Awesome-omni-skill claude-agent-sdk
Build production AI agents using the Claude Agent SDK (TypeScript/Python). Use this skill when the user asks about: (1) Building AI agents with Claude, (2) Using the @anthropic-ai/claude-agent-sdk or claude-agent-sdk packages, (3) Implementing agent features like tools, hooks, subagents, MCP integration, or sessions, (4) Migrating from Claude Code SDK to Agent SDK, (5) Configuring permissions, budgets, or custom tool access.
git clone https://github.com/diegosouzapw/awesome-omni-skill
T=$(mktemp -d) && git clone --depth=1 https://github.com/diegosouzapw/awesome-omni-skill "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/development/claude-agent-sdk-jeongsk" ~/.claude/skills/diegosouzapw-awesome-omni-skill-claude-agent-sdk-b52a6b && rm -rf "$T"
skills/development/claude-agent-sdk-jeongsk/SKILL.mdClaude Agent SDK
Build AI agents that autonomously read files, run commands, search the web, edit code, and more. The Agent SDK provides the same tools, agent loop, and context management that power Claude Code, programmable in Python and TypeScript.
Installation
Prerequisites
Install Claude Code runtime first:
# macOS/Linux/WSL curl -fsSL https://claude.ai/install.sh | bash # or via npm npm install -g @anthropic-ai/claude-code
SDK Installation
# TypeScript npm install @anthropic-ai/claude-agent-sdk # Python pip install claude-agent-sdk
API Key
export ANTHROPIC_API_KEY=your-api-key
Alternative authentication:
- Amazon Bedrock:
CLAUDE_CODE_USE_BEDROCK=1 - Google Vertex AI:
CLAUDE_CODE_USE_VERTEX=1 - Microsoft Foundry:
CLAUDE_CODE_USE_FOUNDRY=1
Basic Usage
TypeScript
import { query } from "@anthropic-ai/claude-agent-sdk"; for await (const message of query({ prompt: "Find and fix the bug in auth.py", options: { allowedTools: ["Read", "Edit", "Bash"] } })) { if ("result" in message) console.log(message.result); }
Python
import asyncio from claude_agent_sdk import query, ClaudeAgentOptions async def main(): async for message in query( prompt="Find and fix the bug in auth.py", options=ClaudeAgentOptions(allowed_tools=["Read", "Edit", "Bash"]) ): if hasattr(message, "result"): print(message.result) asyncio.run(main())
Built-in Tools
| Tool | Description |
|---|---|
| Read | Read any file in the working directory |
| Write | Create new files |
| Edit | Make precise edits to existing files |
| Bash | Run terminal commands, scripts, git operations |
| Glob | Find files by pattern (, ) |
| Grep | Search file contents with regex |
| WebSearch | Search the web for current information |
| WebFetch | Fetch and parse web page content |
| Task | Spawn subagents for specialized tasks |
Key Features
For detailed API reference and examples:
- TypeScript: See references/typescript-api.md
- Python: See references/python-api.md
Permission Modes
: Standard permission checksdefault
: Automatically approve file editsacceptEdits
: Skip all permission checks (use with caution)bypassPermissions
Sessions
Resume conversations with full context:
// Capture session ID from init message if (message.type === 'system' && message.subtype === 'init') { sessionId = message.session_id; } // Resume later for await (const message of query({ prompt: "Continue where we left off", options: { resume: sessionId } })) { ... }
MCP Integration
Connect external tools via Model Context Protocol:
const response = query({ prompt: "Open example.com and describe what you see", options: { mcpServers: { playwright: { command: "npx", args: ["@playwright/mcp@latest"] } } } });
Subagents
Spawn specialized agents for focused tasks:
const response = query({ prompt: "Use the code-reviewer agent to review this codebase", options: { allowedTools: ["Read", "Glob", "Grep", "Task"], agents: { "code-reviewer": { description: "Expert code reviewer for quality and security reviews.", prompt: "Analyze code quality and suggest improvements.", tools: ["Read", "Glob", "Grep"] } } } });
Hooks
Run custom code at key points in the agent lifecycle:
,PreToolUsePostToolUse
,Stop
,SessionStartSessionEndUserPromptSubmit
Budget Control
const response = query({ prompt: "Analyze the codebase", options: { maxBudgetUsd: 2.5 // Stop if cost exceeds $2.50 } });
Project Skills
Load custom skills from
.claude/commands/:
const response = query({ prompt: "Review the pull request using available skills", options: { settingSources: ["project"] // Loads skills from .claude/commands/ } });
Message Types
The SDK streams various message types:
(subtypesystem
): Session initialization withinitsession_id
: Claude's text responses and tool callsassistant
: Tool results and user inputuser
: Final response with execution statsresult
: Error informationerror
Available Models
- Most capableclaude-opus-4-5-20251101
- Balanced (default)claude-sonnet-4-20250514
- Latest Sonnetclaude-sonnet-4-5-20250929
- Fastestclaude-haiku-4-5-20251001
SDK vs Client SDK
The Agent SDK handles tool loops autonomously:
// Agent SDK: Claude handles tools autonomously for await (const message of query({ prompt: "Fix the bug in auth.py" })) { console.log(message); }
The Client SDK requires you to implement tool execution:
// Client SDK: You implement the tool loop let response = await client.messages.create({...}); while (response.stop_reason === "tool_use") { const result = yourToolExecutor(response.tool_use); response = await client.messages.create({ tool_result: result, ... }); }