Claude Code Skills is the modular extension mechanism for Claude Code (Claude’s programming agent). Essentially, they are structured instruction packages used to teach Claude how to handle specific domain programming tasks. It encapsulates domain expertise, workflows, and execution logic into reusable “skill packages,” enabling Claude to perform tasks like a domain expert.

Key Features and Design Principles

The Model-invoked mechanism is the core feature of Skills. Unlike traditional imperative triggers, Skills adopt a declarative design where Claude automatically analyzes user intent based on conversation context to judge whether to enable a specific Skill. This design reduces user cognitive load, eliminating the need to memorize specific command syntax, making interactions more natural and fluid.

The Progressive Disclosure principle is reflected in the skill execution process. Relevant information is injected gradually according to task progress to avoid information overload at once and optimize Token consumption. For example, in a code review skill, Claude will first analyze the code structure, then load specific checking rules and best practices as the review progresses.

Standard Operating Procedures (SOP) ensure consistency and professionalism in outputs. Each Skill standardizes highly repetitive and professional task flows, such as code reviews, test writing, API design, etc., ensuring that the quality of output remains consistent across different times and users.

Positioning and Boundary Clarification

Difference from Slash commands: Slash commands require users to manually input specific triggers (like /review), suitable for executing repetitive tasks or scripts; Skills are automated professional knowledge bases, where Claude automatically judges when to apply specific skills based on context.

Difference from CLAUDE.md: CLAUDE.md is a project-level global instruction file loaded in every conversation turn, defining general rules and preferences for the project; Skills are specialized capabilities for specific tasks, activated only when relevant tasks appear.

Relation to MCP (Model Context Protocol): MCP focuses on connecting external data sources and tools to provide data access capabilities; Skills tell Claude how to utilize these tools and data to execute specific tasks, being an encapsulation of application-layer logic.

Architecture Design and Implementation Principles

Layered Storage Architecture

Skills adopt a multi-layer storage design to support sharing and priority coverage across different scopes:

Priority Coverage Rule: When Skills with the same name exist in multiple levels, they override in the order of Enterprise > Personal > Project > Plugin. This ensures organizational standards take precedence over personal preferences while allowing necessary personalization.

Skill Package Structure and Composition

Each Skill is an independent directory following a standardized file structure:

code-review-skill/
├── SKILL.md                    # Metadata and core instructions (YAML format)
├── instructions.md             # Detailed workflows and rules
├── resources/                  # Knowledge base, templates, scripts
│   ├── security-checklist.md   # Security checklist
│   ├── performance-guidelines.md # Performance guidelines
│   └── template-review-report.md # Review report template
└── tools/                      # External tool integration definitions
    ├── linter-config.json      # Linter tool configuration
    └── test-coverage-script.sh # Test coverage script

Execution Mechanism and Process

1. Discovery and Loading Mechanism: Claude Code scans all configured Skills directories upon startup, parses metadata, and builds a skill index. The scanning process uses an incremental strategy, checking only files with changed modification times.

2. Intent Recognition and Trigger Detection: Matches user intent based on keywords, task type descriptions, and context patterns in metadata. Trigger detection uses a multi-factor weighted algorithm considering keyword match degree, context relevance, and historical usage patterns.

3. Context Injection Strategy: Adopts dynamic context management, loading relevant instructions gradually according to task phases. Only core instructions are loaded initially, with detailed rules and resources loaded as needed during execution.

4. Task Execution and Tool Invocation: Executes according to predefined workflows, capable of calling external tools and scripts. The execution process supports interruption and resumption, allowing user intervention and direction adjustment.

5. Multi-Skill Collaboration Mechanism: Supports chained skill invocation, e.g., CodeReviewSkill can automatically call TestSkill to generate test cases, or call DocumentationSkill to update documentation.

Usage Steps: From Environment Configuration to Skill Development

Step 1: Environment Preparation and Basic Configuration

Confirm Claude Code version supports Skills feature. Check Claude Code version:

claude --version
# Output Example: Claude Code 1.5.0 (Skills Supported)

Configure Skills storage path. Edit Claude Code configuration file (usually located at ~/.claude/config.yaml):

skills:
  # Enable skills feature
  enabled: true
  
  # Skill directory configuration (in priority order)
  directories:
    - path: /path/to/enterprise/skills  # Enterprise skills
      priority: 100
    - path: ~/.claude/skills            # Personal skills
      priority: 80
    - path: ./.claude/skills            # Project skills
      priority: 60
    - path: /path/to/plugin/skills      # Plugin skills
      priority: 40
  
  # Performance optimization config
  cache:
    enabled: true
    ttl: 3600  # Cache time to live (seconds)
  scan:
    interval: 300  # Directory scan interval (seconds)
    incremental: true  # Incremental scan

Step 2: Create Your First Skill Package

Create skill directory structure. Taking a code review skill as an example:

# Create new skill in personal skills directory
mkdir -p ~/.claude/skills/code-review-skill
cd ~/.claude/skills/code-review-skill

# Create core files
touch SKILL.md instructions.md
mkdir -p resources tools

Write skill metadata file (SKILL.md):

---
name: code-review-skill
description: "Professional code review skill providing comprehensive code quality checks and security reviews"
version: 1.0.0
author: "Your Name"
created: "2024-01-15"
updated: "2024-01-15"

# Trigger condition configuration
triggers:
  - type: keyword
    patterns:
      - "review this code"
      - "code review"
      - "check the implementation"
      - "review code"
    threshold: 0.7  # Match threshold
  
  - type: task_type
    patterns:
      - "programming"
      - "code_analysis"
      - "quality_assurance"
  
  - type: file_extension
    patterns:
      - ".py"
      - ".js"
      - ".ts"
      - ".java"
      - ".go"

# Dependency declaration
dependencies:
  - tool: 
      name: "pylint"
      version: ">=2.17.0"
      optional: false
  - tool:
      name: "eslint"
      version: ">=8.0.0"
      optional: true
  - library:
      name: "claude-code-tools"
      version: "^1.2.0"

# Permission configuration
permissions:
  file_read:
    patterns:
      - "**/*.py"
      - "**/*.js"
      - "**/*.ts"
      - "**/*.java"
    recursive: true
  file_write: false
  command_execute:
    allowed: ["pylint", "eslint", "npm", "pip"]
    restricted: ["rm", "shutdown", "format"]
  network_access: false

# Execution parameters
parameters:
  review_depth:
    type: "enum"
    values: ["quick", "standard", "deep"]
    default: "standard"
  include_security:
    type: "boolean"
    default: true
  generate_report:
    type: "boolean"
    default: true

# Performance configuration
performance:
  max_context_tokens: 4000
  lazy_load_resources: true
  cache_intermediate: true
---

Step 3: Write Detailed Instructions and Workflows

Define specific execution logic in instructions.md:

# Code Review Skill - Detailed Instructions

## Review Process (Standard Mode)

### Phase 1: Preliminary Analysis
1. Identify code language and framework
2. Analyze code structure and module division
3. Check basic syntax and formatting issues
4. Assess code complexity and maintainability

### Phase 2: Deep Review
1. **Architecture Design Review**
   - Check dependency relationships between modules
   - Assess applicability of design patterns
   - Verify rationality of layered architecture

2. **Code Quality Check**
   - Cyclomatic complexity should not exceed 15
   - Function length should not exceed 50 lines
   - Class single responsibility check
   - Duplicate code detection

3. **Security Review** (When include_security=true)
   - SQL injection risk check
   - XSS vulnerability detection
   - Hardcoded sensitive information check
   - Missing permission validation check

### Phase 3: Tool-Assisted Check
1. Run static code analysis tools
   - Python: pylint, bandit (security)
   - JavaScript/TypeScript: eslint, sonarjs
   - Java: checkstyle, spotbugs

2. Generate Quality Metric Report
   - Code coverage (if tests exist)
   - Technical debt assessment
   - Maintainability index

## Output Format Requirements

### Review Report Structure

Code Review Report

Overview

• Review Time: [timestamp]
• Files Reviewed: [file list]
• Overall Rating: [A-F]

Key Issues (Sorted by Priority)

1. [P0] Security Issue: [description]
2. [P1] Architecture Issue: [description]
3. [P2] Code Quality Issue: [description]

Detailed Issue List

Security Issues

• [File:Line] [Issue Description] [Fix Suggestion]

Architecture Issues

• [File:Line] [Issue Description] [Refactoring Suggestion]

Code Quality Issues

• [File:Line] [Issue Description] [Improvement Suggestion]

Positive Findings

• [Good Practice 1]
• [Good Practice 2]

Improvement Suggestions Summary

1. Short-term Action Items (This week)
2. Mid-term Improvements (This quarter)
3. Long-term Optimization Directions

## Negative Examples (Patterns to Avoid)

### Review Comments to Avoid
- ❌ "This code is bad" (Too vague)
- ❌ "Rewrite the whole module" (Lacks specific guidance)
- ❌ "Write it in my style" (Subjective preference)

### Review Approaches to Avoid
- Don't focus only on formatting while ignoring architectural defects
- Don't offer idealized suggestions that cannot be implemented
- Don't ignore business context and constraints

## Concrete Examples

### Good Review Comments
```python
# Code to Review
def process_data(data):
    result = []
    for item in data:
        # Complex nested logic
        if item['type'] == 'A':
            if item['value'] > 100:
                result.append(transform_a(item))
        elif item['type'] == 'B':
            # More nesting...
    
# Good Review Suggestion
Suggestion: Consider using the Strategy Pattern to replace the complex if-elif chain to improve scalability.
Refactoring Example:
class DataProcessor:
    def __init__(self):
        self.strategies = {'A': TransformA(), 'B': TransformB()}
    
    def process(self, data):
        return [self.strategies[item['type']].execute(item) for item in data]

Quality Checklist

Mandatory Checks

• All functions have docstrings
• Error handling completeness
• Resource release (files, connections, etc.)
• Input validation and boundary checks
• Logging appropriateness

Recommended Checks

• Performance considerations (Time complexity)
• Memory usage optimization
• Concurrency safety
• Backward compatibility
• Configuration adjustability

### Step 4: Configure Tools and Resources

Create tool configuration files in the `tools/` directory. For example, create a Python code check config:

```json
// tools/pylint-config.json
{
  "extends": "pylint.extensions.mccabe",
  "disable": [
    "missing-docstring",
    "too-few-public-methods",
    "line-too-long"
  ],
  "enable": [
    "refactoring",
    "design",
    "format"
  ],
  "messages": {
    "convention": {
      "max-line-length": 100,
      "max-args": 5,
      "max-locals": 10
    },
    "refactor": {
      "max-complexity": 15,
      "max-branches": 12,
      "max-statements": 50
    }
  },
  "security": {
    "enable": true,
    "checks": [
      "eval-used",
      "exec-used",
      "pickle-usage"
    ]
  }
}

Add checklists and templates to the resources/ directory:

# resources/security-checklist.md

## Authentication and Authorization
- [ ] Password storage uses salted hashing
- [ ] Session token securely generated and validated
- [ ] Permission checks performed on server side
- [ ] Principle of least privilege applied

## Input Validation
- [ ] All user inputs validated
- [ ] Use whitelisting instead of blacklisting
- [ ] File upload type and size limits
- [ ] SQL parameterized queries

## Data Protection
- [ ] Sensitive data stored encrypted
- [ ] Sensitive info not recorded in logs
- [ ] Data transmission uses TLS
- [ ] Secure key management

Step 5: Test and Verify Skill

Create test cases to verify skill functionality:

# Create test project
mkdir -p /tmp/test-project
cd /tmp/test-project

# Create test code file
cat > test_code.py << 'EOF'
def insecure_function(user_input):
    # Security vulnerability example: SQL Injection
    query = f"SELECT * FROM users WHERE name = '{user_input}'"
    return execute_query(query)

def complex_function(x):
    # Code quality issue: High cyclomatic complexity
    if x > 0:
        if x < 10:
            for i in range(x):
                if i % 2 == 0:
                    print("even")
                else:
                    print("odd")
        elif x < 20:
            return x * 2
    elif x < 0:
        return -x
    else:
        return 0
EOF

# Test skill via Claude Code
claude code --skill-test code-review-skill test_code.py

Verify if skill output meets expectations. Check:

1. Did it correctly identify security issues (SQL Injection)?
2. Did it detect code quality issues (high cyclomatic complexity)?
3. Does the report format meet requirements?
4. Are suggestions specific and actionable?

Step 6: Deploy and Share Skill

Package the skill for team use:

# Create skill package
cd ~/.claude/skills/code-review-skill

# Create deployment manifest
cat > deploy.yaml << 'EOF'
name: code-review-skill
version: 1.0.0
dependencies:
  - pylint>=2.17.0
  - bandit>=1.7.0
install_script: |
  pip install pylint bandit
  mkdir -p ~/.claude/skills
  cp -r . ~/.claude/skills/code-review-skill
EOF

# Verify skill package integrity
claude skill validate code-review-skill

Key Implementation Details and Best Practices

Metadata Design Patterns

Effective Skills metadata should contain these key elements:

# Trigger condition optimization design
triggers:
  # Multi-pattern composite trigger to improve accuracy
  - type: composite
    conditions:
      - type: keyword
        patterns: ["refactor", "improve", "optimize"]
        weight: 0.3
      - type: code_pattern
        patterns: ["long_method", "deep_nesting", "high_complexity"]
        weight: 0.4
      - type: context
        patterns: ["after_implementation", "during_maintenance"]
        weight: 0.3
    threshold: 0.6  # Combined threshold

Instruction Design Best Practices

1. Concrete over Abstract: Use concrete code examples instead of abstract descriptions.
2. Include Negative Constraints: Clearly state patterns and anti-patterns to avoid.
3. Phased Execution: Break complex tasks into manageable phases.
4. Tool Version Locking: Specify exact versions of dependent tools to ensure environmental consistency.

Performance Optimization Strategies

# Performance optimization config example
performance:
  # Context management
  max_context_tokens: 4000
  compression: true
  summarize_large_outputs: true
  
  # Cache strategy
  cache:
    enabled: true
    strategy: "lru"
    max_size: 100
    ttl: 3600
    
  # Lazy load config
  lazy_load:
    resources: true
    tools: true
    instructions: false  # Core instructions load immediately
    
  # Parallel processing
  parallel:
    enabled: true
    max_workers: 3
    timeout: 30

Applicable Scenarios and Limitations

Ideal Applications