Fetches claude-agent-sdk from jezweb/claude-skills and configures it for Cursor.
3
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
◆ Which agents do you want to install to?
│
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4
Verify installation
Confirm successful installation by checking the skill directory location:
.cursor/skills/claude-agent-sdk
Restart Cursor to activate claude-agent-sdk. Access via /claude-agent-sdk in your agent's command palette.
⚠
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Type safety - Full TypeScript inference with Zod schemas
Example:
import{ query }from"@anthropic-ai/claude-agent-sdk";import{ z }from"zod";import{ zodToJsonSchema }from"zod-to-json-schema";const schema = z.object({ summary: z.string(), sentiment: z.enum(['positive','neutral','negative']), confidence: z.number().min(0).max(1)});const response =query({ prompt:"Analyze this code review feedback", options:{ model:"claude-sonnet-4-5", outputFormat:{ type:"json_schema", json_schema:{ name:"AnalysisResult", strict:true, schema:zodToJsonSchema(schema)}}}});forawait(const message of response){if(message.type ==='result'&& message.structured_output){// Guaranteed to match schemaconst validated = schema.parse(message.structured_output);console.log(`Sentiment: ${validated.sentiment}`);}}
Zod Compatibility (v0.1.71+): SDK supports both Zod v3.24.1+ and Zod v4.0.0+ as peer dependencies. Import remains import { z } from "zod" for either version.
betas - Enable beta features (e.g., 1M context window)
sandbox - Sandbox settings for secure execution
enableFileCheckpointing - Enable file state snapshots
systemPrompt - System prompt (string or preset object)
Extended Context (1M Tokens)
Enable 1 million token context window:
const response =query({ prompt:"Analyze this large codebase", options:{ betas:['context-1m-2025-08-07'],// Enable 1M context model:"claude-sonnet-4-5"}});
System Prompt Configuration
Two forms of systemPrompt:
// 1. Simple stringsystemPrompt:"You are a helpful coding assistant."// 2. Preset with optional append (preserves Claude Code defaults)systemPrompt:{ type:'preset', preset:'claude_code', append:"\n\nAdditional context: Focus on security."}
Use preset form when you want Claude Code's default behaviors plus custom additions.
Tool Integration (Built-in + Custom)
Tool Control:
allowedTools - Whitelist (takes precedence)
disallowedTools - Blacklist
canUseTool - Custom permission callback (see Permission Control section)
const response =query({ prompt:"Review and refactor the codebase", options:{ allowedTools:["Read","Write","Edit","AskUserQuestion"]}});// Agent can now ask clarifying questions// Questions appear in message stream as tool_call with name "AskUserQuestion"
Use cases:
Clarify ambiguous requirements mid-task
Get user approval before destructive operations
Present options and get selection
Tools Configuration (v0.1.57+)
Three forms of tool configuration:
// 1. Exact allowlist (string array)tools:["Read","Write","Grep"]// 2. Disable all tools (empty array)tools:[]// 3. Preset with defaults (object form)tools:{ type:'preset', preset:'claude_code'
Implementation Guide
Prerequisites
›Claude Desktop or compatible AI client with skill support
›Clear understanding of task or problem to solve
›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
1Install skill using provided installation command
2Test with simple use case relevant to your work
3Evaluate output quality and relevance
4Iterate on prompts to improve results
5Integrate into regular workflow if valuable
Common Pitfalls
⚠Expecting perfect results without iteration
⚠Not providing enough context in prompts
⚠Using skill for tasks outside its intended scope
⚠Accepting outputs without review and validation
Best Practices
✓ Do
+Start with clear, specific prompts
+Provide relevant context and constraints
+Review and refine all outputs before using
+Iterate to improve output quality
+Document successful prompt patterns
✗ Don't
−Don't use without understanding skill limitations
−Don't skip validation of outputs
−Don't share sensitive information in prompts
−Don't expect skill to replace human judgment
💡 Pro Tips
★Be specific about desired format and style
★Ask for multiple options to choose from
★Request explanations to understand reasoning
★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
1Familiarize yourself with skill capabilities and limitations
2Start with low-risk, non-critical tasks
3Progress to more complex and valuable use cases
4Build expertise through regular use and experimentation
