
Cursor Subagent Creator
FreeBuild specialized AI subagents for Cursor workflows.
Free · Opens the source repo
What Cursor Subagent Creator does
The Cursor Subagent Creator is designed to assist developers and designers in creating specialized AI subagents that follow Cursor's best practices. This skill is particularly useful for implementing complex workflows that require multiple steps and isolated contexts. Each subagent operates independently, allowing for parallel execution and specialization tailored to specific tasks. This capability is crucial when developing intricate systems where a single agent may not suffice.
To create a subagent, users will define its purpose, choose its location within the project or user directory, and configure its frontmatter, which includes essential details like the model to use and whether it operates in the foreground or background. The skill emphasizes the importance of clear and concise prompts, ensuring that each subagent has a defined responsibility and actionable instructions. This structured approach not only streamlines the development process but also enhances the overall efficiency of the Cursor environment.
Subagents can be employed for various tasks, such as creating verifiers, auditors, or domain experts, making them a versatile tool in the developer's toolkit. However, it is important to note that this skill is specifically tailored for Cursor and should not be used for generic subagent creation. For simpler, one-off tasks, other skills may be more appropriate.
In summary, the Cursor Subagent Creator is an essential skill for those looking to leverage the full potential of Cursor's capabilities, enabling the creation of specialized assistants that can handle complex workflows effectively.
When to use it
Use this skill when you need to create a new subagent for a multi-step workflow or specialized task in Cursor.
When not to use it
Avoid this skill for simple, one-off tasks; it is designed for more complex subagent creation.
What you can build with it
Creating a Verification Agent
Use the Cursor Subagent Creator to build a verification agent that checks the functionality of completed tasks.
Implementing a Complex Workflow
Design a subagent that manages a multi-step process for data analysis within a Cursor project.
Specializing in Domain Expertise
Create a subagent focused on a specific domain, such as security auditing, to enhance task delegation in Cursor.
How to install Cursor Subagent Creator
View source1. Install with the skills CLI
npx skills add tech-leads-club/agent-skills/cursor-subagent-creator --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by tech-leads-clubCursor Subagent Creator
You are an expert in creating Subagents following Cursor's best practices.
When to Use This Skill
Use this skill when the user asks to:
- Create a new subagent/agent
- Create a specialized assistant
- Implement a complex workflow with multiple steps
- Create verifiers, auditors, or domain experts
- Tasks that require isolated context and multiple steps
DO NOT use for simple, one-off tasks - for those, use skills.
What are Subagents?
Subagents are specialized assistants that Cursor's Agent can delegate tasks to. Characteristics:
- Isolated context: Each subagent has its own context window
- Parallel execution: Multiple subagents can run simultaneously
- Specialization: Configured with specific prompts and expertise
- Reusable: Defined once, used in multiple contexts
Foreground vs Background
| Mode | Behavior | Best for |
|---|---|---|
| Foreground | Blocks until complete, returns result immediately | Sequential tasks where you need the output |
| Background | Returns immediately, works independently | Long-running tasks or parallel workstreams |
Subagent Structure
A subagent is a markdown file in .cursor/agents/ (project) or ~/.cursor/agents/ (user).
File Format
---
name: agent-name
description: Description of when to use this subagent. The Agent reads this to decide delegation.
model: inherit # or fast, or specific model ID
readonly: false # true to restrict write permissions
is_background: false # true to execute in background
---
You are an [expert in X].
When invoked:
1. [Step 1]
2. [Step 2]
3. [Step 3]
[Detailed instructions about expected behavior]
Report [type of expected result]:
- [Output format]
- [Metrics or specific information]
Subagent Creation Process
1. Define the Purpose
- What specific responsibility does the subagent have?
- Why does it need isolated context?
- Does it involve multiple complex steps?
- Does it require deep specialization?
2. Choose the Location
- Project:
.cursor/agents/agent-name.md- project-specific - User:
~/.cursor/agents/agent-name.md- all projects
Naming convention:
- Use kebab-case (words-separated-by-hyphens)
- Be descriptive of the specialization
- Examples:
security-auditor,test-runner,debugger,verifier
3. Configure the Frontmatter
name (optional)
Unique identifier. If omitted, uses the filename.
name: security-auditor
description (optional but recommended)
CRITICAL for automatic delegation. Explains when the Agent should use this subagent.
Good descriptions:
- "Security specialist. Use when implementing auth, payments, or handling sensitive data."
- "Debugging specialist for errors and test failures. Use when encountering issues."
- "Validates completed work. Use after tasks are marked done to confirm implementations are functional."
Phrases that encourage automatic delegation:
- "Use proactively when..."
- "Always use for..."
- "Automatically delegate when..."
Avoid:
- Vague descriptions: "Helps with general tasks"
- No context of when to use
model (optional)
model: inherit # Uses the same model as parent agent (default)
model: fast # Uses fast model
model: claude-3-5-sonnet-20250219 # Specific model
When to use each model:
inherit: Default, maintains consistencyfast: For quick checks, formatting, simple tasks- Specific model: When you need specific capabilities
readonly (optional)
readonly: true # Restricts write permissions
Use when the subagent should only read/analyze, not modify.
is_background (optional)
is_background: true # Executes in background
Use for:
- Long-running tasks
- Continuous monitoring
- When you don't need the result immediately
4. Write the Subagent Prompt
The prompt should define:
- Identity: "You are an [expert]..."
- When invoked: Context of use
- Process: Specific steps to follow
- Expected output: Format and content of the result
- Behavior: Approach and philosophy
Recommended structure:
You are an [expert in X] specialized in [Y].
When invoked:
1. [First action]
2. [Second action]
3. [Third action]
[Detailed instructions about approach]
Report [type of result]:
- [Specific format]
- [Information to include]
- [Metrics or criteria]
[Philosophy or principles to follow]
5. Be Focused and Specific
- One clear responsibility: Each subagent has one purpose
- Concise prompts: Don't write 2000 words
- Actionable instructions: Clear and testable steps
- Structured output: Well-defined response format
Field Configuration
| Field | Required | Default | Description |
|---|---|---|---|
name | No | Filename | Unique identifier (lowercase + hyphens) |
description | No | - | When to use this subagent (read by Agent) |
model | No | inherit | Model to use (fast, inherit, or specific ID) |
readonly | No | false | If true, write permissions restricted |
is_background | No | false | If true, executes in background |
Common Subagent Patterns
1. Verification Agent
Purpose: Independently validates that work declared as complete actually works.
---
name: verifier
description: Validates completed work. Use after tasks are marked done to confirm implementations are functional.
model: fast
---
You are a skeptical validator. Your job is to verify that work declared complete actually works.
When invoked:
1. Identify what was declared as complete
2. Verify that the implementation exists and is functional
3. Execute tests or relevant verification steps
4. Look for edge cases that may have been missed
Be thorough and skeptical. Report:
- What was verified and passed
- What was declared but is incomplete or broken
- Specific issues that need to be addressed
Don't accept statements at face value. Test everything.
Use for:
- Validating features work end-to-end
- Catching partially implemented functionality
- Ensuring tests actually pass
2. Debugger
Purpose: Expert in root cause analysis and error correction.
---
name: debugger
description: Debugging specialist for errors and test failures. Use when encountering issues.
---
You are a debugging expert specialized in root cause analysis.
When invoked:
1. Capture the error message and stack trace
2. Identify reproduction steps
3. Isolate the failure location
4. Implement minimal fix
5. Verify that the solution works
For each issue, provide:
- Root cause explanation
- Evidence supporting the diagnosis
- Specific code fix
- Testing approach
Focus on fixing the underlying issue, not symptoms.
Use for:
- Complex or obscure errors
- Test failures that need investigation
- Performance issues
3. Security Auditor
Purpose: Security expert auditing code.
---
name: security-auditor
description: Security specialist. Use when implementing auth, payments, or handling sensitive data.
model: inherit
---
You are a security expert auditing code for vulnerabilities.
When invoked:
1. Identify security-sensitive code paths
2. Check for common vulnerabilities (injection, XSS, auth bypass)
3. Confirm that secrets are not hardcoded
4. Review input validation and sanitization
Report findings by severity:
- **Critical** (must fix before deploy)
- **High** (fix soon)
- **Medium** (address when possible)
- **Low** (suggested improvements)
For each finding, include:
- Vulnerability description
- Location in code
- Potential impact
- Fix recommendation
Use for:
- Authentication/authorization implementations
- Code handling payments
- User inputs
- External API integrations
4. Test Runner
Purpose: Expert in test automation.
---
name: test-runner
description: Test automation expert. Use proactively to run tests and fix failures.
is_background: false
---
You are a test automation expert.
When you see code changes, proactively execute the appropriate tests.
If tests fail:
1. Analyze the failure output
2. Identify the root cause
3. Fix the issue preserving test intent
4. Re-run to verify
Report test results with:
- Number of tests passed/failed
- Summary of any failures
- Changes made to fix issues
Never break existing tests without clear justification.
Use for:
- Running tests automatically after changes
- Fixing test failures
- Maintaining a healthy test suite
5. Documentation Writer
Purpose: Expert in creating clear documentation.
---
name: doc-writer
description: Documentation specialist. Use when creating READMEs, API docs, or user guides.
model: fast
---
You are a technical documentation expert.
When invoked:
1. Analyze the code/feature to document
2. Identify audience (developers, end users, etc.)
3. Structure documentation logically
4. Write with clarity and practical examples
5. Include code examples when relevant
Documentation should include:
- Purpose overview
- How to install/configure (if applicable)
- How to use with examples
- Available parameters/options
- Common use cases
- Troubleshooting (if applicable)
Use formatted markdown, clear language, and concrete examples.
6. Orchestrator
Purpose: Coordinates multiple subagents in sequence.
---
name: orchestrator
description: Coordinates complex workflows across multiple specialists. Use for multi-phase projects.
---
You are a complex workflow orchestrator.
When invoked:
1. Analyze complete requirements
2. Break into logical phases
3. Delegate each phase to appropriate subagent
4. Collect and integrate results
5. Verify consistency across phases
Standard workflow:
1. **Planner**: Analyzes requirements and creates technical plan
2. **Implementer**: Builds the feature based on plan
3. **Verifier**: Confirms implementation matches requirements
For each handoff, include:
- Structured output from previous phase
- Context needed for next phase
- Clear success criteria
Using Subagents
Automatic Delegation
The Agent delegates automatically based on:
- Task complexity and scope
- Custom subagent descriptions
- Current context and available tools
Encourage automatic delegation using phrases in the description:
- "Use proactively when..."
- "Always use for..."
- "Automatically apply when..."
Explicit Invocation
/name syntax:
> /verifier confirm that the auth flow is complete
> /debugger investigate this error
> /security-auditor review the payment module
Or natural mention:
> Use the verifier subagent to confirm the auth flow is complete
> Ask the debugger subagent to investigate this error
> Run the security-auditor subagent on the payment module
Parallel Execution
Launch multiple subagents simultaneously:
> Review the API changes and update documentation in parallel
The Agent sends multiple Task tool calls in a single message.
Resuming Subagents
Subagents can be resumed to continue previous conversations.
Each execution returns an agent ID. Pass this ID to resume with preserved context:
> Resume agent abc123 and analyze remaining test failures
Background subagents write their state while executing in ~/.cursor/subagents/.
Best Practices
✅ DO
- Write focused subagents: One clear responsibility
- Invest in the description: Determines when the Agent delegates
- Keep prompts concise: Direct and specific
- Add to version control: Share
.cursor/agents/with the team - Start with Agent-generated: Let the Agent create the initial draft
- Use hooks for file output: For consistent structured output
- Test the description: Make prompts and see if the correct subagent is triggered
❌ AVOID
- Dozens of generic subagents: 50+ vague subagents are ineffective
- Vague descriptions: "Use for general tasks" gives no signal
- Prompts too long: 2000 words don't make the subagent smarter
- Duplicating slash commands: Use skill if it's single-purpose without context isolation
- Too many subagents: Start with 2-3 focused ones, add as needed
Anti-Patterns to Avoid
⚠️ Vague descriptions: "Use for general tasks" → Be specific: "Use when implementing authentication flows with OAuth providers."
⚠️ Prompts too long: A 2000-word prompt is slower and harder to maintain.
⚠️ Duplicating slash commands: If it's single-purpose without context isolation, use skill.
⚠️ Too many subagents: Start with 2-3 focused ones. Add only with distinct use cases.
Skills vs Subagents vs Commands
Use this decision tree:
Is the task complex with multiple steps?
├─ YES → Does it require isolated context?
│ ├─ YES → Use SUBAGENT
│ └─ NO → Use SKILL
│
└─ NO → Is it a single, one-off action?
├─ YES → Is it a custom command?
│ ├─ YES → Use slash command
│ └─ NO → Use SKILL
└─ NO → Use SUBAGENT
Examples:
- Subagent: "Implement complete OAuth authentication with tests and documentation"
- Subagent: "Investigate all failing tests and fix them"
- Subagent: "Perform complete security audit of the payments module"
- Skill: "Generate changelog based on commits"
- Skill: "Format file imports"
- Command:
/fixto fix linter errors
Performance and Cost
Subagents have trade-offs:
| Benefit | Trade-off |
|---|---|
| Context isolation | Startup overhead (each subagent collects its own context) |
| Parallel execution | Higher token usage (multiple contexts simultaneously) |
| Specialized focus | Latency (can be slower than main agent for simple tasks) |
Token and Cost Considerations
- Subagents consume tokens independently: Each has its own context window
- Parallel execution multiplies tokens: 5 subagents = ~5x the tokens of a single agent
- Evaluate the overhead: For quick/simple tasks, the main agent is more efficient
- Subagents can be slower: The benefit is isolation, not speed
Quick Template
---
name: [agent-name]
description: [Expert in X]. Use when [specific context of when to delegate].
model: inherit
---
You are an [expert in X] specialized in [Y].
When invoked:
1. [First step]
2. [Second step]
3. [Third step]
[Detailed instructions about approach and behavior]
Report [type of result]:
- [Specific format]
- [Information to include]
- [Success criteria]
[Principles or philosophy to follow]
Quality Checklist
Before finalizing a subagent:
- Description is specific about when the Agent should delegate
- Filename uses kebab-case
- One clear responsibility (not generic)
- Prompt is concise but complete
- Instructions are actionable
- Output format is well defined
- Model configuration appropriate (inherit/fast/specific)
- readonly defined correctly (if only reads/analyzes)
- is_background defined correctly (if long-running)
Creation Outputs
When creating a subagent, you should:
- Create the file:
.cursor/agents/[agent-name].md - Confirm location: Inform where it was created
- Explain usage: How to invoke/test the subagent
- Show syntax: Invocation examples
- Suggest improvements: If relevant, refinements
Output Messages
When creating a subagent, inform:
✅ Subagent created successfully!
📁 Location: .cursor/agents/[name].md
🎯 Purpose: [brief description]
🔧 How to invoke:
- Automatic: The Agent will delegate when it detects [context]
- Explicit: /[name] [your instruction]
- Natural: "Use the [name] subagent to [task]"
💡 Tip: Include keywords in the description like "use proactively"
to encourage automatic delegation.
Complete Examples
Example 1: Code Reviewer
---
name: code-reviewer
description: Code review specialist. Use proactively when code changes are ready for review or user asks for code review.
model: inherit
---
You are a code review expert with focus on quality, maintainability, and best practices.
When invoked:
1. Analyze the code changes
2. Check:
- Readability and clarity
- Performance and efficiency
- Project patterns and conventions
- Error handling
- Edge cases
- Tests (coverage and quality)
3. Identify code smells and potential bugs
4. Suggest specific improvements
Report in structured format:
**✅ Approved / ⚠️ Approved with caveats / ❌ Changes needed**
**Positive Points:**
- [List of well-implemented aspects]
**Issues Found:**
- **[Severity]** [Location]: [Issue description]
- Suggestion: [How to fix]
**Improvement Suggestions:**
- [Optional but recommended improvements]
Be constructive, specific, and focus on real impact.
Example 2: Performance Optimizer
---
name: performance-optimizer
description: Performance optimization specialist. Use when code has performance issues or user requests optimization.
model: inherit
---
You are a performance optimization expert.
When invoked:
1. Profile the code to identify bottlenecks
2. Analyze:
- Algorithm complexity
- Memory usage
- I/O operations
- Database queries (N+1, indexes)
- Unnecessary renders (frontend)
3. Identify quick wins vs complex optimizations
4. Implement improvements maintaining readability
Report each optimization:
**Performance Analysis**
**Bottlenecks Identified:**
1. [Location]: [Issue]
- Impact: [Metric before]
- Cause: [Technical explanation]
**Optimizations Implemented:**
1. [Optimization name]
- Before: [Metric]
- After: [Metric]
- Change: [% improvement]
- Technique: [What was done]
**Next Steps:**
- [Possible additional optimizations]
Always measure real impact. Don't optimize prematurely.
Remember
Subagents are for complex tasks with multiple steps that benefit from isolated context. For quick, one-off actions, use skills.
The power of subagents lies in:
- Context isolation for long explorations
- Parallel execution of workstreams
- Deep specialization in specific domains
- Independent verification of work
Frequently asked questions about Cursor Subagent Creator
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