON.parse(content);
}
generateDocs() {
const docs = [];
docs.push(`# ${this.spec.info.title}\n`);
docs.push(`${this.spec.info.description}\n`);
docs.push(`**Version:** ${this.spec.info.version}\n`);
if (this.spec.servers) {
docs.push(`\n## Base URLs\n`);
this.spec.servers.forEach(server => {
docs.push(`- ${server.url} - ${server.description || ''}\n`);
});
}
docs.push(`\n## Authentication\n`);
this.generateAuthDocs(docs);
docs.push(`\n## Endpoints\n`);
this.generateEndpointDocs(docs);
return docs.join('');
}
generateAuthDocs(docs) {
if (!this.spec.components?.securitySchemes) {
docs.push('No authentication required.\n');
return;
}
Object.entries(this.spec.components.securitySchemes).forEach(([name, scheme]) => {
docs.push(`\n### ${name}\n`);
docs.push(`**Type:** ${scheme.type}\n`);
if (scheme.type === 'http') {
docs.push(`**Scheme:** ${scheme.scheme}\n`);
} else if (scheme.type === 'apiKey') {
docs.push(`**Location:** ${scheme.in}\n`);
docs.push(`**Parameter:** ${scheme.name}\n`);
}
});
}
generateEndpointDocs(docs) {
Object.entries(this.spec.paths).forEach(([path, methods]) => {
Object.entries(methods).forEach(([method, operation]) => {
if (method === 'parameters') return;
docs.push(`\n### ${method.toUpperCase()} ${path}\n`);
docs.push(`${operation.summary || operation.description || ''}\n`);
if (operation.parameters) {
docs.push(`\n**Parameters:**\n`);
operation.parameters.forEach(param => {
const required = param.required ? ' (required)' : '';
docs.push(`- \`${param.name}\` (${param.in})${required}: ${param.description || ''}\n`);
});
}
if (operation.requestBody) {
docs.push(`\n**Request Body:**\n`);
this.generateSchemaExample(docs, operation.requestBody);
}
if (operation.responses) {
docs.push(`\n**Responses:**\n`);
Object.entries(operation.responses).forEach(([code, response]) => {
docs.push(`\n**${code}** - ${response.description}\n`);
});
}
docs.push(this.generateCodeExample(method, path, operation));
});
});
}
generateCodeExample(method, path, operation) {
const example = [];
example.push(`\n**Example (Node.js):**\n`);
example.push('```javascript\n');
example.push(`const response = await fetch('${this.spec.servers[0]?.url || ''}${path}', {\n`);
example.push(` method: '${method.toUpperCase()}',\n`);
example.push(` headers: {\n`);
example.push(` 'Content-Type': 'application/json',\n`);
example.push(` }\n`);
example.push(`});\n`);
example.push(`const data = await response.json();\n`);
example.push('```\n');
return example.join('');
}
generateSchemaExample(docs, requestBody) {
const content = requestBody.content?.['application/json'];
if (content?.schema) {
docs.push('```json\n');
docs.push(JSON.stringify(this.generateExampleFromSchema(content.schema), null, 2));
docs.push('\n```\n');
}
}
generateExampleFromSchema(schema) {
if (schema.example) return schema.example;
if (schema.type === 'object' && schema.properties) {
const example = {};
Object.entries(schema.properties).forEach(([key, prop]) => {
example[key] = this.generateExampleFromSchema(prop);
});
return example;
}
if (schema.type === 'array' && schema.items) {
return [this.generateExampleFromSchema(schema.items)];
}
return schema.type === 'string' ? 'string' : schema.type === 'number' ? 0 : null;
}
}
if (require.main === module) {
const specPath = process.argv[2];
if (!specPath) {
console.error('Usage: node generate_docs.js ');
process.exit(1);
}
const generator = new OpenAPIDocGenerator(specPath);
const docs = generator.generateDocs();
fs.writeFileSync('api_documentation.md', docs);
console.log('Documentation generated: api_documentation.md');
}
module.exports = OpenAPIDocGenerator;
Python Implementation
The Python version provides similar functionality with pythonic patterns. Save this as generate_docs.py.
import json
import yaml
from pathlib import Path
from typing import Dict, Any, List
class OpenAPIDocGenerator:
def __init__(self, spec_path: str):
self.spec = self._load_spec(spec_path)
def _load_spec(self, spec_path: str) -> Dict[str, Any]:
path = Path(spec_path)
content = path.read_text()
if path.suffix in ['.yaml', '.yml']:
return yaml.safe_load(content)
return json.loads(content)
def generate_docs(self) -> str:
docs = []
docs.append(f"# {self.spec['info']['title']}\n\n")
docs.append(f"{self.spec['info'].get('description', '')}\n\n")
docs.append(f"**Version:** {self.spec['info']['version']}\n\n")
if 'servers' in self.spec:
docs.append("## Base URLs\n\n")
for server in self.spec['servers']:
desc = server.get('description', '')
docs.append(f"- {server['url']} - {desc}\n")
docs.append("\n")
docs.append("## Authentication\n\n")
self._generate_auth_docs(docs)
docs.append("## Endpoints\n\n")
self._generate_endpoint_docs(docs)
return ''.join(docs)
def _generate_auth_docs(self, docs: List[str]) -> None:
security_schemes = self.spec.get('components', {}).get('securitySchemes')
if not security_schemes:
docs.append("No authentication required.\n\n")
return
for name, scheme in security_schemes.items():
docs.append(f"### {name}\n\n")
docs.append(f"**Type:** {scheme['type']}\n")
if scheme['type'] == 'http':
docs.append(f"**Scheme:** {scheme.get('scheme', 'N/A')}\n")
elif scheme['type'] == 'apiKey':
docs.append(f"**Location:** {scheme.get('in', 'N/A')}\n")
docs.append(f"**Parameter:** {scheme.get('name', 'N/A')}\n")
docs.append("\n")
def _generate_endpoint_docs(self, docs: List[str]) -> None:
for path, methods in self.spec.get('paths', {}).items():
for method, operation in methods.items():
if method == 'parameters':
continue
docs.append(f"### {method.upper()} {path}\n\n")
docs.append(f"{operation.get('summary', operation.get('description', ''))}\n\n")
if 'parameters' in operation:
docs.append("**Parameters:**\n\n")
for param in operation['parameters']:
required = ' (required)' if param.get('required') else ''
desc = param.get('description', '')
docs.append(f"- `{param['name']}` ({param['in']}){required}: {desc}\n")
docs.append("\n")
if 'requestBody' in operation:
docs.append("**Request Body:**\n\n")
self._generate_schema_example(docs, operation['requestBody'])
if 'responses' in operation:
docs.append("**Responses:**\n\n")
for code, response in operation['responses'].items():
docs.append(f"**{code}** - {response.get('description', '')}\n\n")
docs.append(self._generate_code_example(method, path, operation))
docs.append("\n")
def _generate_code_example(self, method: str, path: str, operation: Dict) -> str:
base_url = self.spec.get('servers', [{}])[0].get('url', '')
example = []
example.append("**Example (Python):**\n\n")
example.append("```python\n")
example.append("import requests\n\n")
example.append(f"response = requests.{method}(\n")
example.append(f" '{base_url}{path}',\n")
example.append(" headers={'Content-Type': 'application/json'}\n")
example.append(")\n")
example.append("data = response.json()\n")
example.append("```\n\n")
return ''.join(example)
def _generate_schema_example(self, docs: List[str], request_body: Dict) -> None:
content = request_body.get('content', {}).get('application/json')
if content and 'schema' in content:
docs.append("```json\n")
example = self._generate_example_from_schema(content['schema'])
docs.append(json.dumps(example, indent=2))
docs.append("\n```\n\n")
def _generate_example_from_schema(self, schema: Dict) -> Any:
if 'example' in schema:
return schema['example']
if schema.get('type') == 'object' and 'properties' in schema:
example = {}
for key, prop in schema['properties'].items():
example[key] = self._generate_example_from_schema(prop)
return example
if schema.get('type') == 'array' and 'items' in schema:
return [self._generate_example_from_schema(schema['items'])]
type_defaults = {
'string': 'string',
'number': 0,
'integer': 0,
'boolean': False
}
return type_defaults.get(schema.get('type'), None)
if __name__ == '__main__':
import sys
if len(sys.argv) < 2:
print('Usage: python generate_docs.py ')
sys.exit(1)
generator = OpenAPIDocGenerator(sys.argv[1])
docs = generator.generate_docs()
Path('api_documentation.md').write_text(docs)
print('Documentation generated: api_documentation.md')
C# Implementation
For .NET environments, here’s a C# implementation that integrates well with Azure and enterprise .NET stacks.
using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;
using System.Text.Json;
using YamlDotNet.Serialization;
public class OpenAPIDocGenerator
{
private JsonDocument _spec;
private JsonElement _root;
public OpenAPIDocGenerator(string specPath)
{
LoadSpec(specPath);
}
private void LoadSpec(string specPath)
{
var content = File.ReadAllText(specPath);
if (specPath.EndsWith(".yaml") || specPath.EndsWith(".yml"))
{
var deserializer = new DeserializerBuilder().Build();
var yamlObject = deserializer.DeserializeIntegrating Skills with the Claude API
Once you’ve built a Skill, you need to make it available through the Claude API. This involves uploading your Skill, managing versions, and incorporating it into your API requests.
Uploading a Custom Skill
Skills are uploaded through the /v1/skills endpoint. You can upload either a directory path or individual files. Here’s how to upload our API documentation generator Skill.
import anthropic
from pathlib import Path
client = anthropic.Anthropic()
# Upload the skill directory
skill = client.beta.skills.create(
name="api-docs-generator",
description="Generate comprehensive API documentation from OpenAPI specifications",
directory=Path("./api-docs-generator"),
betas=["skills-2025-10-02"]
)
print(f"Skill created with ID: {skill.id}")
print(f"Version: {skill.version}")
The skill name must be unique within your organization, lowercase with hyphens, and under 64 characters. The description should clearly explain what the Skill does and when it should be used, as this is what Claude evaluates during skill discovery.
Using Skills in API Requests
To use a Skill in your API requests, specify it in the container parameter. You must also enable the code execution tool, as Skills run in the code execution environment.
import anthropic
client = anthropic.Anthropic()
response = client.beta.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=4096,
betas=["code-execution-2025-08-25", "skills-2025-10-02"],
container={
"skills": [
{
"type": "custom",
"skill_id": "api-docs-generator",
"version": "1.0.0"
}
]
},
messages=[{
"role": "user",
"content": "Generate documentation for this OpenAPI spec: " + spec_content
}],
tools=[{
"type": "code_execution_20250825",
"name": "code_execution"
}]
)
# Extract the generated documentation
for content in response.content:
if content.type == 'text':
print(content.text)
You can include up to 8 Skills per request. Claude automatically determines which Skills to use based on the task. For best performance with prompt caching, keep your Skills list consistent across requests.
Version Management
Skills support versioning, which is critical for production deployments. You can create new versions, pin to specific versions, or use the latest version.
// Create a new version
const newVersion = await client.beta.skills.createVersion({
skillId: "api-docs-generator",
directory: "./api-docs-generator-v2",
betas: ["skills-2025-10-02"]
});
// List all versions
const versions = await client.beta.skills.listVersions({
skillId: "api-docs-generator",
betas: ["skills-2025-10-02"]
});
// Pin to specific version in production
const response = await client.beta.messages.create({
model: "claude-sonnet-4-5-20250929",
betas: ["code-execution-2025-08-25", "skills-2025-10-02"],
container: {
skills: [{
type: "custom",
skill_id: "api-docs-generator",
version: "1.0.0" // Pin to specific version
}]
},
// ... rest of request
});
In production, always pin to specific versions to ensure consistent behavior. Use “latest” only in development and testing environments.
Advanced Patterns: Composing Multiple Skills
One of the most powerful features of Skills is their composability. You can combine multiple Skills in a single request, and Claude will intelligently coordinate their use.
For example, you might combine a data extraction Skill with a reporting Skill. The extraction Skill handles pulling data from various sources, while the reporting Skill formats that data according to your company’s standards.
response = client.beta.messages.create(
model="claude-sonnet-4-5-20250929",
max_tokens=8192,
betas=["code-execution-2025-08-25", "skills-2025-10-02"],
container={
"skills": [
{"type": "custom", "skill_id": "data-extractor", "version": "2.1.0"},
{"type": "custom", "skill_id": "company-report-generator", "version": "1.5.0"},
{"type": "anthropic", "skill_id": "xlsx", "version": "latest"}
]
},
messages=[{
"role": "user",
"content": "Extract sales data from the attached CSV files and generate our standard quarterly report in Excel format"
}],
tools=[{"type": "code_execution_20250825", "name": "code_execution"}]
)
In this request, Claude will use the data-extractor Skill to parse the CSV files, the company-report-generator Skill to format the data according to your organizational standards, and the built-in xlsx Skill to create the final Excel file. The Skills work together seamlessly without requiring explicit orchestration code.
Production Considerations and Best Practices
Moving Skills from development to production requires attention to several key areas.
Security and Trust
Skills can execute arbitrary code in Claude’s environment, which makes security critical. Only use Skills from trusted sources. If you must use a Skill from an external source, audit all files thoroughly before deployment.
Look for unusual network calls, unexpected file access patterns, operations that don’t match the Skill’s stated purpose, and obfuscated or encoded code sections. Malicious Skills could lead to data exfiltration, unauthorized system access, or other security risks.
Error Handling
Implement robust error handling in your Skills. Include validation for input parameters, graceful degradation when resources are unavailable, clear error messages that help debugging, and logging for monitoring production usage.
class RobustSkillExecutor:
def __init__(self, skill_config):
self.config = skill_config
self.logger = self.setup_logging()
def execute_with_retry(self, operation, max_retries=3):
for attempt in range(max_retries):
try:
result = operation()
self.logger.info(f"Operation succeeded on attempt {attempt + 1}")
return result
except Exception as e:
self.logger.warning(f"Attempt {attempt + 1} failed: {str(e)}")
if attempt == max_retries - 1:
self.logger.error(f"All retry attempts exhausted")
raise
time.sleep(2 ** attempt) # Exponential backoff
def validate_inputs(self, inputs, schema):
# Implement input validation based on schema
errors = []
for field, requirements in schema.items():
if requirements.get('required') and field not in inputs:
errors.append(f"Missing required field: {field}")
if errors:
raise ValueError(f"Input validation failed: {', '.join(errors)}")
Performance Optimization
For the best performance, keep your Skill metadata concise. The name and description are loaded for every session, so they should be clear but brief. Place expensive operations in separate files that are only loaded when needed. Take advantage of prompt caching by keeping your Skills list consistent across requests.
When a Skill generates files, use the Files API to download them efficiently. The response includes file IDs that you can use to retrieve the actual content.
// Extract file IDs from the response
const fileIds = [];
for (const item of response.content) {
if (item.type === 'bash_code_execution_tool_result') {
const result = item.content;
if (result.type === 'bash_code_execution_result') {
for (const file of result.content) {
if (file.file_id) {
fileIds.push(file.file_id);
}
}
}
}
}
// Download each file
for (const fileId of fileIds) {
const fileMetadata = await client.beta.files.retrieve_metadata(fileId, {
betas: ["files-api-2025-04-14"]
});
const fileContent = await client.beta.files.retrieve_content(fileId, {
betas: ["files-api-2025-04-14"]
});
// Save or process the file
await fs.writeFile(fileMetadata.filename, fileContent);
}
Real-World Use Cases
Skills shine in scenarios requiring consistency, repeatability, and integration with existing workflows. Here are some production use cases from enterprises using Skills.
A financial services company created a compliance reporting Skill that ensures all regulatory reports follow exact formatting requirements and include all mandatory disclosures. The Skill incorporates their specific regulatory framework, validation rules, and approved language templates. What previously required manual review and multiple revisions now generates consistently compliant reports on the first try.
A SaaS company built a Skill for generating customer-facing documentation from their internal API specifications. The Skill automatically creates tutorials with code examples in multiple languages, formats them according to their documentation style guide, and includes appropriate warnings and best practices. This reduced their documentation time from days to hours while improving consistency across their entire API reference.
An enterprise development team created a code review Skill that enforces their specific coding standards, security guidelines, and architectural patterns. The Skill analyzes pull requests, identifies violations of their internal standards, and suggests corrections that align with their established patterns. This has standardized their code quality across distributed teams.
Skills vs. Projects vs. Prompts: Decision Framework
Deciding when to use Skills versus other approaches can be challenging. Here’s a practical decision framework.
Use Skills when the task is repeatable and should be consistent across users, requires deterministic code execution, needs to be composed with other capabilities, or benefits from organization-wide deployment. Skills are ideal for standardizing workflows, enforcing quality standards, and automating complex multi-step processes.
Use Projects when work is ongoing and collaborative, context builds up over multiple sessions, you need a shared workspace for a specific initiative, or the focus is exploration rather than execution. Projects excel at research, planning, and iterative development.
Use simple prompts when the instruction is brief and one-off, you don’t need reusable capabilities, there’s no code execution or file operations required, or the task is exploratory without established patterns.
The Future of Skills: Open Standard and Portability
In December 2024, Anthropic announced that Agent Skills is now an open standard. This means Skills you create for Claude can potentially work with other AI platforms that adopt the standard. The specification is available at agentskills.io, and we’re already seeing other platforms exploring compatibility.
This portability is significant for enterprises. You can invest in building Skills knowing they’re not locked to a single vendor. As the ecosystem develops, we’ll likely see marketplace dynamics emerge, with both commercial and open-source Skills available for common business needs.
Anthropic has also announced partnerships with major platforms including Notion, Canva, Figma, and Atlassian. These integrations will make it easier to build Skills that work seamlessly with your existing tools and workflows.
Getting Started: Your Next Steps
If you’re ready to start building with Skills, here’s your roadmap. First, identify a repeatable workflow in your organization that would benefit from automation and standardization. Start with something straightforward but valuable.
Next, explore the official Skills repository on GitHub at github.com/anthropics/skills. Study the example Skills to understand common patterns and best practices. The document creation Skills (docx, xlsx, pptx, pdf) are particularly instructive as they’re production-grade implementations.
Build a simple Skill for your use case. Start with just the SKILL.md file and basic instructions. Test it thoroughly in development before moving to production. Once it works reliably, create a versioned release and pin to that version in your production code.
As you gain experience, explore combining multiple Skills, integrating with MCP for external data access, and building organization-specific Skills that encode your unique workflows and standards.
Conclusion
Agent Skills represent a fundamental shift in how we extend AI capabilities. Rather than treating AI as a black box that we prompt differently each time, Skills let us package expertise, ensure consistency, and build truly reusable AI components.
The progressive disclosure architecture makes Skills remarkably efficient. The composability enables sophisticated workflows without complex orchestration code. The open standard ensures your investment is portable across platforms.
For enterprise developers, Skills solve real problems around consistency, auditability, and governance. They make AI systems more predictable and reliable, which is exactly what production environments require.
Start building your Skills today. The examples in this guide give you working implementations across multiple languages. The ecosystem is still early, which means there’s opportunity to establish expertise and build capabilities that will be valuable as Skills become more widespread.
References
- Anthropic – Introducing Agent Skills (https://www.anthropic.com/news/skills)
- Anthropic Engineering – Equipping Agents for the Real World with Agent Skills (https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills)
- Claude Documentation – Agent Skills Overview (https://platform.claude.com/docs/en/agents-and-tools/agent-skills/overview)
- Claude Documentation – Get Started with Agent Skills in the API (https://platform.claude.com/docs/en/agents-and-tools/agent-skills/quickstart)
- Claude Documentation – Using Agent Skills with the API (https://platform.claude.com/docs/en/build-with-claude/skills-guide)
- GitHub – anthropics/skills: Public Repository for Agent Skills (https://github.com/anthropics/skills)
- Simon Willison – Claude Skills Are Awesome, Maybe a Bigger Deal Than MCP (https://simonwillison.net/2025/Oct/16/claude-skills/)
- Axios – Anthropic’s Claude Chatbot Gets Update to Make Work More Orderly (https://www.axios.com/2025/12/18/anthropic-claude-enterprise-skills-update)
- AI Business – Anthropic Launches Skills Open Standard for Claude (https://aibusiness.com/foundation-models/anthropic-launches-skills-open-standard-claude)
- InfoQ – Anthropic Introduces Skills for Custom Claude Tasks (https://www.infoq.com/news/2025/10/anthropic-claude-skills/)
