Claude Agent Skills Deep Dive: A New Paradigm for Expanding AI Agent Capabilities

For example:

If you need Claude to help you develop a React form component, the Prompt approach is:

Please help me create a React form component, paying attention to the following points:
1. Use TypeScript
2. Implement form validation
3. Handle submission and error states
4. Follow team coding standards
5. Add unit tests
...

The Skills approach is:

md
---
name: react-component-dev
description: Develop React components using TypeScript, including type definitions, state management, testing, and other best practices.
---

Skills will automatically include all necessary development specifications, code templates, and testing strategies. You only need to say, "Create a form component," and Claude will know how to proceed.

Skills vs. Tools: Synergistic Relationship

In Claude’s ecosystem, Skills and Tools are complementary:

• Tools (Tool Use): Connect Claude to external systems (databases, APIs, code repositories, build tools, etc.)
• Skills (Capability): Define how these connections are used to accomplish specific tasks.

Analogy:

• Tools are like a developer's toolbox (Git, npm, Docker, database clients).
• Skills are like the professional skills to use these tools (Frontend development skill, Backend development skill, DevOps skill).

Combining both improves accuracy, reduces latency, and provides consistent results.

Core Advantages

Agent Skills offer three core advantages:

1. Specialization: Customizing capabilities for specific domain tasks, making Claude perform like a domain expert.
2. Reduced Repetition: Created once, used automatically, eliminating the need to provide the same instructions in every conversation.
3. Composition: Combining multiple Skills to build complex workflows for more powerful functionality.

Technical Architecture Deep Dive

The core innovation of Agent Skills lies in its Progressive Disclosure mechanism. This is a clever design that allows Claude to access rich, specialized knowledge without consuming excessive context.

Progressive Disclosure Mechanism

The traditional approach is to load all necessary information into the context window at once, which leads to:

• Rapid depletion of the context window
• Wasting space with irrelevant information
• Slower response times
• Increased token costs

Skills adopt a completely different strategy: loading information in stages, rather than consuming all context upfront.

Three-Tier Content Loading Model

Skills categorize content into three levels, each loaded at a different time:

Level 1: Metadata (Always Loaded)

Loading Time: When Claude starts. Content Type: Lightweight discovery information.

md
---
name: react-component-dev
description: Develop React components, including TypeScript type definitions, Hooks usage, performance optimization, testing, etc. Use when creating or reviewing React components.
---

This YAML frontmatter is included in the system prompt but is very lightweight. Claude only knows:

• The name of the Skill
• What it can do
• When it should be used

Key Advantage: You can install dozens of Skills with no context penalty, as each Skill only occupies a few dozen tokens.

Level 2: Main Instructions (Loaded on Trigger)

Loading Time: When the user request matches the Skill description. Content Type: Procedural knowledge, workflows, best practices.

markdown
# React Component Development Skill

## Quick Start

Use functional components and TypeScript:

```typescript
import React from 'react'

interface Props {
  title: string
}

export const MyComponent: React.FC<Props> = ({ title }) => {
  return <div>{title}</div>
}
```

For detailed Hooks usage patterns, see [HOOKS_GUIDE.md](HOOKS_GUIDE.md).

When you say, "Create a React component," Claude reads SKILL.md from the file system using a bash command:

bash
bash: read react-component-dev/SKILL.md

Only at this moment are these detailed instructions loaded into the context window.

Level 3: Resources and Code (Loaded on Demand)

Loading Time: When referenced by SKILL.md. Content Type: Detailed documentation, executable scripts, reference materials.

A complete Skill directory structure:

react-component-dev/
├── SKILL.md              # Main instructions
├── HOOKS_GUIDE.md        # Hooks usage guide
├── TESTING_GUIDE.md      # Testing guide
└── scripts/
    └── lint.sh           # Code checking script

Three Resource Types:

1. Instruction Files: Additional markdown files containing professional guidance and workflows.
2. Executable Scripts: Scripts for code checking, running tests, etc., providing deterministic operations.
3. Reference Materials: Code templates, configuration examples, best practice documents, etc.

Key Feature:

• Claude only accesses these files when referenced.
• When scripts execute, the code itself does not enter the context; only the output consumes tokens.
• Unused files remain in the file system, consuming zero tokens.

Architectural Advantages

This three-tier loading model brings significant advantages:

1. On-Demand File Access

A Skill can contain dozens of reference files, but if the task only requires one, Claude loads only that file. The rest remain on the file system, consuming zero tokens.

2. Efficient Script Execution

When Claude runs lint.sh to check code:

• The script code is never loaded into the context window.
• Only the script's output (e.g., "Check passed" or error messages) consumes tokens.
• This is much more efficient than having Claude generate equivalent code on the fly.

3. No Practical Content Limits

Because files are not loaded until accessed, Skills can contain:

• Complete API documentation
• Large code examples
• Detailed configuration templates
• Any required reference materials

Packaged content that is unused incurs no context penalty.

Complete Loading Flow Example

Let’s walk through a full example to understand the entire process:

Scenario: User requests, "Create a React component with form validation."

1. Initialization Phase
   System prompt includes: "react-component-dev - Develop React components..."

2. User Request
   "Create a React component with form validation."

3. Claude's Decision
   Matches the description of the react-component-dev skill.

4. Load Instructions
   bash: read react-component-dev/SKILL.md
   → Instructions loaded into context.

5. Evaluate Needs
   Form validation is required, read FORMS_GUIDE.md.
   Testing is required, view TESTING_GUIDE.md.

6. Execute Task
   Completes the task using instructions and templates from SKILL.md.

Throughout this process, only necessary content enters the context window, achieving optimal token utilization efficiency.

SKILL.md File Format

After understanding the architectural principles, let's see how to create a Skill.

Basic Structure

The core of a Skill is the SKILL.md file, which consists of two parts:

markdown
---
name: my-skill-name
description: Clearly describe the function and usage scenarios of this skill.
allowed-tools: [Optional]
---

# Skill Name

[Add instructions for Claude to follow when this skill is activated]

## Examples

- Usage Example 1
- Usage Example 2

## Guiding Principles

- Principle 1
- Principle 2

Frontmatter Fields

Required Fields:

• name: Unique identifier for the skill.

  • Lowercase letters, numbers, hyphens.
  • Max 64 characters.
  • Example: react-component-dev, go-api-dev

• description: A full description of the skill's functionality and usage context.

  • Max 1024 characters.
  • This is key for Claude to decide whether to use the Skill.
  • Should include trigger keywords and usage scenarios.

Optional Fields:

• allowed-tools: Restricts the tools Claude can use when the skill is activated.
  • Enhances control and security.
  • Example: ["bash", "python"]

Content Section Best Practices

The Markdown content after the Frontmatter defines the Skill's behavior:

1. Keep it Concise

• The main SKILL.md file should be under 5,000 characters.
• Provide an overview and quick start.
• Externalize detailed content.

2. Use Progressive Disclosure

• Provide core guidance in SKILL.md.
• Reference detailed documentation via links.
• Allow Claude to load details on demand.

3. Structured Organization

• Use clear heading hierarchies.
• Provide concrete examples.
• Include guidance for common scenarios.

Supported Files and Directories

Optional Subdirectories:

my-skill/
├── SKILL.md              # Main instructions (Required)
├── scripts/              # Executable scripts
│   ├── lint.sh
│   └── test.sh
├── references/           # Reference documents
│   ├── API.md
│   └── patterns.md
└── templates/            # Code templates
    └── component.tsx

• scripts/: Executable code (Bash, Python scripts) for deterministic operations.
• references/: Documentation or reference materials (Markdown, JSON schemas, config templates).
• templates/: Code templates and example files.

Usage Methods

Agent Skills offer two main ways to use them: pre-built Skills and custom Skills.

Pre-built Agent Skills

Anthropic provides four official Skills immediately available to all users:

1. PowerPoint (pptx): Create presentations, edit slides, analyze presentation content.
2. Excel (xlsx): Create spreadsheets, analyze data, generate reports with charts.
3. Word (docx): Create documents, edit content, format text.
4. PDF (pdf): Generate formatted PDF documents and reports.

These Skills are available in the Claude API and claude.ai without any setup required.

Custom Skill Development

Custom Skills allow you to package your organization's domain knowledge and workflows. Typical application scenarios include:

Frontend Development

• React/Vue/Angular component development standards
• Frontend performance optimization
• UI/UX best practices

Backend Development

• Go/Java/Python API development
• Database design and optimization
• Microservice architecture

DevOps

• CI/CD pipeline automation
• Containerization and orchestration
• Monitoring and log management

Comparison of Four Deployment Methods

Agent Skills support four deployment methods, each with distinct characteristics:

1. Claude API

Use Case: Production environments, organization-wide deployment.

Prerequisites: Requires three beta headers.

python
headers = {
    "anthropic-beta": "code-execution-2025-08-25,skills-2025-10-02,files-api-2025-04-14"
}

Usage:

• Pre-built Skills: Referenced by skill_id (e.g., pptx, xlsx).
• Custom Skills: Created and uploaded via the Skills API ( /v1/skills endpoint).
• Sharing Scope: Organization-wide sharing.

Advantages: Centralized management, version control, organizational sharing.

2. Claude Code

Use Case: Local development, personal projects.

Usage:

• Create a directory containing the SKILL.md file.
• Claude automatically discovers and uses it.
• File system-based, no API upload required.

Installation Example:

bash
# Add marketplace
/plugin marketplace add anthropics/skills

# Install specific skill set
/plugin install document-skills@anthropic-agent-skills

Advantages: Rapid iteration, local testing, no network required.

3. Claude Agent SDK

Use Case: Integration into custom applications.

Configuration:

• Create a directory containing SKILL.md in .claude/skills/.
• Include "Skill" in the allowed_tools configuration.
• SDK automatically discovers them at runtime.

Advantages: Deep integration with applications, flexible configuration.

4. Claude.ai

Use Case: Personal use, quick verification.

Pre-built Skills: Already working in the background when creating documents, no setup needed.

Custom Skills:

• Upload a zip file via Settings > Features.
• Available for Pro, Max, Team, and Enterprise plans (if code execution is enabled).
• Sharing Scope: User-specific, not shared organization-wide.

Advantages: Zero-configuration use of pre-built Skills, quick upload of custom Skills.

Comparison Summary:

Core Comparison Analysis

To better understand the positioning of Agent Skills, let's compare them with related concepts.

Agent Skills vs. MCP (Model Context Protocol)

Many people confuse Skills and MCP, but they solve entirely different problems.

MCP: Connectivity and Access (Infrastructure)

Positioning: MCP is an open standard providing a standardized interface for AI to access external systems.

Core Features:

• Client-Server Architecture: AI applications act as MCP clients; external systems expose functionalities via MCP servers.
• Standardized Connection: Like "USB-C for LLMs," offering a unified way to connect.
• External Integration: Connecting to file systems, GitHub, databases, and other external resources.

Analogy: MCP is the infrastructure of the development environment, providing access to various tools and services.

Skills: Capability and Task Execution (Expertise)

Positioning: Skills define how to use these connections to accomplish specific tasks.

Core Features:

• Specialized Knowledge Packaging: Encoding domain knowledge and workflows into reusable modules.
• Task Execution Guidance: Providing detailed steps and best practices for completing specific tasks.
• Portability: Usable across platforms (Claude Code, API, claude.ai, etc.).

Analogy: Skills are the professional expertise and operational manuals for using this infrastructure.

Relationship and Synergy

MCP provides the infrastructure; Skills provide the specialized knowledge.

Specific Example:

Suppose you need Claude to analyze the code quality of a GitHub repository:

1. MCP's Role:

   • Provides a standardized interface for accessing the GitHub API.
   • Allows Claude to read repository content, commit history, PRs, etc.

2. Skills' Role:

   • Defines the workflow for code quality analysis.
   • Provides best practices for code review.
   • Guides how to generate analysis reports.

Use Case Comparison:

Agent Skills vs. Context Management

The context window is the LLM's "working memory," and efficiently using it is a core challenge in AI application development.

Traditional Context Management: One-time Loading

Method: Loading all potentially needed information into the context at once.

Problems:

• Context window depletes rapidly.
• Irrelevant information wastes space.
• High token costs.
• Slower response times.

Example:

System Prompt:
- React development standards (2000 tokens)
- Go API standards (3000 tokens)
- Testing strategy (2000 tokens)
- Code examples (3000 tokens)
Total: 10000 tokens

User Request: "Create a simple React button component."
Actual Need: React standards + some testing strategy (approx. 2500 tokens)
Wasted: 7500 tokens

Skills Method: Progressive Disclosure

Method: Tiered loading, on-demand access.

Advantages:

• Only loads necessary content.
• Dynamically responds to task requirements.
• High token utilization efficiency.
• Can package a large amount of reference material.

Same Example:

Level 1 (Startup):
- React standards Skill metadata (50 tokens)
- Go API standards Skill metadata (50 tokens)
- Testing strategy Skill metadata (50 tokens)
Total: 150 tokens

User Request: "Create a simple React button component."

Level 2 (Triggered):
- Load React standards SKILL.md (1500 tokens)
- Load component testing section from TESTING_GUIDE.md (800 tokens)
Total: 2300 tokens

Savings: 7700 tokens (77%)

Token Optimization Comparison

Scenario: An AI assistant covering 10 specialized domains.

Use Case Comparison

Using Traditional Context Management:

• Simple, one-off tasks.
• Context requirement under 2000 tokens.
• Scenarios where reuse is not necessary.

Using Skills:

• Tasks requiring extensive domain knowledge.
• Repetitive, specialized work.
• Scenarios needing cross-conversation reuse.
• Organizational knowledge management.

Agent Skills vs. Prompt Engineering

Prompt Engineering and Skills are both ways to guide AI behavior, but they suit different scenarios.

Prompt Engineering: Temporary Instructions

Characteristics:

• Session-level instructions.
• Flexible, immediate.
• Suitable for one-off tasks.
• Must be rewritten every time.

Example:

Help me create a Go HTTP handler, noting:
1. Use standard error handling.
2. Add request logging.
3. Implement input validation.
4. Return JSON responses.
Follow RESTful design principles.

Skills: Persistent Capability

Characteristics:

• Persistent knowledge base.
• Reusable, standardized.
• Suitable for repetitive tasks.
• Created once, used automatically.

Example:

md
---
name: go-api-dev
description: Develop Go RESTful APIs, including project structure, error handling, middleware, testing, and security best practices.
---

# Go API Development Skill

## Handler Template

[Complete handler template and best practices]

## Error Handling

[Standardized error handling patterns]

## Testing Strategy

[Unit testing and integration testing guidelines]

Use Case Comparison

Combined Usage: Best Practices

The most powerful approach is combining Prompts and Skills:

Skills provide:
- Standardized development frameworks
- Team best practices
- Code templates and tools

Prompts provide:
- Specific feature requirements
- Unique business logic
- Temporary adjustments

Example:
"Use the go-api-dev skill to create a user management API,
paying special attention to implementing password encryption and JWT authentication,
as this service is internet-facing."

Practical Application Scenarios

Let's examine specific use cases to understand the actual value of Skills. All examples focus on real-world development scenarios.

Scenario 1: React Component Development Standards

Requirement: Frontend team needs to create components following unified React development standards.

Problems with Traditional Methods:

• Having to consult code standard documents every time.
• Inconsistent component styles across different developers.
• Repeated issues found during Code Review.
• Difficult onboarding for new hires.

Solution with Skills:

md
---
name: react-component-dev
description: Develop React components using TypeScript, including type definitions, Hooks best practices, performance optimization, testing, and accessibility. Use when creating or reviewing React components.
---

# React Component Development Skill

## Component Structure

### Functional Component Template

```typescript
import React from 'react'
import styles from './ComponentName.module.css'

interface ComponentNameProps {
  // Props definition
}

export const ComponentName: React.FC<ComponentNameProps> = (
  {
    // Destructured props
  }
) => {
  // Hooks (useState, useEffect, etc.)

  // Event handlers

  // Rendering helpers

  return <div className={styles.container}>{/* JSX */}</div>
}
```

Best Practices

1. TypeScript Usage

• Always define prop interfaces.
• Use strict type checking.
• Avoid using any types.
• See [TYPESCRIPT_GUIDE.md](TYPESCRIPT_GUIDE.md) for details.

2. Hooks Rules

• Call Hooks at the top level.
• Use custom Hooks to reuse logic.
• Correctly set dependency arrays in useEffect.
• See [HOOKS_PATTERNS.md](HOOKS_PATTERNS.md) for details.

3. Performance Optimization

• Use React.memo to optimize expensive components.
• Use useMemo/useCallback to optimize re-renders.
• Use React.lazy for code splitting.
• See [PERFORMANCE.md](PERFORMANCE.md) for details.

4. Testing

• Write tests for every component.
• Test user interactions, not implementation details.
• Use React Testing Library.
• See [TESTING_GUIDE.md](TESTING_GUIDE.md) for details.

5. Accessibility

• Use semantic HTML.
• Add ARIA attributes where necessary.
• Support keyboard navigation.
• See [A11Y_CHECKLIST.md](A11Y_CHECKLIST.md) for details.

Code Style

• Run [scripts/lint.sh](scripts/lint.sh) before committing.
• Follow [STYLE_GUIDE.md](STYLE_GUIDE.md).
• Component naming: PascalCase.
• File naming: ComponentName.tsx.

Common Patterns

• Form handling: [patterns/forms.md](patterns/forms.md)
• Data fetching: [patterns/data-fetching.md](patterns/data-fetching.md)
• State management: [patterns/state.md](patterns/state.md)

Directory Structure:

react-component-dev/
├── SKILL.md # Main guidance
├── TYPESCRIPT_GUIDE.md # TypeScript best practices
├── HOOKS_PATTERNS.md # Hooks usage patterns
├── PERFORMANCE.md # Performance optimization guide
├── TESTING_GUIDE.md # Testing guide
├── A11Y_CHECKLIST.md # Accessibility checklist
├── STYLE_GUIDE.md # Code style guide
├── scripts/
│ ├── lint.sh # Lint check script
│ └── test.sh # Test execution script
└── patterns/
├── forms.md # Form handling patterns
├── data-fetching.md # Data fetching patterns
└── state.md # State management patterns

Effect:

• Component code consistency improved by 90%.
• Code Review time reduced by 50%.
• New hire ramp-up time reduced from 2 weeks to 3 days.
• Accessibility issues reduced by 80%.

Scenario 2: Go API Development Standards

Requirement: Backend team needs to create RESTful APIs following unified Go development standards.

Problems with Traditional Methods:

• Inconsistent API design.
• Varied ways of handling errors.
• Lack of unified logging and monitoring.
• Incomplete security checks.

Solution with Skills:

md
---
name: go-api-dev
description: Develop RESTful APIs using Go, including project structure, error handling, middleware, testing, and security best practices. Use when developing Go backend services.
---

# Go API Development Skill

## Project Structure

project/
├── cmd/
│ └── server/
│ └── main.go # Entry point
├── internal/
│ ├── handler/ # HTTP handlers
│ ├── service/ # Business logic
│ ├── repository/ # Data access
│ └── middleware/ # Middleware
├── pkg/
│ ├── errors/ # Error types
│ └── response/ # Response helpers
├── config/
│ └── config.go # Configuration
└── tests/
├── integration/ # Integration tests
└── unit/ # Unit tests

## Handler Template

```go
package handler

import (
    "encoding/json"
    "net/http"

    "project/pkg/errors"
    "project/pkg/response"
)

type UserHandler struct {
    userService UserService
    logger      Logger
}

func NewUserHandler(svc UserService, log Logger) *UserHandler {
    return &UserHandler{
        userService: svc,
        logger:      log,
    }
}

func (h *UserHandler) CreateUser(w http.ResponseWriter, r *http.Request) {
    ctx := r.Context()

    // 1. Parse and validate request
    var req CreateUserRequest
    if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
        response.Error(w, errors.BadRequest("Invalid request body"))
        return
    }

    if err := req.Validate(); err != nil {
        response.Error(w, errors.ValidationError(err))
        return
    }

    // 2. Call service layer
    user, err := h.userService.Create(ctx, req)
    if err != nil {
        h.logger.Error("Failed to create user", "error", err)
        response.Error(w, err)
        return
    }

    // 3. Return response
    response.JSON(w, http.StatusCreated, user)
}
```

Best Practices

1. Error Handling

• Use custom error types.
• Wrap errors using context.
• Use structured logging for errors.
• See [ERROR_HANDLING.md](ERROR_HANDLING.md) for details.

2. Middleware

• Authentication/Authorization.
• Request logging.
• Rate limiting.
• CORS handling.
• See [MIDDLEWARE_GUIDE.md](MIDDLEWARE_GUIDE.md) for details.

3. Database

• Use Repository pattern.
• Handle transactions correctly.
• Use prepared statements.
• Connection pool configuration.
• See [DATABASE_GUIDE.md](DATABASE_GUIDE.md) for details.

4. Testing

• Unit tests for business logic.
• Integration tests for APIs.
• Use table-driven tests.
• Mock external dependencies.
• See [TESTING_GUIDE.md](TESTING_GUIDE.md) for details.

5. Security

• Input validation.
• SQL injection protection.
• XSS protection.
• Rate limiting.
• See [SECURITY_CHECKLIST.md](SECURITY_CHECKLIST.md) for details.

Code Quality

• Run [scripts/lint.sh](scripts/lint.sh) (golangci-lint).
• Format using gofmt.
• Follow [STYLE_GUIDE.md](STYLE_GUIDE.md).
• Use [scripts/test.sh](scripts/test.sh) to run tests.

Common Patterns

• Pagination: [patterns/pagination.md](patterns/pagination.md)
• Authentication: [patterns/auth.md](patterns/auth.md)
• File uploads: [patterns/file-upload.md](patterns/file-upload.md)

Directory Structure:

go-api-dev/
├── SKILL.md # Main guidance
├── ERROR_HANDLING.md # Error handling guide
├── MIDDLEWARE_GUIDE.md # Middleware development guide
├── DATABASE_GUIDE.md # Database best practices
├── TESTING_GUIDE.md # Testing guide
├── SECURITY_CHECKLIST.md # Security checklist
├── STYLE_GUIDE.md # Code style guide
├── scripts/
│ ├── lint.sh # Lint check
│ ├── test.sh # Run tests
│ └── migrate.sh # Database migration
└── patterns/
├── pagination.md # Pagination patterns
├── auth.md # Authentication patterns
└── file-upload.md # File upload patterns

Effect:

• API design consistency improved by 95%.
• Security vulnerabilities reduced by 70%.
• Code review time reduced by 45%.
• New service development speed doubled.

Scenario 3: Go Microservice Development

Requirement: Team needs to rapidly develop Go services adhering to microservice architecture standards.

Problems with Traditional Methods:

• Inconsistent service-to-service communication methods.
• Lack of unified observability.
• Chaotic configuration management.
• Non-standardized deployment processes.

Solution with Skills:

md
---
name: go-microservice
description: Develop microservices using Go, including gRPC, observability, configuration management, and deployment best practices. Use when creating new microservices or refactoring existing ones.
---

# Go Microservice Development Skill

## Service Template

### gRPC Service Definition

```protobuf
syntax = "proto3";

package user.v1;

service UserService {
  rpc CreateUser(CreateUserRequest) returns (CreateUserResponse);
  rpc GetUser(GetUserRequest) returns (GetUserResponse);
  rpc ListUsers(ListUsersRequest) returns (ListUsersResponse);
}

message CreateUserRequest {
  string name = 1;
  string email = 2;
}

message CreateUserResponse {
  User user = 1;
}
```

Service Implementation

go
package service

import (
    "context"

    pb "project/api/user/v1"
    "project/internal/repository"
)

type UserService struct {
    pb.UnimplementedUserServiceServer
    repo   repository.UserRepository
    logger Logger
    tracer Tracer
}

func (s *UserService) CreateUser(
    ctx context.Context,
    req *pb.CreateUserRequest,
) (*pb.CreateUserResponse, error) {
    // Start trace span
    ctx, span := s.tracer.Start(ctx, "CreateUser")
    defer span.End()

    // Validate request
    if err := validateCreateUserRequest(req); err != nil {
        s.logger.Error("Validation failed", "error", err)
        return nil, status.Error(codes.InvalidArgument, err.Error())
    }

    // Create user
    user, err := s.repo.Create(ctx, req)
    if err != nil {
        s.logger.Error("Failed to create user", "error", err)
        span.RecordError(err)
        return nil, status.Error(codes.Internal, "Internal error")
    }

    s.logger.Info("User created", "user_id", user.ID)
    return &pb.CreateUserResponse{User: user}, nil
}

Architecture Components

1. Service Communication

• Use gRPC for inter-service communication.
• Implement the Circuit Breaker pattern.
• Use exponential backoff for retries.
• See [GRPC_GUIDE.md](GRPC_GUIDE.md) for details.

2. Observability

• Structured logging (zerolog/zap).
• Distributed tracing (OpenTelemetry).
• Metrics collection (Prometheus).
• Health checks.
• See [OBSERVABILITY.md](OBSERVABILITY.md) for details.

3. Configuration Management

• Use environment variables.
• Support configuration files (YAML/JSON).
• Secret management.
• Feature flags.
• See [CONFIG_GUIDE.md](CONFIG_GUIDE.md) for details.

4. Data Management

• Each service has an independent database.
• Key data uses Event Sourcing.
• Distributed transactions use the Saga pattern.
• See [DATA_PATTERNS.md](DATA_PATTERNS.md) for details.

5. Deployment

• Docker containerization.
• Kubernetes manifests.
• CI/CD pipelines.
• See [DEPLOYMENT.md](DEPLOYMENT.md) for details.

Service Checklist

• gRPC service definition
• Health check endpoint
• Metrics endpoint
• Structured logging
• Distributed tracing
• Configuration management
• Unit and integration tests
• Docker image
• Kubernetes manifest
• CI/CD pipeline

Tools and Scripts

• Code generation: [scripts/generate.sh](scripts/generate.sh)
• Local execution: [scripts/run-local.sh](scripts/run-local.sh)
• Build Docker: [scripts/build-docker.sh](scripts/build-docker.sh)
• Deployment: [scripts/deploy.sh](scripts/deploy.sh)

Directory Structure:

go-microservice/
├── SKILL.md # Main guidance
├── GRPC_GUIDE.md # gRPC development guide
├── OBSERVABILITY.md # Observability guide
├── CONFIG_GUIDE.md # Configuration management guide
├── DATA_PATTERNS.md # Data management patterns
├── DEPLOYMENT.md # Deployment guide
├── scripts/
│ ├── generate.sh # Code generation
│ ├── run-local.sh # Local run
│ ├── build-docker.sh # Docker build
│ └── deploy.sh # Deployment script
└── templates/
├── service.go.tmpl # Service template
├── Dockerfile.tmpl # Dockerfile template
└── k8s.yaml.tmpl # Kubernetes template

Effect:

• New service development time reduced from 2 weeks to 3 days.
• Service observability coverage is 100%.
• Time to locate production issues reduced by 60%.
• Deployment failure rate reduced by 80%.

Development Best Practices

Based on practical usage experience, here are some best practices for developing Skills.

1. Principle of Conciseness

Keep the main SKILL.md file under 5,000 characters.

❌ Bad Practice:

markdown
# React Development Skill

## Introduction

React is a JavaScript library for building user interfaces...
(3000 characters of background introduction)

## Component Lifecycle

Component lifecycle is a core concept in React...
(5000 characters of detailed explanation)

## Hooks API

Hooks are a new feature introduced in React 16.8...
(4000 characters of API documentation)

✅ Good Practice:

markdown
# React Development Skill

## Quick Start

Develop using functional components and Hooks.

## Common Workflows

1. Component development: See `[COMPONENTS.md](COMPONENTS.md)`
2. State management: See `[STATE.md](STATE.md)`
3. Performance optimization: See `[PERFORMANCE.md](PERFORMANCE.md)`

## Examples

- Basic component: `[examples/basic.tsx](examples/basic.tsx)`
- Advanced patterns: `[examples/advanced.tsx](examples/advanced.tsx)`

2. Progressive Disclosure Strategy

Provide an overview in SKILL.md, externalize detailed content.

Directory Structure:

react-dev-skill/
├── SKILL.md                 # Overview and quick start (< 1000 characters)
├── COMPONENTS.md            # Detailed component development guide
├── STATE.md                 # State management methods
├── PERFORMANCE.md           # Performance optimization tips
├── scripts/
│   ├── lint.sh              # Code checking script
│   └── test.sh              # Test execution script
└── references/
    ├── hooks-cheatsheet.md  # Hooks cheat sheet
    └── patterns.md          # Common pattern references

SKILL.md referencing via links:

markdown
For detailed component development workflows, see `[COMPONENTS.md](COMPONENTS.md)`.
To run automated tests, use `[scripts/test.sh](scripts/test.sh)`.

3. Utilizing Scripts for Efficiency

Implement deterministic operations as scripts.

Why use scripts?:

• Script code does not enter the context; only output consumes tokens.
• Execution is faster, and results are more reliable.
• Can handle complex operations and transformations.

Example:

❌ Letting Claude generate code:

markdown
## Code Validation

Have Claude write validation code for each file.

• Code generated every time.
• Consumes many tokens.
• May have inconsistencies.

✅ Using predefined scripts:

Code Validation

Run the validation script:

bash
bash scripts/validate.sh src/

The script checks:

• Code formatting.
• Type errors.
• Test coverage.
• Security issues.

Script Example (validate.sh):

bash
#!/bin/bash

DIR=$1

echo "Running code checks..."

# Format check
if ! npm run lint -- "$DIR"; then
    echo "❌ Code format check failed"
    exit 1
fi

# Type check
if ! npm run type-check; then
    echo "❌ Type check failed"
    exit 1
fi

# Tests
if ! npm run test -- --coverage; then
    echo "❌ Tests failed"
    exit 1
fi

echo "✅ All checks passed!"

4. Clear Descriptions

The description field is key for Claude's decision to use a Skill.

❌ Vague Description:

md
---
name: dev-skill
description: Helps with development
---

✅ Clear Description:

md
---
name: react-component-dev
description: Develop React components using TypeScript, including type definitions, Hooks usage, performance optimization, testing, and accessibility. Use when creating or reviewing React components.
---

The description should include:

1. Functional description (what it does).
2. Technology stack (what it uses).
3. Trigger keywords (when to use it).
4. Scope coverage (what it includes).

5. Modular Design

Each Skill should focus on a single responsibility.

❌ Overly Large Skill:

super-dev-skill/
├── SKILL.md (Contains all content for frontend, backend, DevOps, etc.)

✅ Modular Skills:

skills/
├── react-component-dev/
│   └── SKILL.md
├── go-api-dev/
│   └── SKILL.md
└── go-microservice/
    └── SKILL.md

Combined Usage:

User: "Develop a user management feature, including frontend interface and backend API."

Claude:
1. Uses react-component-dev skill to create frontend components.
2. Uses go-api-dev skill to create backend API.
3. The two Skills work together.

6. Security Considerations

Only deploy Skills from trusted sources.

Potential Risks:

• Malicious script execution.
• Data leakage.
• System compromise.

Security Measures:

1. Review Skill Sources

   • Use Skills only from official or trusted sources.
   • Check GitHub repository stars and activity levels.

2. Review Script Content

   • Inspect all scripts in the scripts/ directory.
   • Ensure no suspicious system calls or network requests.

3. Use allowed-tools for Restriction

md
---
name: safe-skill
description: A safe skill that restricts tool access.
allowed-tools: ['bash'] # Only allow bash, no network access allowed.
---

4. Organization-level Management
   • Centrally review and approve Skills when deploying via the API.
   • Establish Skill management processes.
   • Conduct regular audits of deployed Skills.

Summary and Outlook

Core Value of Agent Skills

Agent Skills represent a new paradigm for extending AI agent capabilities, achieving efficient specialization through the following features:

1. Progressive Loading

• Loads content on demand, optimizing token usage.
• Can package virtually unlimited reference materials.
• Token savings up to 94%.

2. File System Architecture

• Supports script execution, providing deterministic operations.
• Code does not enter the context; only output consumes tokens.
• Natural file organization structure.

3. Composability

• Combining multiple Skills to build complex workflows.
• Each Skill focuses on a single responsibility.
• Flexible adaptation to different scenarios.

4. Cross-Platform Support

• API, Claude.ai, Claude Code, Agent SDK.
• Unified SKILL.md format.
• Strong portability.

Use Case Summary

Agent Skills:

• ✅ Specialized tasks that require repetitive execution.
• ✅ Packaging domain knowledge and best practices.
• ✅ Complex workflows requiring the combination of multiple capabilities.
• ✅ Scenarios needing organizational sharing and reuse of workflows.
• ✅ Optimization of token usage and costs.

Traditional Prompts:

• ✅ One-off, exploratory tasks.
• ✅ Quick prototyping validation.
• ✅ Context needs under 2000 tokens.
• ✅ Scenarios not requiring cross-session reuse.

MCP:

• ✅ Need to access external systems and data sources.
• ✅ Need standardized integration interfaces.
• ✅ Need to connect multiple external services.

Best Practice: Combination

MCP (Connecting external systems)
  +
Skills (Specialized knowledge and workflows)
  +
Prompt (Specific tasks and context)
  =
A powerful AI agent system

Future Development Directions

Agent Skills, as an emerging standard, may evolve in the following directions:

1. Ecosystem Expansion

• More official pre-built Skills.
• Community-contributed Skills marketplace.
• Industry-specific Skill libraries.

2. Toolchain Improvement

• Tools for Skill development and testing.
• Performance analysis and optimization tools.
• Version management and collaboration tools.

3. Standardization and Interoperability

• Skills standards across different AI platforms.
• Integration with other AI agent frameworks.
• Richer metadata and discovery mechanisms.

4. Enterprise Features

• Finer-grained permission controls.
• Audit and compliance support.
• Centralized management and monitoring.

Recommendations for Getting Started

If you want to start using Agent Skills, here is a suggested path:

1. Start with Pre-built Skills

• Try the document processing Skills on claude.ai.
• Understand how Skills work and their value.

2. Create a Simple Custom Skill

• Start with a small, well-defined use case.
• Create a basic SKILL.md using a template.
• Test it in Claude Code.

3. Gradually Increase Complexity

• Add external reference files.
• Introduce scripts for automation.
• Combine multiple Skills.

4. Organization-wide Deployment

• Deploy to production via the API.
• Establish Skill management processes.
• Collect feedback and iterate continuously.

Reference Resources

Official Documentation

• Agent Skills Overview
• Skills Quickstart
• Skills API Guide
• Agent SDK Skills
• Claude Code Skills

Help Center

• What are Skills?
• Using Skills in Claude
• Creating Custom Skills
• Teach Claude Your Way of Working

Code and Examples

• GitHub: anthropics/skills
• Skills Cookbook

Engineering Blog

• Equipping Agents for the Real World with Agent Skills