Claude SDK Expert Skill

Purpose

Build autonomous AI agents using Claude Agent SDK, leveraging computer use, tool orchestration, and MCP integration for production deployments.

SDK Overview

Claude Agent SDK (2026)

Enables building autonomous agents that control computers, write files, run commands, and iterate on work.

Core Philosophy: Give Claude a computer to unlock agent effectiveness beyond chat.

Key Capabilities

1. Computer Use

Claude can control a computer environment:

File system operations (read, write, edit)
Terminal command execution
Iterative debugging and refinement
Multi-step autonomous workflows

Use Cases: Finance agents, personal assistants, customer support, development agents, research agents

2. Built-in Tools

Category	Tools
Files	Read, Write, Edit
Commands	Bash
Search	Grep, Glob
Web	WebFetch, WebSearch

3. MCP Integration

Define custom tools via Model Context Protocol servers.

Benefits:

Standardized tool interface
Reusable across agents
Enterprise data connectivity

Popular MCP Servers: GitHub, Slack, PostgreSQL, MongoDB, Stripe, Salesforce

Architecture Patterns

Pattern 1: Autonomous Task Completion

Agent completes multi-step task without intervention.

User Request → Analyze → Subtasks → Execute Tools → Iterate → Result

Pattern 2: Human-in-the-Loop

Agent proposes actions, waits for approval.

Task → Plan → Human Review → Approve? → Execute → Result

Pattern 3: Iterative Refinement

Agent retries on errors automatically.

Attempt 1 → Error → Analyze → Attempt 2 → Success

See: resources/code-examples.py for full implementations

Tool Design Best Practices

DO

Provide tools relevant to the task
Use clear, descriptive names
Write detailed descriptions (Claude reads these!)
Define strict input schemas
Implement error handling
Return structured outputs

DON'T

Give agents unnecessary tools
Use ambiguous names ("handler", "processor")
Skip input validation
Return raw errors without context
Hide side effects

See: resources/code-examples.py for good/bad tool examples

MCP Integration

Connecting MCP Servers

mcp_config = {
    "servers": {
        "github": {
            "command": "npx",
            "args": ["-y", "@modelcontextprotocol/server-github"],
            "env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")}
        }
    }
}

Full example: resources/code-examples.py

Production Best Practices

1. Streaming

Show real-time progress to build user trust.

2. Error Handling

Catch API errors, rate limits, tool failures
Implement fallbacks and retries
Log errors for debugging

3. Cost Optimization

Use Haiku for simple tasks, Sonnet for complex
Cache repetitive contexts
Batch similar requests
Monitor token usage

4. Security

Restrict file/command access
Sanitize dangerous inputs
Audit all agent actions
Validate tool outputs

Implementation: resources/code-examples.py

Model Selection (January 2026)

Model	Best For	Pricing (per M tokens)	Speed
claude-opus-4-5	Flagship reasoning, complex agents, highest accuracy	$5 in / $25 out	Slower
claude-sonnet-4-5	Best balance - coding, agents, computer use	$3 in / $15 out	Medium
claude-haiku-4	Simple tasks, format conversions, high-throughput	$0.25 in / $1.25 out	Fast

Note: Opus 4.5 achieved 80.9% on SWE-bench Verified. Sonnet 4.5 supports 1M token context with beta header.

Testing Agents

Unit Testing

Test individual tools in isolation.

Integration Testing

Test agent workflows with multiple tools.

Evaluation Framework

Measure accuracy, latency, tool efficiency.

Examples: resources/code-examples.py

Monitoring Metrics

Metric	Description
Tool Call Success Rate	% of tool invocations succeeding
Task Completion Rate	% of requests fully resolved
Average Iterations	Tool calls per task
Latency	Time to complete requests
Token Usage	Input + output tokens
Error Rate	% of requests with errors

Decision Framework

Use Claude SDK when:

Building on Anthropic models
Need computer use capabilities
Want production-ready agent framework
Require MCP integration
Building autonomous agents

Consider alternatives when:

Committed to OpenAI ecosystem → AgentKit
Need visual agent builder → AgentKit
Require complex state machines → LangGraph
Want full OSS control → AutoGen/LangGraph

Resources

Documentation:

GitHub:

Key Principles

Computer Use is Game-Changing - Leverage file/bash capabilities fully
Tools are First-Class - Design tools as carefully as prompts
MCP for Data - Use MCP servers for enterprise connectivity
Stream for UX - Real-time feedback builds trust
Security Always - Validate inputs, restrict permissions, audit
Right Model for Task - Haiku for simple, Sonnet for complex

Build powerful, autonomous agents using Claude's cutting-edge capabilities.