mirror of https://fastgit.cc/github.com/luongnv89/claude-howto synced 2026-04-20 21:00:14 +08:00

Files

Luong NGUYEN cf92e8e2c1 chore(release): v2.1.112

Sync all English tutorials with Claude Code v2.1.112 and the new Opus 4.7
model (claude-opus-4-7). Introduces the xhigh effort level (new default on
Opus 4.7), two new built-in slash commands (/ultrareview,
/less-permission-prompts), auto mode without --enable-auto-mode for Max
subscribers on Opus 4.7, PowerShell tool on Windows, Auto-match-terminal
theme, and plan files named after prompts. All 18 EN doc footers bumped to
Claude Code v2.1.112.

Version callouts in README.md, zh/README.md, and uk/README.md updated to
v2.1.112. Changelog entry prepended.

Repo version now tracks Claude Code version (2.3.0 → 2.1.112 is intentional
per new convention).

2026-04-16 23:13:34 +02:00

82 KiB

Raw Permalink Blame History

Complete Guide to Claude Concepts

A comprehensive reference guide covering Slash Commands, Subagents, Memory, MCP Protocol, and Agent Skills with tables, diagrams, and practical examples.

Slash Commands
Subagents
Memory
MCP Protocol
Agent Skills
Plugins
Hooks
Checkpoints and Rewind
Advanced Features
Comparison & Integration

Slash Commands

Overview

Slash commands are user-invoked shortcuts stored as Markdown files that Claude Code can execute. They enable teams to standardize frequently-used prompts and workflows.

Architecture

graph TD
    A["User Input: /command-name"] -->|Triggers| B["Search .claude/commands/"]
    B -->|Finds| C["command-name.md"]
    C -->|Loads| D["Markdown Content"]
    D -->|Executes| E["Claude Processes Prompt"]
    E -->|Returns| F["Result in Context"]

File Structure

graph LR
    A["Project Root"] -->|contains| B[".claude/commands/"]
    B -->|contains| C["optimize.md"]
    B -->|contains| D["test.md"]
    B -->|contains| E["docs/"]
    E -->|contains| F["generate-api-docs.md"]
    E -->|contains| G["generate-readme.md"]

Command Organization Table

Location	Scope	Availability	Use Case	Git Tracked
`.claude/commands/`	Project-specific	Team members	Team workflows, shared standards	✅ Yes
`~/.claude/commands/`	Personal	Individual user	Personal shortcuts across projects	❌ No
Subdirectories	Namespaced	Based on parent	Organize by category	✅ Yes

Features & Capabilities

Feature	Example	Supported
Shell script execution	`bash scripts/deploy.sh`	✅ Yes
File references	`@path/to/file.js`	✅ Yes
Bash integration	`$(git log --oneline)`	✅ Yes
Arguments	`/pr --verbose`	✅ Yes
MCP commands	`/mcp__github__list_prs`	✅ Yes

Practical Examples

Example 1: Code Optimization Command

File: .claude/commands/optimize.md

---
name: Code Optimization
description: Analyze code for performance issues and suggest optimizations
tags: performance, analysis
---

# Code Optimization

Review the provided code for the following issues in order of priority:

1. **Performance bottlenecks** - identify O(n²) operations, inefficient loops
2. **Memory leaks** - find unreleased resources, circular references
3. **Algorithm improvements** - suggest better algorithms or data structures
4. **Caching opportunities** - identify repeated computations
5. **Concurrency issues** - find race conditions or threading problems

Format your response with:
- Issue severity (Critical/High/Medium/Low)
- Location in code
- Explanation
- Recommended fix with code example

Usage:

# User types in Claude Code
/optimize

# Claude loads the prompt and waits for code input

Example 2: Pull Request Helper Command

File: .claude/commands/pr.md

---
name: Prepare Pull Request
description: Clean up code, stage changes, and prepare a pull request
tags: git, workflow
---

# Pull Request Preparation Checklist

Before creating a PR, execute these steps:

1. Run linting: `prettier --write .`
2. Run tests: `npm test`
3. Review git diff: `git diff HEAD`
4. Stage changes: `git add .`
5. Create commit message following conventional commits:
   - `fix:` for bug fixes
   - `feat:` for new features
   - `docs:` for documentation
   - `refactor:` for code restructuring
   - `test:` for test additions
   - `chore:` for maintenance

6. Generate PR summary including:
   - What changed
   - Why it changed
   - Testing performed
   - Potential impacts

Usage:

/pr

# Claude runs through checklist and prepares the PR

Example 3: Hierarchical Documentation Generator

File: .claude/commands/docs/generate-api-docs.md

---
name: Generate API Documentation
description: Create comprehensive API documentation from source code
tags: documentation, api
---

# API Documentation Generator

Generate API documentation by:

1. Scanning all files in `/src/api/`
2. Extracting function signatures and JSDoc comments
3. Organizing by endpoint/module
4. Creating markdown with examples
5. Including request/response schemas
6. Adding error documentation

Output format:
- Markdown file in `/docs/api.md`
- Include curl examples for all endpoints
- Add TypeScript types

Command Lifecycle Diagram

sequenceDiagram
    participant User
    participant Claude as Claude Code
    participant FS as File System
    participant CLI as Shell/Bash

    User->>Claude: Types /optimize
    Claude->>FS: Searches .claude/commands/
    FS-->>Claude: Returns optimize.md
    Claude->>Claude: Loads Markdown content
    Claude->>User: Displays prompt context
    User->>Claude: Provides code to analyze
    Claude->>CLI: (May execute scripts)
    CLI-->>Claude: Results
    Claude->>User: Returns analysis

Best Practices

✅ Do	❌ Don't
Use clear, action-oriented names	Create commands for one-time tasks
Document trigger words in description	Build complex logic in commands
Keep commands focused on single task	Create redundant commands
Version control project commands	Hardcode sensitive information
Organize in subdirectories	Create long lists of commands
Use simple, readable prompts	Use abbreviated or cryptic wording

Subagents

Overview

Subagents are specialized AI assistants with isolated context windows and customized system prompts. They enable delegated task execution while maintaining clean separation of concerns.

Architecture Diagram

graph TB
    User["👤 User"]
    Main["🎯 Main Agent<br/>(Coordinator)"]
    Reviewer["🔍 Code Reviewer<br/>Subagent"]
    Tester["✅ Test Engineer<br/>Subagent"]
    Docs["📝 Documentation<br/>Subagent"]

    User -->|asks| Main
    Main -->|delegates| Reviewer
    Main -->|delegates| Tester
    Main -->|delegates| Docs
    Reviewer -->|returns result| Main
    Tester -->|returns result| Main
    Docs -->|returns result| Main
    Main -->|synthesizes| User

Subagent Lifecycle

sequenceDiagram
    participant User
    participant MainAgent as Main Agent
    participant CodeReviewer as Code Reviewer<br/>Subagent
    participant Context as Separate<br/>Context Window

    User->>MainAgent: "Build new auth feature"
    MainAgent->>MainAgent: Analyze task
    MainAgent->>CodeReviewer: "Review this code"
    CodeReviewer->>Context: Initialize clean context
    Context->>CodeReviewer: Load reviewer instructions
    CodeReviewer->>CodeReviewer: Perform review
    CodeReviewer-->>MainAgent: Return findings
    MainAgent->>MainAgent: Incorporate results
    MainAgent-->>User: Provide synthesis

Subagent Configuration Table

Configuration	Type	Purpose	Example
`name`	String	Agent identifier	`code-reviewer`
`description`	String	Purpose & trigger terms	`Comprehensive code quality analysis`
`tools`	List/String	Allowed capabilities	`read, grep, diff, lint_runner`
`system_prompt`	Markdown	Behavioral instructions	Custom guidelines

Tool Access Hierarchy

graph TD
    A["Subagent Configuration"] -->|Option 1| B["Inherit All Tools<br/>from Main Thread"]
    A -->|Option 2| C["Specify Individual Tools"]
    B -->|Includes| B1["File Operations"]
    B -->|Includes| B2["Shell Commands"]
    B -->|Includes| B3["MCP Tools"]
    C -->|Explicit List| C1["read, grep, diff"]
    C -->|Explicit List| C2["Bash(npm:*), Bash(test:*)"]

Practical Examples

Example 1: Complete Subagent Setup

File: .claude/agents/code-reviewer.md

---
name: code-reviewer
description: Comprehensive code quality and maintainability analysis
tools: read, grep, diff, lint_runner
---

# Code Reviewer Agent

You are an expert code reviewer specializing in:
- Performance optimization
- Security vulnerabilities
- Code maintainability
- Testing coverage
- Design patterns

## Review Priorities (in order)

1. **Security Issues** - Authentication, authorization, data exposure
2. **Performance Problems** - O(n²) operations, memory leaks, inefficient queries
3. **Code Quality** - Readability, naming, documentation
4. **Test Coverage** - Missing tests, edge cases
5. **Design Patterns** - SOLID principles, architecture

## Review Output Format

For each issue:
- **Severity**: Critical / High / Medium / Low
- **Category**: Security / Performance / Quality / Testing / Design
- **Location**: File path and line number
- **Issue Description**: What's wrong and why
- **Suggested Fix**: Code example
- **Impact**: How this affects the system

## Example Review

### Issue: N+1 Query Problem
- **Severity**: High
- **Category**: Performance
- **Location**: src/user-service.ts:45
- **Issue**: Loop executes database query in each iteration
- **Fix**: Use JOIN or batch query

File: .claude/agents/test-engineer.md

---
name: test-engineer
description: Test strategy, coverage analysis, and automated testing
tools: read, write, bash, grep
---

# Test Engineer Agent

You are expert at:
- Writing comprehensive test suites
- Ensuring high code coverage (>80%)
- Testing edge cases and error scenarios
- Performance benchmarking
- Integration testing

## Testing Strategy

1. **Unit Tests** - Individual functions/methods
2. **Integration Tests** - Component interactions
3. **End-to-End Tests** - Complete workflows
4. **Edge Cases** - Boundary conditions
5. **Error Scenarios** - Failure handling

## Test Output Requirements

- Use Jest for JavaScript/TypeScript
- Include setup/teardown for each test
- Mock external dependencies
- Document test purpose
- Include performance assertions when relevant

## Coverage Requirements

- Minimum 80% code coverage
- 100% for critical paths
- Report missing coverage areas

File: .claude/agents/documentation-writer.md

---
name: documentation-writer
description: Technical documentation, API docs, and user guides
tools: read, write, grep
---

# Documentation Writer Agent

You create:
- API documentation with examples
- User guides and tutorials
- Architecture documentation
- Changelog entries
- Code comment improvements

## Documentation Standards

1. **Clarity** - Use simple, clear language
2. **Examples** - Include practical code examples
3. **Completeness** - Cover all parameters and returns
4. **Structure** - Use consistent formatting
5. **Accuracy** - Verify against actual code

## Documentation Sections

### For APIs
- Description
- Parameters (with types)
- Returns (with types)
- Throws (possible errors)
- Examples (curl, JavaScript, Python)
- Related endpoints

### For Features
- Overview
- Prerequisites
- Step-by-step instructions
- Expected outcomes
- Troubleshooting
- Related topics

Example 2: Subagent Delegation in Action

# Scenario: Building a Payment Feature

## User Request
"Build a secure payment processing feature that integrates with Stripe"

## Main Agent Flow

1. **Planning Phase**
   - Understands requirements
   - Determines tasks needed
   - Plans architecture

2. **Delegates to Code Reviewer Subagent**
   - Task: "Review the payment processing implementation for security"
   - Context: Auth, API keys, token handling
   - Reviews for: SQL injection, key exposure, HTTPS enforcement

3. **Delegates to Test Engineer Subagent**
   - Task: "Create comprehensive tests for payment flows"
   - Context: Success scenarios, failures, edge cases
   - Creates tests for: Valid payments, declined cards, network failures, webhooks

4. **Delegates to Documentation Writer Subagent**
   - Task: "Document the payment API endpoints"
   - Context: Request/response schemas
   - Produces: API docs with curl examples, error codes

5. **Synthesis**
   - Main agent collects all outputs
   - Integrates findings
   - Returns complete solution to user

Example 3: Tool Permission Scoping

Restrictive Setup - Limited to Specific Commands

---
name: secure-reviewer
description: Security-focused code review with minimal permissions
tools: read, grep
---

# Secure Code Reviewer

Reviews code for security vulnerabilities only.

This agent:
- ✅ Reads files to analyze
- ✅ Searches for patterns
- ❌ Cannot execute code
- ❌ Cannot modify files
- ❌ Cannot run tests

This ensures the reviewer doesn't accidentally break anything.

Extended Setup - All Tools for Implementation

---
name: implementation-agent
description: Full implementation capabilities for feature development
tools: read, write, bash, grep, edit, glob
---

# Implementation Agent

Builds features from specifications.

This agent:
- ✅ Reads specifications
- ✅ Writes new code files
- ✅ Runs build commands
- ✅ Searches codebase
- ✅ Edits existing files
- ✅ Finds files matching patterns

Full capabilities for independent feature development.

Subagent Context Management

graph TB
    A["Main Agent Context<br/>50,000 tokens"]
    B["Subagent 1 Context<br/>20,000 tokens"]
    C["Subagent 2 Context<br/>20,000 tokens"]
    D["Subagent 3 Context<br/>20,000 tokens"]

    A -->|Clean slate| B
    A -->|Clean slate| C
    A -->|Clean slate| D

    B -->|Results only| A
    C -->|Results only| A
    D -->|Results only| A

    style A fill:#e1f5ff
    style B fill:#fff9c4
    style C fill:#fff9c4
    style D fill:#fff9c4

When to Use Subagents

Scenario	Use Subagent	Why
Complex feature with many steps	✅ Yes	Separate concerns, prevent context pollution
Quick code review	❌ No	Not necessary overhead
Parallel task execution	✅ Yes	Each subagent has own context
Specialized expertise needed	✅ Yes	Custom system prompts
Long-running analysis	✅ Yes	Prevents main context exhaustion
Single task	❌ No	Adds latency unnecessarily

Agent Teams

Agent Teams coordinate multiple agents working on related tasks. Rather than delegating to one subagent at a time, Agent Teams allow the main agent to orchestrate a group of agents that collaborate, share intermediate results, and work toward a common goal. This is useful for large-scale tasks like full-stack feature development where a frontend agent, backend agent, and testing agent work in parallel.

Memory

Overview

Memory enables Claude to retain context across sessions and conversations. It exists in two forms: automatic synthesis in claude.ai, and filesystem-based CLAUDE.md in Claude Code.

Memory Architecture

graph TB
    A["Claude Session"]
    B["User Input"]
    C["Memory System"]
    D["Memory Storage"]

    B -->|User provides info| C
    C -->|Synthesizes every 24h| D
    D -->|Loads automatically| A
    A -->|Uses context| C

Memory Hierarchy in Claude Code (7 Tiers)

Claude Code loads memory from 7 tiers, listed from highest to lowest priority:

graph TD
    A["1. Managed Policy<br/>Enterprise admin policies"] --> B["2. Project Memory<br/>./CLAUDE.md"]
    B --> C["3. Project Rules<br/>.claude/rules/*.md"]
    C --> D["4. User Memory<br/>~/.claude/CLAUDE.md"]
    D --> E["5. User Rules<br/>~/.claude/rules/*.md"]
    E --> F["6. Local Memory<br/>.claude/local/CLAUDE.md"]
    F --> G["7. Auto Memory<br/>Automatically captured preferences"]

    style A fill:#fce4ec,stroke:#333,color:#333
    style B fill:#e1f5fe,stroke:#333,color:#333
    style C fill:#e1f5fe,stroke:#333,color:#333
    style D fill:#f3e5f5,stroke:#333,color:#333
    style E fill:#f3e5f5,stroke:#333,color:#333
    style F fill:#e8f5e9,stroke:#333,color:#333
    style G fill:#fff3e0,stroke:#333,color:#333

Memory Locations Table

Tier	Location	Scope	Priority	Shared	Best For
1. Managed Policy	Enterprise admin	Organization	Highest	All org users	Compliance, security policies
2. Project	`./CLAUDE.md`	Project	High	Team (Git)	Team standards, architecture
3. Project Rules	`.claude/rules/*.md`	Project	High	Team (Git)	Modular project conventions
4. User	`~/.claude/CLAUDE.md`	Personal	Medium	Individual	Personal preferences
5. User Rules	`~/.claude/rules/*.md`	Personal	Medium	Individual	Personal rule modules
6. Local	`.claude/local/CLAUDE.md`	Local	Low	Not shared	Machine-specific settings
7. Auto Memory	Automatic	Session	Lowest	Individual	Learned preferences, patterns

Auto Memory

Auto Memory automatically captures user preferences and patterns observed during sessions. Claude learns from your interactions and remembers:

Coding style preferences
Common corrections you make
Framework and tool choices
Communication style preferences

Auto Memory works in the background and does not require manual configuration.

Memory Update Lifecycle

sequenceDiagram
    participant User
    participant Claude as Claude Code
    participant Editor as File System
    participant Memory as CLAUDE.md

    User->>Claude: "Remember: use async/await"
    Claude->>User: "Which memory file?"
    User->>Claude: "Project memory"
    Claude->>Editor: Open ~/.claude/settings.json
    Claude->>Memory: Write to ./CLAUDE.md
    Memory-->>Claude: File saved
    Claude->>Claude: Load updated memory
    Claude-->>User: "Memory saved!"

Practical Examples

Example 1: Project Memory Structure

File: ./CLAUDE.md

# Project Configuration

## Project Overview
- **Name**: E-commerce Platform
- **Tech Stack**: Node.js, PostgreSQL, React 18, Docker
- **Team Size**: 5 developers
- **Deadline**: Q4 2025

## Architecture
@docs/architecture.md
@docs/api-standards.md
@docs/database-schema.md

## Development Standards

### Code Style
- Use Prettier for formatting
- Use ESLint with airbnb config
- Maximum line length: 100 characters
- Use 2-space indentation

### Naming Conventions
- **Files**: kebab-case (user-controller.js)
- **Classes**: PascalCase (UserService)
- **Functions/Variables**: camelCase (getUserById)
- **Constants**: UPPER_SNAKE_CASE (API_BASE_URL)
- **Database Tables**: snake_case (user_accounts)

### Git Workflow
- Branch names: `feature/description` or `fix/description`
- Commit messages: Follow conventional commits
- PR required before merge
- All CI/CD checks must pass
- Minimum 1 approval required

### Testing Requirements
- Minimum 80% code coverage
- All critical paths must have tests
- Use Jest for unit tests
- Use Cypress for E2E tests
- Test filenames: `*.test.ts` or `*.spec.ts`

### API Standards
- RESTful endpoints only
- JSON request/response
- Use HTTP status codes correctly
- Version API endpoints: `/api/v1/`
- Document all endpoints with examples

### Database
- Use migrations for schema changes
- Never hardcode credentials
- Use connection pooling
- Enable query logging in development
- Regular backups required

### Deployment
- Docker-based deployment
- Kubernetes orchestration
- Blue-green deployment strategy
- Automatic rollback on failure
- Database migrations run before deploy

## Common Commands

| Command | Purpose |
|---------|---------|
| `npm run dev` | Start development server |
| `npm test` | Run test suite |
| `npm run lint` | Check code style |
| `npm run build` | Build for production |
| `npm run migrate` | Run database migrations |

## Team Contacts
- Tech Lead: Sarah Chen (@sarah.chen)
- Product Manager: Mike Johnson (@mike.j)
- DevOps: Alex Kim (@alex.k)

## Known Issues & Workarounds
- PostgreSQL connection pooling limited to 20 during peak hours
- Workaround: Implement query queuing
- Safari 14 compatibility issues with async generators
- Workaround: Use Babel transpiler

## Related Projects
- Analytics Dashboard: `/projects/analytics`
- Mobile App: `/projects/mobile`
- Admin Panel: `/projects/admin`

Example 2: Directory-Specific Memory

File: ./src/api/CLAUDE.md

# API Module Standards

This file overrides root CLAUDE.md for everything in /src/api/

## API-Specific Standards

### Request Validation
- Use Zod for schema validation
- Always validate input
- Return 400 with validation errors
- Include field-level error details

### Authentication
- All endpoints require JWT token
- Token in Authorization header
- Token expires after 24 hours
- Implement refresh token mechanism

### Response Format

All responses must follow this structure:

```json
{
  "success": true,
  "data": { /* actual data */ },
  "timestamp": "2025-11-06T10:30:00Z",
  "version": "1.0"
}
```

### Error responses:
```json
{
  "success": false,
  "error": {
    "code": "VALIDATION_ERROR",
    "message": "User message",
    "details": { /* field errors */ }
  },
  "timestamp": "2025-11-06T10:30:00Z"
}
```

### Pagination
- Use cursor-based pagination (not offset)
- Include `hasMore` boolean
- Limit max page size to 100
- Default page size: 20

### Rate Limiting
- 1000 requests per hour for authenticated users
- 100 requests per hour for public endpoints
- Return 429 when exceeded
- Include retry-after header

### Caching
- Use Redis for session caching
- Cache duration: 5 minutes default
- Invalidate on write operations
- Tag cache keys with resource type

Example 3: Personal Memory

File: ~/.claude/CLAUDE.md

# My Development Preferences

## About Me
- **Experience Level**: 8 years full-stack development
- **Preferred Languages**: TypeScript, Python
- **Communication Style**: Direct, with examples
- **Learning Style**: Visual diagrams with code

## Code Preferences

### Error Handling
I prefer explicit error handling with try-catch blocks and meaningful error messages.
Avoid generic errors. Always log errors for debugging.

### Comments
Use comments for WHY, not WHAT. Code should be self-documenting.
Comments should explain business logic or non-obvious decisions.

### Testing
I prefer TDD (test-driven development).
Write tests first, then implementation.
Focus on behavior, not implementation details.

### Architecture
I prefer modular, loosely-coupled design.
Use dependency injection for testability.
Separate concerns (Controllers, Services, Repositories).

## Debugging Preferences
- Use console.log with prefix: `[DEBUG]`
- Include context: function name, relevant variables
- Use stack traces when available
- Always include timestamps in logs

## Communication
- Explain complex concepts with diagrams
- Show concrete examples before explaining theory
- Include before/after code snippets
- Summarize key points at the end

## Project Organization
I organize my projects as:
```
project/
  ├── src/
  │   ├── api/
  │   ├── services/
  │   ├── models/
  │   └── utils/
  ├── tests/
  ├── docs/
  └── docker/
```

## Tooling
- **IDE**: VS Code with vim keybindings
- **Terminal**: Zsh with Oh-My-Zsh
- **Format**: Prettier (100 char line length)
- **Linter**: ESLint with airbnb config
- **Test Framework**: Jest with React Testing Library

Example 4: Memory Update During Session

Session Interaction:

User: Remember that I prefer using React hooks instead of class components
     for all new components.

Claude: I'm adding that to your memory. Which memory file should this go in?
        1. Project memory (./CLAUDE.md)
        2. Personal memory (~/.claude/CLAUDE.md)

User: Project memory

Claude: ✅ Memory saved!

Added to ./CLAUDE.md:
---

### Component Development
- Use functional components with React Hooks
- Prefer hooks over class components
- Custom hooks for reusable logic
- Use useCallback for event handlers
- Use useMemo for expensive computations

Memory in Claude Web/Desktop

Memory Synthesis Timeline

graph LR
    A["Day 1: User<br/>Conversations"] -->|24 hours| B["Day 2: Memory<br/>Synthesis"]
    B -->|Automatic| C["Memory Updated<br/>Summarized"]
    C -->|Loaded in| D["Day 2-N:<br/>New Conversations"]
    D -->|Add to| E["Memory"]
    E -->|24 hours later| F["Memory Refreshed"]

Example Memory Summary:

## Claude's Memory of User

### Professional Background
- Senior full-stack developer with 8 years experience
- Focus on TypeScript/Node.js backends and React frontends
- Active open source contributor
- Interested in AI and machine learning

### Project Context
- Currently building e-commerce platform
- Tech stack: Node.js, PostgreSQL, React 18, Docker
- Working with team of 5 developers
- Using CI/CD and blue-green deployments

### Communication Preferences
- Prefers direct, concise explanations
- Likes visual diagrams and examples
- Appreciates code snippets
- Explains business logic in comments

### Current Goals
- Improve API performance
- Increase test coverage to 90%
- Implement caching strategy
- Document architecture

Memory Features Comparison

Feature	Claude Web/Desktop	Claude Code (CLAUDE.md)
Auto-synthesis	✅ Every 24h	❌ Manual
Cross-project	✅ Shared	❌ Project-specific
Team access	✅ Shared projects	✅ Git-tracked
Searchable	✅ Built-in	✅ Through `/memory`
Editable	✅ In-chat	✅ Direct file edit
Import/Export	✅ Yes	✅ Copy/paste
Persistent	✅ 24h+	✅ Indefinite

MCP Protocol

Overview

MCP (Model Context Protocol) is a standardized way for Claude to access external tools, APIs, and real-time data sources. Unlike Memory, MCP provides live access to changing data.

MCP Architecture

graph TB
    A["Claude"]
    B["MCP Server"]
    C["External Service"]

    A -->|Request: list_issues| B
    B -->|Query| C
    C -->|Data| B
    B -->|Response| A

    A -->|Request: create_issue| B
    B -->|Action| C
    C -->|Result| B
    B -->|Response| A

MCP Ecosystem

graph TB
    A["Claude"] -->|MCP| B["Filesystem<br/>MCP Server"]
    A -->|MCP| C["GitHub<br/>MCP Server"]
    A -->|MCP| D["Database<br/>MCP Server"]
    A -->|MCP| E["Slack<br/>MCP Server"]
    A -->|MCP| F["Google Docs<br/>MCP Server"]

    B -->|File I/O| G["Local Files"]
    C -->|API| H["GitHub Repos"]
    D -->|Query| I["PostgreSQL/MySQL"]
    E -->|Messages| J["Slack Workspace"]
    F -->|Docs| K["Google Drive"]

MCP Setup Process

sequenceDiagram
    participant User
    participant Claude as Claude Code
    participant Config as Config File
    participant Service as External Service

    User->>Claude: Type /mcp
    Claude->>Claude: List available MCP servers
    Claude->>User: Show options
    User->>Claude: Select GitHub MCP
    Claude->>Config: Update configuration
    Config->>Claude: Activate connection
    Claude->>Service: Test connection
    Service-->>Claude: Authentication successful
    Claude->>User: ✅ MCP connected!

Available MCP Servers Table

MCP Server	Purpose	Common Tools	Auth	Real-time
Filesystem	File operations	read, write, delete	OS permissions	✅ Yes
GitHub	Repository management	list_prs, create_issue, push	OAuth	✅ Yes
Slack	Team communication	send_message, list_channels	Token	✅ Yes
Database	SQL queries	query, insert, update	Credentials	✅ Yes
Google Docs	Document access	read, write, share	OAuth	✅ Yes
Asana	Project management	create_task, update_status	API Key	✅ Yes
Stripe	Payment data	list_charges, create_invoice	API Key	✅ Yes
Memory	Persistent memory	store, retrieve, delete	Local	❌ No

Practical Examples

Example 1: GitHub MCP Configuration

File: .mcp.json (project scope) or ~/.claude.json (user scope)

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_TOKEN": "${GITHUB_TOKEN}"
      }
    }
  }
}

Available GitHub MCP Tools:

# GitHub MCP Tools

## Pull Request Management
- `list_prs` - List all PRs in repository
- `get_pr` - Get PR details including diff
- `create_pr` - Create new PR
- `update_pr` - Update PR description/title
- `merge_pr` - Merge PR to main branch
- `review_pr` - Add review comments

Example request:
```
/mcp__github__get_pr 456

# Returns:
Title: Add dark mode support
Author: @alice
Description: Implements dark theme using CSS variables
Status: OPEN
Reviewers: @bob, @charlie
```

## Issue Management
- `list_issues` - List all issues
- `get_issue` - Get issue details
- `create_issue` - Create new issue
- `close_issue` - Close issue
- `add_comment` - Add comment to issue

## Repository Information
- `get_repo_info` - Repository details
- `list_files` - File tree structure
- `get_file_content` - Read file contents
- `search_code` - Search across codebase

## Commit Operations
- `list_commits` - Commit history
- `get_commit` - Specific commit details
- `create_commit` - Create new commit

Example 2: Database MCP Setup

Configuration:

{
  "mcpServers": {
    "database": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-database"],
      "env": {
        "DATABASE_URL": "postgresql://user:pass@localhost/mydb"
      }
    }
  }
}

Example Usage:

User: Fetch all users with more than 10 orders

Claude: I'll query your database to find that information.

# Using MCP database tool:
SELECT u.*, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
GROUP BY u.id
HAVING COUNT(o.id) > 10
ORDER BY order_count DESC;

# Results:
- Alice: 15 orders
- Bob: 12 orders
- Charlie: 11 orders

Example 3: Multi-MCP Workflow

Scenario: Daily Report Generation

# Daily Report Workflow using Multiple MCPs

## Setup
1. GitHub MCP - fetch PR metrics
2. Database MCP - query sales data
3. Slack MCP - post report
4. Filesystem MCP - save report

## Workflow

### Step 1: Fetch GitHub Data
/mcp__github__list_prs completed:true last:7days

Output:
- Total PRs: 42
- Average merge time: 2.3 hours
- Review turnaround: 1.1 hours

### Step 2: Query Database
SELECT COUNT(*) as sales, SUM(amount) as revenue
FROM orders
WHERE created_at > NOW() - INTERVAL '1 day'

Output:
- Sales: 247
- Revenue: $12,450

### Step 3: Generate Report
Combine data into HTML report

### Step 4: Save to Filesystem
Write report.html to /reports/

### Step 5: Post to Slack
Send summary to #daily-reports channel

Final Output:
✅ Report generated and posted
📊 47 PRs merged this week
💰 $12,450 in daily sales

Example 4: Filesystem MCP Operations

Configuration:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["@modelcontextprotocol/server-filesystem", "/home/user/projects"]
    }
  }
}

Available Operations:

Operation	Command	Purpose
List files	`ls ~/projects`	Show directory contents
Read file	`cat src/main.ts`	Read file contents
Write file	`create docs/api.md`	Create new file
Edit file	`edit src/app.ts`	Modify file
Search	`grep "async function"`	Search in files
Delete	`rm old-file.js`	Delete file

MCP vs Memory: Decision Matrix

graph TD
    A["Need external data?"]
    A -->|No| B["Use Memory"]
    A -->|Yes| C["Does it change frequently?"]
    C -->|No/Rarely| B
    C -->|Yes/Often| D["Use MCP"]

    B -->|Stores| E["Preferences<br/>Context<br/>History"]
    D -->|Accesses| F["Live APIs<br/>Databases<br/>Services"]

    style B fill:#e1f5ff
    style D fill:#fff9c4

Request/Response Pattern

sequenceDiagram
    participant App as Claude
    participant MCP as MCP Server
    participant DB as Database

    App->>MCP: Request: "SELECT * FROM users WHERE id=1"
    MCP->>DB: Execute query
    DB-->>MCP: Result set
    MCP-->>App: Return parsed data
    App->>App: Process result
    App->>App: Continue task

    Note over MCP,DB: Real-time access<br/>No caching

Agent Skills

Overview

Agent Skills are reusable, model-invoked capabilities packaged as folders containing instructions, scripts, and resources. Claude automatically detects and uses relevant skills.

Skill Architecture

graph TB
    A["Skill Directory"]
    B["SKILL.md"]
    C["YAML Metadata"]
    D["Instructions"]
    E["Scripts"]
    F["Templates"]

    A --> B
    B --> C
    B --> D
    E --> A
    F --> A

Skill Loading Process

sequenceDiagram
    participant User
    participant Claude as Claude
    participant System as System
    participant Skill as Skill

    User->>Claude: "Create Excel report"
    Claude->>System: Scan available skills
    System->>System: Load skill metadata
    Claude->>Claude: Match user request to skills
    Claude->>Skill: Load xlsx skill SKILL.md
    Skill-->>Claude: Return instructions + tools
    Claude->>Claude: Execute skill
    Claude->>User: Generate Excel file

Skill Types & Locations Table

Type	Location	Scope	Shared	Sync	Best For
Pre-built	Built-in	Global	All users	Auto	Document creation
Personal	`~/.claude/skills/`	Individual	No	Manual	Personal automation
Project	`.claude/skills/`	Team	Yes	Git	Team standards
Plugin	Via plugin install	Varies	Depends	Auto	Integrated features

Pre-built Skills

graph TB
    A["Pre-built Skills"]
    B["PowerPoint (pptx)"]
    C["Excel (xlsx)"]
    D["Word (docx)"]
    E["PDF"]

    A --> B
    A --> C
    A --> D
    A --> E

    B --> B1["Create presentations"]
    B --> B2["Edit slides"]
    C --> C1["Create spreadsheets"]
    C --> C2["Analyze data"]
    D --> D1["Create documents"]
    D --> D2["Format text"]
    E --> E1["Generate PDFs"]
    E --> E2["Fill forms"]

Bundled Skills

Claude Code now includes 5 bundled skills available out of the box:

Skill	Command	Purpose
Simplify	`/simplify`	Simplify complex code or explanations
Batch	`/batch`	Run operations across multiple files or items
Debug	`/debug`	Systematic debugging of issues with root cause analysis
Loop	`/loop`	Schedule recurring tasks on a timer
Claude API	`/claude-api`	Interact with the Anthropic API directly

These bundled skills are always available and do not require installation or configuration.

Practical Examples

Example 1: Custom Code Review Skill

Directory Structure:

~/.claude/skills/code-review/
├── SKILL.md
├── templates/
│   ├── review-checklist.md
│   └── finding-template.md
└── scripts/
    ├── analyze-metrics.py
    └── compare-complexity.py

File: ~/.claude/skills/code-review/SKILL.md

---
name: Code Review Specialist
description: Comprehensive code review with security, performance, and quality analysis
version: "1.0.0"
tags:
  - code-review
  - quality
  - security
when_to_use: When users ask to review code, analyze code quality, or evaluate pull requests
effort: high
shell: bash
---

# Code Review Skill

This skill provides comprehensive code review capabilities focusing on:

1. **Security Analysis**
   - Authentication/authorization issues
   - Data exposure risks
   - Injection vulnerabilities
   - Cryptographic weaknesses
   - Sensitive data logging

2. **Performance Review**
   - Algorithm efficiency (Big O analysis)
   - Memory optimization
   - Database query optimization
   - Caching opportunities
   - Concurrency issues

3. **Code Quality**
   - SOLID principles
   - Design patterns
   - Naming conventions
   - Documentation
   - Test coverage

4. **Maintainability**
   - Code readability
   - Function size (should be < 50 lines)
   - Cyclomatic complexity
   - Dependency management
   - Type safety

## Review Template

For each piece of code reviewed, provide:

### Summary
- Overall quality assessment (1-5)
- Key findings count
- Recommended priority areas

### Critical Issues (if any)
- **Issue**: Clear description
- **Location**: File and line number
- **Impact**: Why this matters
- **Severity**: Critical/High/Medium
- **Fix**: Code example

### Findings by Category

#### Security (if issues found)
List security vulnerabilities with examples

#### Performance (if issues found)
List performance problems with complexity analysis

#### Quality (if issues found)
List code quality issues with refactoring suggestions

#### Maintainability (if issues found)
List maintainability problems with improvements

Python Script: analyze-metrics.py

#!/usr/bin/env python3
import re
import sys

def analyze_code_metrics(code):
    """Analyze code for common metrics."""

    # Count functions
    functions = len(re.findall(r'^def\s+\w+', code, re.MULTILINE))

    # Count classes
    classes = len(re.findall(r'^class\s+\w+', code, re.MULTILINE))

    # Average line length
    lines = code.split('\n')
    avg_length = sum(len(l) for l in lines) / len(lines) if lines else 0

    # Estimate complexity
    complexity = len(re.findall(r'\b(if|elif|else|for|while|and|or)\b', code))

    return {
        'functions': functions,
        'classes': classes,
        'avg_line_length': avg_length,
        'complexity_score': complexity
    }

if __name__ == '__main__':
    with open(sys.argv[1], 'r') as f:
        code = f.read()
    metrics = analyze_code_metrics(code)
    for key, value in metrics.items():
        print(f"{key}: {value:.2f}")

Python Script: compare-complexity.py

#!/usr/bin/env python3
"""
Compare cyclomatic complexity of code before and after changes.
Helps identify if refactoring actually simplifies code structure.
"""

import re
import sys
from typing import Dict, Tuple

class ComplexityAnalyzer:
    """Analyze code complexity metrics."""

    def __init__(self, code: str):
        self.code = code
        self.lines = code.split('\n')

    def calculate_cyclomatic_complexity(self) -> int:
        """
        Calculate cyclomatic complexity using McCabe's method.
        Count decision points: if, elif, else, for, while, except, and, or
        """
        complexity = 1  # Base complexity

        # Count decision points
        decision_patterns = [
            r'\bif\b',
            r'\belif\b',
            r'\bfor\b',
            r'\bwhile\b',
            r'\bexcept\b',
            r'\band\b(?!$)',
            r'\bor\b(?!$)'
        ]

        for pattern in decision_patterns:
            matches = re.findall(pattern, self.code)
            complexity += len(matches)

        return complexity

    def calculate_cognitive_complexity(self) -> int:
        """
        Calculate cognitive complexity - how hard is it to understand?
        Based on nesting depth and control flow.
        """
        cognitive = 0
        nesting_depth = 0

        for line in self.lines:
            # Track nesting depth
            if re.search(r'^\s*(if|for|while|def|class|try)\b', line):
                nesting_depth += 1
                cognitive += nesting_depth
            elif re.search(r'^\s*(elif|else|except|finally)\b', line):
                cognitive += nesting_depth

            # Reduce nesting when unindenting
            if line and not line[0].isspace():
                nesting_depth = 0

        return cognitive

    def calculate_maintainability_index(self) -> float:
        """
        Maintainability Index ranges from 0-100.
        > 85: Excellent
        > 65: Good
        > 50: Fair
        < 50: Poor
        """
        lines = len(self.lines)
        cyclomatic = self.calculate_cyclomatic_complexity()
        cognitive = self.calculate_cognitive_complexity()

        # Simplified MI calculation
        mi = 171 - 5.2 * (cyclomatic / lines) - 0.23 * (cognitive) - 16.2 * (lines / 1000)

        return max(0, min(100, mi))

    def get_complexity_report(self) -> Dict:
        """Generate comprehensive complexity report."""
        return {
            'cyclomatic_complexity': self.calculate_cyclomatic_complexity(),
            'cognitive_complexity': self.calculate_cognitive_complexity(),
            'maintainability_index': round(self.calculate_maintainability_index(), 2),
            'lines_of_code': len(self.lines),
            'avg_line_length': round(sum(len(l) for l in self.lines) / len(self.lines), 2) if self.lines else 0
        }


def compare_files(before_file: str, after_file: str) -> None:
    """Compare complexity metrics between two code versions."""

    with open(before_file, 'r') as f:
        before_code = f.read()

    with open(after_file, 'r') as f:
        after_code = f.read()

    before_analyzer = ComplexityAnalyzer(before_code)
    after_analyzer = ComplexityAnalyzer(after_code)

    before_metrics = before_analyzer.get_complexity_report()
    after_metrics = after_analyzer.get_complexity_report()

    print("=" * 60)
    print("CODE COMPLEXITY COMPARISON")
    print("=" * 60)

    print("\nBEFORE:")
    print(f"  Cyclomatic Complexity:    {before_metrics['cyclomatic_complexity']}")
    print(f"  Cognitive Complexity:     {before_metrics['cognitive_complexity']}")
    print(f"  Maintainability Index:    {before_metrics['maintainability_index']}")
    print(f"  Lines of Code:            {before_metrics['lines_of_code']}")
    print(f"  Avg Line Length:          {before_metrics['avg_line_length']}")

    print("\nAFTER:")
    print(f"  Cyclomatic Complexity:    {after_metrics['cyclomatic_complexity']}")
    print(f"  Cognitive Complexity:     {after_metrics['cognitive_complexity']}")
    print(f"  Maintainability Index:    {after_metrics['maintainability_index']}")
    print(f"  Lines of Code:            {after_metrics['lines_of_code']}")
    print(f"  Avg Line Length:          {after_metrics['avg_line_length']}")

    print("\nCHANGES:")
    cyclomatic_change = after_metrics['cyclomatic_complexity'] - before_metrics['cyclomatic_complexity']
    cognitive_change = after_metrics['cognitive_complexity'] - before_metrics['cognitive_complexity']
    mi_change = after_metrics['maintainability_index'] - before_metrics['maintainability_index']
    loc_change = after_metrics['lines_of_code'] - before_metrics['lines_of_code']

    print(f"  Cyclomatic Complexity:    {cyclomatic_change:+d}")
    print(f"  Cognitive Complexity:     {cognitive_change:+d}")
    print(f"  Maintainability Index:    {mi_change:+.2f}")
    print(f"  Lines of Code:            {loc_change:+d}")

    print("\nASSESSMENT:")
    if mi_change > 0:
        print("  ✅ Code is MORE maintainable")
    elif mi_change < 0:
        print("  ⚠️  Code is LESS maintainable")
    else:
        print("  ➡️  Maintainability unchanged")

    if cyclomatic_change < 0:
        print("  ✅ Complexity DECREASED")
    elif cyclomatic_change > 0:
        print("  ⚠️  Complexity INCREASED")
    else:
        print("  ➡️  Complexity unchanged")

    print("=" * 60)


if __name__ == '__main__':
    if len(sys.argv) != 3:
        print("Usage: python compare-complexity.py <before_file> <after_file>")
        sys.exit(1)

    compare_files(sys.argv[1], sys.argv[2])

Template: review-checklist.md

# Code Review Checklist

## Security Checklist
- [ ] No hardcoded credentials or secrets
- [ ] Input validation on all user inputs
- [ ] SQL injection prevention (parameterized queries)
- [ ] CSRF protection on state-changing operations
- [ ] XSS prevention with proper escaping
- [ ] Authentication checks on protected endpoints
- [ ] Authorization checks on resources
- [ ] Secure password hashing (bcrypt, argon2)
- [ ] No sensitive data in logs
- [ ] HTTPS enforced

## Performance Checklist
- [ ] No N+1 queries
- [ ] Appropriate use of indexes
- [ ] Caching implemented where beneficial
- [ ] No blocking operations on main thread
- [ ] Async/await used correctly
- [ ] Large datasets paginated
- [ ] Database connections pooled
- [ ] Regular expressions optimized
- [ ] No unnecessary object creation
- [ ] Memory leaks prevented

## Quality Checklist
- [ ] Functions < 50 lines
- [ ] Clear variable naming
- [ ] No duplicate code
- [ ] Proper error handling
- [ ] Comments explain WHY, not WHAT
- [ ] No console.logs in production
- [ ] Type checking (TypeScript/JSDoc)
- [ ] SOLID principles followed
- [ ] Design patterns applied correctly
- [ ] Self-documenting code

## Testing Checklist
- [ ] Unit tests written
- [ ] Edge cases covered
- [ ] Error scenarios tested
- [ ] Integration tests present
- [ ] Coverage > 80%
- [ ] No flaky tests
- [ ] Mock external dependencies
- [ ] Clear test names

Template: finding-template.md

# Code Review Finding Template

Use this template when documenting each issue found during code review.

---

## Issue: [TITLE]

### Severity
- [ ] Critical (blocks deployment)
- [ ] High (should fix before merge)
- [ ] Medium (should fix soon)
- [ ] Low (nice to have)

### Category
- [ ] Security
- [ ] Performance
- [ ] Code Quality
- [ ] Maintainability
- [ ] Testing
- [ ] Design Pattern
- [ ] Documentation

### Location
**File:** `src/components/UserCard.tsx`

**Lines:** 45-52

**Function/Method:** `renderUserDetails()`

### Issue Description

**What:** Describe what the issue is.

**Why it matters:** Explain the impact and why this needs to be fixed.

**Current behavior:** Show the problematic code or behavior.

**Expected behavior:** Describe what should happen instead.

### Code Example

#### Current (Problematic)

```typescript
// Shows the N+1 query problem
const users = fetchUsers();
users.forEach(user => {
  const posts = fetchUserPosts(user.id); // Query per user!
  renderUserPosts(posts);
});
```

#### Suggested Fix

```typescript
// Optimized with JOIN query
const usersWithPosts = fetchUsersWithPosts();
usersWithPosts.forEach(({ user, posts }) => {
  renderUserPosts(posts);
});
```

### Impact Analysis

| Aspect | Impact | Severity |
|--------|--------|----------|
| Performance | 100+ queries for 20 users | High |
| User Experience | Slow page load | High |
| Scalability | Breaks at scale | Critical |
| Maintainability | Hard to debug | Medium |

### Related Issues

- Similar issue in `AdminUserList.tsx` line 120
- Related PR: #456
- Related issue: #789

### Additional Resources

- [N+1 Query Problem](https://en.wikipedia.org/wiki/N%2B1_problem)
- [Database Join Documentation](https://docs.example.com/joins)

### Reviewer Notes

- This is a common pattern in this codebase
- Consider adding this to the code style guide
- Might be worth creating a helper function

### Author Response (for feedback)

*To be filled by the code author:*

- [ ] Fix implemented in commit: `abc123`
- [ ] Fix status: Complete / In Progress / Needs Discussion
- [ ] Questions or concerns: (describe)

---

## Finding Statistics (for Reviewer)

When reviewing multiple findings, track:

- **Total Issues Found:** X
- **Critical:** X
- **High:** X
- **Medium:** X
- **Low:** X

**Recommendation:** ✅ Approve / ⚠️ Request Changes / 🔄 Needs Discussion

**Overall Code Quality:** 1-5 stars

Usage Example

User: Review this React component code

Claude: I'll conduct a comprehensive code review using my Code Review Skill.

[Loads SKILL.md]
[Analyzes code against checklist]
[Runs Python metrics script]
[Applies templates]

## Code Review Results

### Summary
Quality Score: 3.5/5
Key Findings: 7 items
Priority Areas: Performance optimization, error handling

### Critical Issues: 1

**Issue**: N+1 Query Problem
**Location**: UserComponent.tsx, lines 45-52
**Impact**: Renders 100+ database queries for list of 20 users
**Severity**: High
**Fix**:
```javascript
// Before: N+1 queries
const users = fetchUsers();
users.forEach(user => fetchUserPosts(user.id)); // 20+ queries

// After: Single query with JOIN
const users = fetchUsersWithPosts(); // 1 query
```

### Performance Findings
- Missing pagination on large lists
- Recommendation: Use React.memo() for items
- Database query: Could be optimized with index

### Quality Findings
- Function in line 20 is 127 lines (max: 50)
- Missing error boundary
- Props should have TypeScript types

Example 2: Brand Voice Skill

Directory Structure:

.claude/skills/brand-voice/
├── SKILL.md
├── brand-guidelines.md
├── tone-examples.md
└── templates/
    ├── email-template.txt
    ├── social-post-template.txt
    └── blog-post-template.md

File: .claude/skills/brand-voice/SKILL.md

---
name: Brand Voice Consistency
description: Ensure all communication matches brand voice and tone guidelines
tags:
  - brand
  - writing
  - consistency
when_to_use: When creating marketing copy, customer communications, or public-facing content
---

# Brand Voice Skill

## Overview
This skill ensures all communications maintain consistent brand voice, tone, and messaging.

## Brand Identity

### Mission
Help teams automate their development workflows with AI

### Values
- **Simplicity**: Make complex things simple
- **Reliability**: Rock-solid execution
- **Empowerment**: Enable human creativity

### Tone of Voice
- **Friendly but professional** - approachable without being casual
- **Clear and concise** - avoid jargon, explain technical concepts simply
- **Confident** - we know what we're doing
- **Empathetic** - understand user needs and pain points

## Writing Guidelines

### Do's ✅
- Use "you" when addressing readers
- Use active voice: "Claude generates reports" not "Reports are generated by Claude"
- Start with value proposition
- Use concrete examples
- Keep sentences under 20 words
- Use lists for clarity
- Include calls-to-action

### Don'ts ❌
- Don't use corporate jargon
- Don't patronize or oversimplify
- Don't use "we believe" or "we think"
- Don't use ALL CAPS except for emphasis
- Don't create walls of text
- Don't assume technical knowledge

## Vocabulary

### ✅ Preferred Terms
- Claude (not "the Claude AI")
- Code generation (not "auto-coding")
- Agent (not "bot")
- Streamline (not "revolutionize")
- Integrate (not "synergize")

### ❌ Avoid Terms
- "Cutting-edge" (overused)
- "Game-changer" (vague)
- "Leverage" (corporate-speak)
- "Utilize" (use "use")
- "Paradigm shift" (unclear)

Examples

✅ Good Example

"Claude automates your code review process. Instead of manually checking each PR, Claude reviews security, performance, and quality—saving your team hours every week."

Why it works: Clear value, specific benefits, action-oriented

❌ Bad Example

"Claude leverages cutting-edge AI to provide comprehensive software development solutions."

Why it doesn't work: Vague, corporate jargon, no specific value

Template: Email

Subject: [Clear, benefit-driven subject]

Hi [Name],

[Opening: What's the value for them]

[Body: How it works / What they'll get]

[Specific example or benefit]

[Call to action: Clear next step]

Best regards,
[Name]

[Hook: Grab attention in first line]
[2-3 lines: Value or interesting fact]
[Call to action: Link, question, or engagement]
[Emoji: 1-2 max for visual interest]

File: tone-examples.md

Exciting announcement:
"Save 8 hours per week on code reviews. Claude reviews your PRs automatically."

Empathetic support:
"We know deployments can be stressful. Claude handles testing so you don't have to worry."

Confident product feature:
"Claude doesn't just suggest code. It understands your architecture and maintains consistency."

Educational blog post:
"Let's explore how agents improve code review workflows. Here's what we learned..."

Example 3: Documentation Generator Skill

File: .claude/skills/doc-generator/SKILL.md

---
name: API Documentation Generator
description: Generate comprehensive, accurate API documentation from source code
version: "1.0.0"
tags:
  - documentation
  - api
  - automation
when_to_use: When creating or updating API documentation
---

# API Documentation Generator Skill

## Generates

- OpenAPI/Swagger specifications
- API endpoint documentation
- SDK usage examples
- Integration guides
- Error code references
- Authentication guides

## Documentation Structure

### For Each Endpoint

```markdown
## GET /api/v1/users/:id

### Description
Brief explanation of what this endpoint does

### Parameters

| Name | Type | Required | Description |
|------|------|----------|-------------|
| id | string | Yes | User ID |

### Response

**200 Success**
```json
{
  "id": "usr_123",
  "name": "John Doe",
  "email": "john@example.com",
  "created_at": "2025-01-15T10:30:00Z"
}
```

**404 Not Found**
```json
{
  "error": "USER_NOT_FOUND",
  "message": "User does not exist"
}
```

### Examples

**cURL**
```bash
curl -X GET "https://api.example.com/api/v1/users/usr_123" \
  -H "Authorization: Bearer YOUR_TOKEN"
```

**JavaScript**
```javascript
const user = await fetch('/api/v1/users/usr_123', {
  headers: { 'Authorization': 'Bearer token' }
}).then(r => r.json());
```

**Python**
```python
response = requests.get(
    'https://api.example.com/api/v1/users/usr_123',
    headers={'Authorization': 'Bearer token'}
)
user = response.json()
```

## Python Script: generate-docs.py

```python
#!/usr/bin/env python3
import ast
import json
from typing import Dict, List

class APIDocExtractor(ast.NodeVisitor):
    """Extract API documentation from Python source code."""

    def __init__(self):
        self.endpoints = []

    def visit_FunctionDef(self, node):
        """Extract function documentation."""
        if node.name.startswith('get_') or node.name.startswith('post_'):
            doc = ast.get_docstring(node)
            endpoint = {
                'name': node.name,
                'docstring': doc,
                'params': [arg.arg for arg in node.args.args],
                'returns': self._extract_return_type(node)
            }
            self.endpoints.append(endpoint)
        self.generic_visit(node)

    def _extract_return_type(self, node):
        """Extract return type from function annotation."""
        if node.returns:
            return ast.unparse(node.returns)
        return "Any"

def generate_markdown_docs(endpoints: List[Dict]) -> str:
    """Generate markdown documentation from endpoints."""
    docs = "# API Documentation\n\n"

    for endpoint in endpoints:
        docs += f"## {endpoint['name']}\n\n"
        docs += f"{endpoint['docstring']}\n\n"
        docs += f"**Parameters**: {', '.join(endpoint['params'])}\n\n"
        docs += f"**Returns**: {endpoint['returns']}\n\n"
        docs += "---\n\n"

    return docs

if __name__ == '__main__':
    import sys
    with open(sys.argv[1], 'r') as f:
        tree = ast.parse(f.read())

    extractor = APIDocExtractor()
    extractor.visit(tree)

    markdown = generate_markdown_docs(extractor.endpoints)
    print(markdown)

Skill Discovery & Invocation

graph TD
    A["User Request"] --> B["Claude Analyzes"]
    B -->|Scans| C["Available Skills"]
    C -->|Metadata check| D["Skill Description Match?"]
    D -->|Yes| E["Load SKILL.md"]
    D -->|No| F["Try next skill"]
    F -->|More skills?| D
    F -->|No more| G["Use general knowledge"]
    E --> H["Extract Instructions"]
    H --> I["Execute Skill"]
    I --> J["Return Results"]

Skill vs Other Features

graph TB
    A["Extending Claude"]
    B["Slash Commands"]
    C["Subagents"]
    D["Memory"]
    E["MCP"]
    F["Skills"]

    A --> B
    A --> C
    A --> D
    A --> E
    A --> F

    B -->|User-invoked| G["Quick shortcuts"]
    C -->|Auto-delegated| H["Isolated contexts"]
    D -->|Persistent| I["Cross-session context"]
    E -->|Real-time| J["External data access"]
    F -->|Auto-invoked| K["Autonomous execution"]

Claude Code Plugins

Overview

Claude Code Plugins are bundled collections of customizations (slash commands, subagents, MCP servers, and hooks) that install with a single command. They represent the highest-level extension mechanism—combining multiple features into cohesive, shareable packages.

Architecture

graph TB
    A["Plugin"]
    B["Slash Commands"]
    C["Subagents"]
    D["MCP Servers"]
    E["Hooks"]
    F["Configuration"]

    A -->|bundles| B
    A -->|bundles| C
    A -->|bundles| D
    A -->|bundles| E
    A -->|bundles| F

Plugin Loading Process

sequenceDiagram
    participant User
    participant Claude as Claude Code
    participant Plugin as Plugin Marketplace
    participant Install as Installation
    participant SlashCmds as Slash Commands
    participant Subagents
    participant MCPServers as MCP Servers
    participant Hooks
    participant Tools as Configured Tools

    User->>Claude: /plugin install pr-review
    Claude->>Plugin: Download plugin manifest
    Plugin-->>Claude: Return plugin definition
    Claude->>Install: Extract components
    Install->>SlashCmds: Configure
    Install->>Subagents: Configure
    Install->>MCPServers: Configure
    Install->>Hooks: Configure
    SlashCmds-->>Tools: Ready to use
    Subagents-->>Tools: Ready to use
    MCPServers-->>Tools: Ready to use
    Hooks-->>Tools: Ready to use
    Tools-->>Claude: Plugin installed ✅

Plugin Types & Distribution

Type	Scope	Shared	Authority	Examples
Official	Global	All users	Anthropic	PR Review, Security Guidance
Community	Public	All users	Community	DevOps, Data Science
Organization	Internal	Team members	Company	Internal standards, tools
Personal	Individual	Single user	Developer	Custom workflows

Plugin Definition Structure

---
name: plugin-name
version: "1.0.0"
description: "What this plugin does"
author: "Your Name"
license: MIT

# Plugin metadata
tags:
  - category
  - use-case

# Requirements
requires:
  - claude-code: ">=1.0.0"

# Components bundled
components:
  - type: commands
    path: commands/
  - type: agents
    path: agents/
  - type: mcp
    path: mcp/
  - type: hooks
    path: hooks/

# Configuration
config:
  auto_load: true
  enabled_by_default: true
---

Plugin Structure

my-plugin/
├── .claude-plugin/
│   └── plugin.json
├── commands/
│   ├── task-1.md
│   ├── task-2.md
│   └── workflows/
├── agents/
│   ├── specialist-1.md
│   ├── specialist-2.md
│   └── configs/
├── skills/
│   ├── skill-1.md
│   └── skill-2.md
├── hooks/
│   └── hooks.json
├── .mcp.json
├── .lsp.json
├── settings.json
├── templates/
│   └── issue-template.md
├── scripts/
│   ├── helper-1.sh
│   └── helper-2.py
├── docs/
│   ├── README.md
│   └── USAGE.md
└── tests/
    └── plugin.test.js

Practical Examples

Example 1: PR Review Plugin

File: .claude-plugin/plugin.json

{
  "name": "pr-review",
  "version": "1.0.0",
  "description": "Complete PR review workflow with security, testing, and docs",
  "author": {
    "name": "Anthropic"
  },
  "license": "MIT"
}

File: commands/review-pr.md

---
name: Review PR
description: Start comprehensive PR review with security and testing checks
---

# PR Review

This command initiates a complete pull request review including:

1. Security analysis
2. Test coverage verification
3. Documentation updates
4. Code quality checks
5. Performance impact assessment

File: agents/security-reviewer.md

---
name: security-reviewer
description: Security-focused code review
tools: read, grep, diff
---

# Security Reviewer

Specializes in finding security vulnerabilities:
- Authentication/authorization issues
- Data exposure
- Injection attacks
- Secure configuration

Installation:

/plugin install pr-review

# Result:
# ✅ 3 slash commands installed
# ✅ 3 subagents configured
# ✅ 2 MCP servers connected
# ✅ 4 hooks registered
# ✅ Ready to use!

Example 2: DevOps Plugin

Components:

devops-automation/
├── commands/
│   ├── deploy.md
│   ├── rollback.md
│   ├── status.md
│   └── incident.md
├── agents/
│   ├── deployment-specialist.md
│   ├── incident-commander.md
│   └── alert-analyzer.md
├── mcp/
│   ├── github-config.json
│   ├── kubernetes-config.json
│   └── prometheus-config.json
├── hooks/
│   ├── pre-deploy.js
│   ├── post-deploy.js
│   └── on-error.js
└── scripts/
    ├── deploy.sh
    ├── rollback.sh
    └── health-check.sh

Example 3: Documentation Plugin

Bundled Components:

documentation/
├── commands/
│   ├── generate-api-docs.md
│   ├── generate-readme.md
│   ├── sync-docs.md
│   └── validate-docs.md
├── agents/
│   ├── api-documenter.md
│   ├── code-commentator.md
│   └── example-generator.md
├── mcp/
│   ├── github-docs-config.json
│   └── slack-announce-config.json
└── templates/
    ├── api-endpoint.md
    ├── function-docs.md
    └── adr-template.md

Plugin Marketplace

graph TB
    A["Plugin Marketplace"]
    B["Official<br/>Anthropic"]
    C["Community<br/>Marketplace"]
    D["Enterprise<br/>Registry"]

    A --> B
    A --> C
    A --> D

    B -->|Categories| B1["Development"]
    B -->|Categories| B2["DevOps"]
    B -->|Categories| B3["Documentation"]

    C -->|Search| C1["DevOps Automation"]
    C -->|Search| C2["Mobile Dev"]
    C -->|Search| C3["Data Science"]

    D -->|Internal| D1["Company Standards"]
    D -->|Internal| D2["Legacy Systems"]
    D -->|Internal| D3["Compliance"]

Plugin Installation & Lifecycle

graph LR
    A["Discover"] -->|Browse| B["Marketplace"]
    B -->|Select| C["Plugin Page"]
    C -->|View| D["Components"]
    D -->|Install| E["/plugin install"]
    E -->|Extract| F["Configure"]
    F -->|Activate| G["Use"]
    G -->|Check| H["Update"]
    H -->|Available| G
    G -->|Done| I["Disable"]
    I -->|Later| J["Enable"]
    J -->|Back| G

Plugin Features Comparison

Feature	Slash Command	Skill	Subagent	Plugin
Installation	Manual copy	Manual copy	Manual config	One command
Setup Time	5 minutes	10 minutes	15 minutes	2 minutes
Bundling	Single file	Single file	Single file	Multiple
Versioning	Manual	Manual	Manual	Automatic
Team Sharing	Copy file	Copy file	Copy file	Install ID
Updates	Manual	Manual	Manual	Auto-available
Dependencies	None	None	None	May include
Marketplace	No	No	No	Yes
Distribution	Repository	Repository	Repository	Marketplace

Plugin Use Cases

Use Case	Recommendation	Why
Team Onboarding	✅ Use Plugin	Instant setup, all configurations
Framework Setup	✅ Use Plugin	Bundles framework-specific commands
Enterprise Standards	✅ Use Plugin	Central distribution, version control
Quick Task Automation	❌ Use Command	Overkill complexity
Single Domain Expertise	❌ Use Skill	Too heavy, use skill instead
Specialized Analysis	❌ Use Subagent	Create manually or use skill
Live Data Access	❌ Use MCP	Standalone, don't bundle

When to Create a Plugin

graph TD
    A["Should I create a plugin?"]
    A -->|Need multiple components| B{"Multiple commands<br/>or subagents<br/>or MCPs?"}
    B -->|Yes| C["✅ Create Plugin"]
    B -->|No| D["Use Individual Feature"]
    A -->|Team workflow| E{"Share with<br/>team?"}
    E -->|Yes| C
    E -->|No| F["Keep as Local Setup"]
    A -->|Complex setup| G{"Needs auto<br/>configuration?"}
    G -->|Yes| C
    G -->|No| D

Publishing a Plugin

Steps to publish:

Create plugin structure with all components
Write .claude-plugin/plugin.json manifest
Create README.md with documentation
Test locally with /plugin install ./my-plugin
Submit to plugin marketplace
Get reviewed and approved
Published on marketplace
Users can install with one command

Example submission:

# PR Review Plugin

## Description
Complete PR review workflow with security, testing, and documentation checks.

## What's Included
- 3 slash commands for different review types
- 3 specialized subagents
- GitHub and CodeQL MCP integration
- Automated security scanning hooks

## Installation
```bash
/plugin install pr-review
```

## Features
✅ Security analysis
✅ Test coverage checking
✅ Documentation verification
✅ Code quality assessment
✅ Performance impact analysis

## Usage
```bash
/review-pr
/check-security
/check-tests
```

## Requirements
- Claude Code 1.0+
- GitHub access
- CodeQL (optional)

Plugin vs Manual Configuration

Manual Setup (2+ hours):

Install slash commands one by one
Create subagents individually
Configure MCPs separately
Set up hooks manually
Document everything
Share with team (hope they configure correctly)

With Plugin (2 minutes):

/plugin install pr-review
# ✅ Everything installed and configured
# ✅ Ready to use immediately
# ✅ Team can reproduce exact setup

Comparison & Integration

Feature Comparison Matrix

Feature	Invocation	Persistence	Scope	Use Case
Slash Commands	Manual (`/cmd`)	Session only	Single command	Quick shortcuts
Subagents	Auto-delegated	Isolated context	Specialized task	Task distribution
Memory	Auto-loaded	Cross-session	User/team context	Long-term learning
MCP Protocol	Auto-queried	Real-time external	Live data access	Dynamic information
Skills	Auto-invoked	Filesystem-based	Reusable expertise	Automated workflows

Interaction Timeline

graph LR
    A["Session Start"] -->|Load| B["Memory (CLAUDE.md)"]
    B -->|Discover| C["Available Skills"]
    C -->|Register| D["Slash Commands"]
    D -->|Connect| E["MCP Servers"]
    E -->|Ready| F["User Interaction"]

    F -->|Type /cmd| G["Slash Command"]
    F -->|Request| H["Skill Auto-Invoke"]
    F -->|Query| I["MCP Data"]
    F -->|Complex task| J["Delegate to Subagent"]

    G -->|Uses| B
    H -->|Uses| B
    I -->|Uses| B
    J -->|Uses| B

Practical Integration Example: Customer Support Automation

Architecture

graph TB
    User["Customer Email"] -->|Receives| Router["Support Router"]

    Router -->|Analyze| Memory["Memory<br/>Customer history"]
    Router -->|Lookup| MCP1["MCP: Customer DB<br/>Previous tickets"]
    Router -->|Check| MCP2["MCP: Slack<br/>Team status"]

    Router -->|Route Complex| Sub1["Subagent: Tech Support<br/>Context: Technical issues"]
    Router -->|Route Simple| Sub2["Subagent: Billing<br/>Context: Payment issues"]
    Router -->|Route Urgent| Sub3["Subagent: Escalation<br/>Context: Priority handling"]

    Sub1 -->|Format| Skill1["Skill: Response Generator<br/>Brand voice maintained"]
    Sub2 -->|Format| Skill2["Skill: Response Generator"]
    Sub3 -->|Format| Skill3["Skill: Response Generator"]

    Skill1 -->|Generate| Output["Formatted Response"]
    Skill2 -->|Generate| Output
    Skill3 -->|Generate| Output

    Output -->|Post| MCP3["MCP: Slack<br/>Notify team"]
    Output -->|Send| Reply["Customer Reply"]

Request Flow

## Customer Support Request Flow

### 1. Incoming Email
"I'm getting error 500 when trying to upload files. This is blocking my workflow!"

### 2. Memory Lookup
- Loads CLAUDE.md with support standards
- Checks customer history: VIP customer, 3rd incident this month

### 3. MCP Queries
- GitHub MCP: List open issues (finds related bug report)
- Database MCP: Check system status (no outages reported)
- Slack MCP: Check if engineering is aware

### 4. Skill Detection & Loading
- Request matches "Technical Support" skill
- Loads support response template from Skill

### 5. Subagent Delegation
- Routes to Tech Support Subagent
- Provides context: customer history, error details, known issues
- Subagent has full access to: read, bash, grep tools

### 6. Subagent Processing
Tech Support Subagent:
- Searches codebase for 500 error in file upload
- Finds recent change in commit 8f4a2c
- Creates workaround documentation

### 7. Skill Execution
Response Generator Skill:
- Uses Brand Voice guidelines
- Formats response with empathy
- Includes workaround steps
- Links to related documentation

### 8. MCP Output
- Posts update to #support Slack channel
- Tags engineering team
- Updates ticket in Jira MCP

### 9. Response
Customer receives:
- Empathetic acknowledgment
- Explanation of cause
- Immediate workaround
- Timeline for permanent fix
- Link to related issues

Complete Feature Orchestration

sequenceDiagram
    participant User
    participant Claude as Claude Code
    participant Memory as Memory<br/>CLAUDE.md
    participant MCP as MCP Servers
    participant Skills as Skills
    participant SubAgent as Subagents

    User->>Claude: Request: "Build auth system"
    Claude->>Memory: Load project standards
    Memory-->>Claude: Auth standards, team practices
    Claude->>MCP: Query GitHub for similar implementations
    MCP-->>Claude: Code examples, best practices
    Claude->>Skills: Detect matching Skills
    Skills-->>Claude: Security Review Skill + Testing Skill
    Claude->>SubAgent: Delegate implementation
    SubAgent->>SubAgent: Build feature
    Claude->>Skills: Apply Security Review Skill
    Skills-->>Claude: Security checklist results
    Claude->>SubAgent: Delegate testing
    SubAgent-->>Claude: Test results
    Claude->>User: Complete system delivered

When to Use Each Feature

graph TD
    A["New Task"] --> B{Type of Task?}

    B -->|Repeated workflow| C["Slash Command"]
    B -->|Need real-time data| D["MCP Protocol"]
    B -->|Remember for next time| E["Memory"]
    B -->|Specialized subtask| F["Subagent"]
    B -->|Domain-specific work| G["Skill"]

    C --> C1["✅ Team shortcut"]
    D --> D1["✅ Live API access"]
    E --> E1["✅ Persistent context"]
    F --> F1["✅ Parallel execution"]
    G --> G1["✅ Auto-invoked expertise"]

Selection Decision Tree

graph TD
    Start["Need to extend Claude?"]

    Start -->|Quick repeated task| A{"Manual or Auto?"}
    A -->|Manual| B["Slash Command"]
    A -->|Auto| C["Skill"]

    Start -->|Need external data| D{"Real-time?"}
    D -->|Yes| E["MCP Protocol"]
    D -->|No/Cross-session| F["Memory"]

    Start -->|Complex project| G{"Multiple roles?"}
    G -->|Yes| H["Subagents"]
    G -->|No| I["Skills + Memory"]

    Start -->|Long-term context| J["Memory"]
    Start -->|Team workflow| K["Slash Command +<br/>Memory"]
    Start -->|Full automation| L["Skills +<br/>Subagents +<br/>MCP"]

Summary Table

Aspect	Slash Commands	Subagents	Memory	MCP	Skills	Plugins
Setup Difficulty	Easy	Medium	Easy	Medium	Medium	Easy
Learning Curve	Low	Medium	Low	Medium	Medium	Low
Team Benefit	High	High	Medium	High	High	Very High
Automation Level	Low	High	Medium	High	High	Very High
Context Management	Single-session	Isolated	Persistent	Real-time	Persistent	All features
Maintenance Burden	Low	Medium	Low	Medium	Medium	Low
Scalability	Good	Excellent	Good	Excellent	Excellent	Excellent
Shareability	Fair	Fair	Good	Good	Good	Excellent
Versioning	Manual	Manual	Manual	Manual	Manual	Automatic
Installation	Manual copy	Manual config	N/A	Manual config	Manual copy	One command

Quick Start Guide

Week 1: Start Simple

Create 2-3 slash commands for common tasks
Enable Memory in Settings
Document team standards in CLAUDE.md

Week 2: Add Real-time Access

Set up 1 MCP (GitHub or Database)
Use /mcp to configure
Query live data in your workflows

Week 3: Distribute Work

Create first Subagent for specific role
Use /agents command
Test delegation with simple task

Week 4: Automate Everything

Create first Skill for repeated automation
Use Skill marketplace or build custom
Combine all features for full workflow

Ongoing

Review and update Memory monthly
Add new Skills as patterns emerge
Optimize MCP queries
Refine Subagent prompts

Hooks

Overview

Hooks are event-driven shell commands that execute automatically in response to Claude Code events. They enable automation, validation, and custom workflows without manual intervention.

Hook Events

Claude Code supports 25 hook events across four hook types (command, http, prompt, agent):

Hook Event	Trigger	Use Cases
SessionStart	Session begins/resumes/clear/compact	Environment setup, initialization
InstructionsLoaded	CLAUDE.md or rules file loaded	Validation, transformation, augmentation
UserPromptSubmit	User submits prompt	Input validation, prompt filtering
PreToolUse	Before any tool runs	Validation, approval gates, logging
PermissionRequest	Permission dialog shown	Auto-approve/deny flows
PostToolUse	After tool succeeds	Auto-formatting, notifications, cleanup
PostToolUseFailure	Tool execution fails	Error handling, logging
Notification	Notification sent	Alerting, external integrations
SubagentStart	Subagent spawned	Context injection, initialization
SubagentStop	Subagent finishes	Result validation, logging
Stop	Claude finishes responding	Summary generation, cleanup tasks
StopFailure	API error ends turn	Error recovery, logging
TeammateIdle	Agent team teammate idle	Work distribution, coordination
TaskCompleted	Task marked complete	Post-task processing
TaskCreated	Task created via TaskCreate	Task tracking, logging
ConfigChange	Config file changes	Validation, propagation
CwdChanged	Working directory changes	Directory-specific setup
FileChanged	Watched file changes	File monitoring, rebuild triggers
PreCompact	Before context compaction	State preservation
PostCompact	After compaction completes	Post-compact actions
WorktreeCreate	Worktree being created	Environment setup, dependency install
WorktreeRemove	Worktree being removed	Cleanup, resource deallocation
Elicitation	MCP server requests user input	Input validation
ElicitationResult	User responds to elicitation	Response processing
SessionEnd	Session terminates	Cleanup, final logging

Common Hooks

Hooks are configured in ~/.claude/settings.json (user-level) or .claude/settings.json (project-level):

{
  "hooks": {
    "PostToolUse": [
      {
        "matcher": "Write",
        "hooks": [
          {
            "type": "command",
            "command": "prettier --write $CLAUDE_FILE_PATH"
          }
        ]
      }
    ],
    "PreToolUse": [
      {
        "matcher": "Edit",
        "hooks": [
          {
            "type": "command",
            "command": "eslint $CLAUDE_FILE_PATH"
          }
        ]
      }
    ]
  }
}

Hook Environment Variables

$CLAUDE_FILE_PATH - Path to file being edited/written
$CLAUDE_TOOL_NAME - Name of tool being used
$CLAUDE_SESSION_ID - Current session identifier
$CLAUDE_PROJECT_DIR - Project directory path

Best Practices

✅ Do:

Keep hooks fast (< 1 second)
Use hooks for validation and automation
Handle errors gracefully
Use absolute paths

❌ Don't:

Make hooks interactive
Use hooks for long-running tasks
Hardcode credentials

See: 06-hooks/ for detailed examples

Checkpoints and Rewind

Overview

Checkpoints allow you to save conversation state and rewind to previous points, enabling safe experimentation and exploration of multiple approaches.

Key Concepts

Concept	Description
Checkpoint	Snapshot of conversation state including messages, files, and context
Rewind	Return to a previous checkpoint, discarding subsequent changes
Branch Point	Checkpoint from which multiple approaches are explored

Accessing Checkpoints

Checkpoints are created automatically with every user prompt. To rewind:

# Press Esc twice to open the checkpoint browser
Esc + Esc

# Or use the /rewind command
/rewind

When you select a checkpoint, you choose from five options:

Restore code and conversation -- Revert both to that point
Restore conversation -- Rewind messages, keep current code
Restore code -- Revert files, keep conversation
Summarize from here -- Compress conversation into a summary
Never mind -- Cancel

Use Cases

Scenario	Workflow
Exploring Approaches	Save → Try A → Save → Rewind → Try B → Compare
Safe Refactoring	Save → Refactor → Test → If fail: Rewind
A/B Testing	Save → Design A → Save → Rewind → Design B → Compare
Mistake Recovery	Notice issue → Rewind to last good state

Configuration

{
  "autoCheckpoint": true
}

See: 08-checkpoints/ for detailed examples

Advanced Features

Planning Mode

Create detailed implementation plans before coding.

Activation:

/plan Implement user authentication system

Benefits:

Clear roadmap with time estimates
Risk assessment
Systematic task breakdown
Opportunity for review and modification

Extended Thinking

Deep reasoning for complex problems.

Activation:

Toggle with Alt+T (or Option+T on macOS) during a session
Set MAX_THINKING_TOKENS environment variable for programmatic control

# Enable extended thinking via environment variable
export MAX_THINKING_TOKENS=50000
claude -p "Should we use microservices or monolith?"

Benefits:

Thorough analysis of trade-offs
Better architectural decisions
Consideration of edge cases
Systematic evaluation

Background Tasks

Run long operations without blocking the conversation.

Usage:

User: Run tests in background

Claude: Started task bg-1234

/task list           # Show all tasks
/task status bg-1234 # Check progress
/task show bg-1234   # View output
/task cancel bg-1234 # Cancel task

Permission Modes

Control what Claude can do.

Mode	Description	Use Case
default	Standard permissions with prompts for sensitive actions	General development
acceptEdits	Automatically accept file edits without confirmation	Trusted editing workflows
plan	Analysis and planning only, no file modifications	Code review, architecture planning
auto	Automatically approve safe actions, prompt only for risky ones	Balanced autonomy with safety
dontAsk	Execute all actions without confirmation prompts	Experienced users, automation
bypassPermissions	Full unrestricted access, no safety checks	CI/CD pipelines, trusted scripts

Usage:

claude --permission-mode plan          # Read-only analysis
claude --permission-mode acceptEdits   # Auto-accept edits
claude --permission-mode auto          # Auto-approve safe actions
claude --permission-mode dontAsk       # No confirmation prompts

Headless Mode (Print Mode)

Run Claude Code without interactive input for automation and CI/CD using the -p (print) flag.

Usage:

# Run specific task
claude -p "Run all tests"

# Pipe input for analysis
cat error.log | claude -p "explain this error"

# CI/CD integration (GitHub Actions)
- name: AI Code Review
  run: claude -p "Review PR changes and report issues"

# JSON output for scripting
claude -p --output-format json "list all functions in src/"

Scheduled Tasks

Run tasks on a repeating schedule using the /loop command.

Usage:

/loop every 30m "Run tests and report failures"
/loop every 2h "Check for dependency updates"
/loop every 1d "Generate daily summary of code changes"

Scheduled tasks run in the background and report results when complete. They are useful for continuous monitoring, periodic checks, and automated maintenance workflows.

Chrome Integration

Claude Code can integrate with the Chrome browser for web automation tasks. This enables capabilities like navigating web pages, filling forms, taking screenshots, and extracting data from websites directly within your development workflow.

Session Management

Manage multiple work sessions.

Commands:

/resume                # Resume a previous conversation
/rename "Feature"      # Name the current session
/fork                  # Fork into a new session
claude -c              # Continue most recent conversation
claude -r "Feature"    # Resume session by name/ID

Interactive Features

Keyboard Shortcuts:

Ctrl + R - Search command history
Tab - Autocomplete
↑ / ↓ - Command history
Ctrl + L - Clear screen

Multi-line Input:

User: \
> Long complex prompt
> spanning multiple lines
> \end

Configuration

Complete configuration example:

{
  "planning": {
    "autoEnter": true,
    "requireApproval": true
  },
  "extendedThinking": {
    "enabled": true,
    "showThinkingProcess": true
  },
  "backgroundTasks": {
    "enabled": true,
    "maxConcurrentTasks": 5
  },
  "permissions": {
    "mode": "default"
  }
}

See: 09-advanced-features/ for comprehensive guide

Resources

Last updated: April 16, 2026 For Claude Haiku 4.5, Sonnet 4.6, and Opus 4.7 Now includes: Hooks, Checkpoints, Planning Mode, Extended Thinking, Background Tasks, Permission Modes (6 modes), Headless Mode, Session Management, Auto Memory, Agent Teams, Scheduled Tasks, Chrome Integration, Channels, Voice Dictation, and Bundled Skills

Last Updated: April 16, 2026 Claude Code Version: 2.1.112 Sources:

https://docs.anthropic.com/en/docs/claude-code
https://www.anthropic.com/news/claude-opus-4-7
https://support.claude.com/en/articles/12138966-release-notes Compatible Models: Claude Sonnet 4.6, Claude Opus 4.7, Claude Haiku 4.5

82 KiB Raw Permalink Blame History

Complete Guide to Claude Concepts

Table of Contents

Slash Commands

Overview

Architecture

File Structure

Command Organization Table

Features & Capabilities

Practical Examples

Example 1: Code Optimization Command

Example 2: Pull Request Helper Command

Example 3: Hierarchical Documentation Generator

Command Lifecycle Diagram

Best Practices

Subagents

Overview

Architecture Diagram

Subagent Lifecycle

Subagent Configuration Table

Tool Access Hierarchy

Practical Examples

Example 1: Complete Subagent Setup

Example 2: Subagent Delegation in Action

Example 3: Tool Permission Scoping

Subagent Context Management

When to Use Subagents

Agent Teams

Memory

Overview

Memory Architecture

Memory Hierarchy in Claude Code (7 Tiers)

Memory Locations Table

Auto Memory

Memory Update Lifecycle

Practical Examples

Example 1: Project Memory Structure

Example 2: Directory-Specific Memory

Example 3: Personal Memory

Example 4: Memory Update During Session

Memory in Claude Web/Desktop

Memory Synthesis Timeline

Memory Features Comparison

MCP Protocol

Overview

MCP Architecture

MCP Ecosystem

MCP Setup Process

Available MCP Servers Table

Practical Examples

Example 1: GitHub MCP Configuration

Example 2: Database MCP Setup

Example 3: Multi-MCP Workflow

Example 4: Filesystem MCP Operations

MCP vs Memory: Decision Matrix

Request/Response Pattern

Agent Skills

Overview

Skill Architecture

Skill Loading Process

Skill Types & Locations Table

Pre-built Skills

Bundled Skills

Practical Examples

Example 1: Custom Code Review Skill

Python Script: analyze-metrics.py

Python Script: compare-complexity.py

Template: review-checklist.md

Template: finding-template.md

Usage Example

Example 2: Brand Voice Skill

Examples

✅ Good Example

❌ Bad Example

Template: Email

Template: Social Media

File: tone-examples.md

Example 3: Documentation Generator Skill

Skill Discovery & Invocation

Skill vs Other Features

82 KiB

Raw Permalink Blame History