Cursor Subagent Creator

Cursor Subagent Creator

Overview

This public intake copy packages packages/skills-catalog/skills/(creation)/cursor-subagent-creator from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Cursor Subagent Creator You are an expert in creating Subagents following Cursor's best practices.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: What are Subagents?, Subagent Structure, Field Configuration, Common Subagent Patterns, Using Subagents, Resuming Subagents.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• 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
• Use when the request clearly matches the imported source intent: Creates Cursor-specific AI subagents with isolated context for complex multi-step workflows. Use when creating subagents for Cursor editor specifically, following Cursor's patterns and directories (.cursor/agents/).....

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. What specific responsibility does the subagent have?
2. Why does it need isolated context?
3. Does it involve multiple complex steps?
4. Does it require deep specialization?
5. Project: .cursor/agents/agent-name.md - project-specific
6. User: ~/.cursor/agents/agent-name.md - all projects
7. Use kebab-case (words-separated-by-hyphens)

Imported Workflow Notes

Imported: 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 consistency
• fast: 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:

1. Identity: "You are an [expert]..."
2. When invoked: Context of use
3. Process: Specific steps to follow
4. Expected output: Format and content of the result
5. 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

Imported: 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

Examples

Example 1: Ask for the upstream workflow directly

Use @cursor-subagent-creator to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @cursor-subagent-creator against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @cursor-subagent-creator for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @cursor-subagent-creator using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: 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: /fix to fix linter errors

Imported: 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.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• 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

Imported Operating Notes

Imported: 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.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(creation)/cursor-subagent-creator, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-cold-outreach - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-sdr - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

Imported Reference Notes

Imported: 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]

Imported: Field Configuration

Imported: 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

Imported: 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.

Imported: 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/.

Imported: Performance and Cost

Subagents have trade-offs:

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

Imported: 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]

Imported: 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)

Imported: Creation Outputs

When creating a subagent, you should:

1. Create the file: .cursor/agents/[agent-name].md
2. Confirm location: Inform where it was created
3. Explain usage: How to invoke/test the subagent
4. Show syntax: Invocation examples
5. Suggest improvements: If relevant, refinements

Imported: 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.

Imported: 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