Nicolas Cantu
785868b53b
**Motivations:** - Centraliser les fichiers Cursor (rules, skills, agents, commands, hooks) par user et par projet **Root causes:** - N/A **Correctifs:** - N/A **Evolutions:** - desk: rules, skills-cursor, agents, commands, hooks, argv/hooks/mcp.json - ncantu: README placeholder - 4NK_node, algo, builazoo, ia_local, lecoffre_ng, lecoffre_ng_pprod, lecoffre_ng_test: .cursor contents **Pages affectées:** - cursor/desk/, cursor/ncantu/, cursor/<project>/
223 lines
6.3 KiB
Markdown
223 lines
6.3 KiB
Markdown
---
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name: create-subagent
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description: Create custom subagents for specialized AI tasks. Use when the user wants to create a new type of subagent, set up task-specific agents, configure code reviewers, debuggers, or domain-specific assistants with custom prompts.
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disable-model-invocation: true
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---
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# Creating Custom Subagents
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This skill guides you through creating custom subagents for Cursor. Subagents are specialized AI assistants that run in isolated contexts with custom system prompts.
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## When to Use Subagents
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Subagents help you:
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- **Preserve context** by isolating exploration from your main conversation
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- **Specialize behavior** with focused system prompts for specific domains
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- **Reuse configurations** across projects with user-level subagents
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### Inferring from Context
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If you have previous conversation context, infer the subagent's purpose and behavior from what was discussed. Create the subagent based on specialized tasks or workflows that emerged in the conversation.
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## Subagent Locations
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| Location | Scope | Priority |
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|----------|-------|----------|
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| `.cursor/agents/` | Current project | Higher |
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| `~/.cursor/agents/` | All your projects | Lower |
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When multiple subagents share the same name, the higher-priority location wins.
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**Project subagents** (`.cursor/agents/`): Ideal for codebase-specific agents. Check into version control to share with your team.
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**User subagents** (`~/.cursor/agents/`): Personal agents available across all your projects.
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## Subagent File Format
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Create a `.md` file with YAML frontmatter and a markdown body (the system prompt):
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```markdown
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---
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name: code-reviewer
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description: Reviews code for quality and best practices
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---
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You are a code reviewer. When invoked, analyze the code and provide
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specific, actionable feedback on quality, security, and best practices.
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```
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### Required Fields
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| Field | Description |
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|-------|-------------|
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| `name` | Unique identifier (lowercase letters and hyphens only) |
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| `description` | When to delegate to this subagent (be specific!) |
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## Writing Effective Descriptions
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The description is **critical** - the AI uses it to decide when to delegate.
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```yaml
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# ❌ Too vague
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description: Helps with code
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# ✅ Specific and actionable
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description: Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code.
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```
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Include "use proactively" to encourage automatic delegation.
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## Example Subagents
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### Code Reviewer
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```markdown
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---
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name: code-reviewer
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description: Expert code review specialist. Proactively reviews code for quality, security, and maintainability. Use immediately after writing or modifying code.
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---
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You are a senior code reviewer ensuring high standards of code quality and security.
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When invoked:
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1. Run git diff to see recent changes
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2. Focus on modified files
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3. Begin review immediately
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Review checklist:
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- Code is clear and readable
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- Functions and variables are well-named
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- No duplicated code
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- Proper error handling
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- No exposed secrets or API keys
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- Input validation implemented
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- Good test coverage
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- Performance considerations addressed
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Provide feedback organized by priority:
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- Critical issues (must fix)
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- Warnings (should fix)
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- Suggestions (consider improving)
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Include specific examples of how to fix issues.
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```
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### Debugger
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```markdown
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---
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name: debugger
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description: Debugging specialist for errors, test failures, and unexpected behavior. Use proactively when encountering any issues.
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---
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You are an expert debugger specializing in root cause analysis.
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When invoked:
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1. Capture error message and stack trace
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2. Identify reproduction steps
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3. Isolate the failure location
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4. Implement minimal fix
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5. Verify solution works
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Debugging process:
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- Analyze error messages and logs
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- Check recent code changes
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- Form and test hypotheses
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- Add strategic debug logging
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- Inspect variable states
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For each issue, provide:
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- Root cause explanation
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- Evidence supporting the diagnosis
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- Specific code fix
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- Testing approach
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- Prevention recommendations
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Focus on fixing the underlying issue, not the symptoms.
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```
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### Data Scientist
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```markdown
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---
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name: data-scientist
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description: Data analysis expert for SQL queries, BigQuery operations, and data insights. Use proactively for data analysis tasks and queries.
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---
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You are a data scientist specializing in SQL and BigQuery analysis.
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When invoked:
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1. Understand the data analysis requirement
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2. Write efficient SQL queries
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3. Use BigQuery command line tools (bq) when appropriate
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4. Analyze and summarize results
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5. Present findings clearly
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Key practices:
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- Write optimized SQL queries with proper filters
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- Use appropriate aggregations and joins
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- Include comments explaining complex logic
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- Format results for readability
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- Provide data-driven recommendations
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For each analysis:
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- Explain the query approach
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- Document any assumptions
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- Highlight key findings
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- Suggest next steps based on data
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Always ensure queries are efficient and cost-effective.
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```
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## Subagent Creation Workflow
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### Step 1: Decide the Scope
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- **Project-level** (`.cursor/agents/`): For codebase-specific agents shared with team
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- **User-level** (`~/.cursor/agents/`): For personal agents across all projects
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### Step 2: Create the File
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```bash
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# For project-level
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mkdir -p .cursor/agents
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touch .cursor/agents/my-agent.md
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# For user-level
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mkdir -p ~/.cursor/agents
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touch ~/.cursor/agents/my-agent.md
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```
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### Step 3: Define Configuration
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Write the frontmatter with the required fields (`name` and `description`).
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### Step 4: Write the System Prompt
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The body becomes the system prompt. Be specific about:
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- What the agent should do when invoked
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- The workflow or process to follow
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- Output format and structure
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- Any constraints or guidelines
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### Step 5: Test the Agent
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Ask the AI to use your new agent:
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```
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Use the my-agent subagent to [task description]
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```
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## Best Practices
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1. **Design focused subagents**: Each should excel at one specific task
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2. **Write detailed descriptions**: Include trigger terms so the AI knows when to delegate
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3. **Check into version control**: Share project subagents with your team
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4. **Use proactive language**: Include "use proactively" in descriptions
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## Troubleshooting
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### Subagent Not Found
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- Ensure file is in `.cursor/agents/` or `~/.cursor/agents/`
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- Check file has `.md` extension
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- Verify YAML frontmatter syntax is valid
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