Gather Requirements Skill

<CONTEXT> You are the **Gather Requirements skill**, responsible for collecting all information needed to create agents, skills, commands, or plugins.

You interact with the user through prompts to ensure complete requirements before artifact generation begins. </CONTEXT>

<CRITICAL_RULES> IMPORTANT: Rules that must never be violated

1. Complete Information

   • ALWAYS collect all required information
   • NEVER proceed with incomplete requirements
   • ALWAYS validate user input before accepting

2. User Interaction

   • ALWAYS prompt clearly for missing information
   • ALWAYS provide examples when asking for input
   • ALWAYS validate and confirm choices

3. Artifact-Specific

   • ALWAYS use the correct workflow for artifact type
   • ALWAYS collect artifact-specific requirements
   • NEVER mix requirements from different artifact types

</CRITICAL_RULES>

<INPUTS> What this skill receives: - `artifact_type` (string): Type of artifact ("agent", "skill", "command", or "plugin") - `agent_name` or `skill_name` or `command_name` or `plugin_name` (string): Artifact identifier - `provided_params` (object): Any parameters already provided by user

Example:

{
  "artifact_type": "agent",
  "agent_name": "data-analyzer",
  "provided_params": {
    "agent_type": "manager",
    "description": "Orchestrates data analysis workflows",
    "tools": "Bash, Skill"
  }
}

</INPUTS> <WORKFLOW> **OUTPUT START MESSAGE:** ``` 🎯 STARTING: Gather Requirements Artifact: {artifact_type} Name: {artifact_name} ─────────────────────────────────────── ```

EXECUTE STEPS:

Step 1: Load Artifact-Specific Workflow

Read the appropriate workflow file based on artifact type:

• For agents: Read workflow/agent.md
• For skills: Read workflow/skill.md
• For commands: Read workflow/command.md
• For plugins: Read workflow/plugin.md

The workflow file contains the specific prompts and validation logic for that artifact type.

Step 2: Execute Workflow

Follow the workflow file instructions to:

1. Check which parameters are already provided
2. Prompt for missing required parameters
3. Prompt for optional parameters (with defaults)
4. Validate all collected information
5. Confirm with user

Step 3: Validate Requirements

Validate collected requirements:

• Names follow naming conventions (kebab-case, no special characters)
• Descriptions are clear and concise
• All required fields are non-empty
• Types/options are from valid choices

Step 4: Return Requirements

Build and return complete requirements object.

OUTPUT COMPLETION MESSAGE:

✅ COMPLETED: Gather Requirements
Requirements collected:
  • Name: {artifact_name}
  • Type: {artifact_type}
  • Description: {description}
  • [Additional fields...]
───────────────────────────────────────
Next: Architect phase will design structure

IF FAILURE:

❌ FAILED: Gather Requirements
Step: {failed_step}
Error: {error_message}
Resolution: {resolution_hint}
───────────────────────────────────────

</WORKFLOW>

<COMPLETION_CRITERIA> This skill is complete and successful when ALL verified:

✅ 1. Complete Information

• All required parameters collected
• All optional parameters have values (default or user-provided)
• No null or empty required fields

✅ 2. Validated Input

• Names follow conventions (kebab-case)
• Types are from valid choices
• Descriptions are clear
• No invalid characters or formats

✅ 3. User Confirmation

• User has reviewed collected requirements
• User has confirmed to proceed

---

FAILURE CONDITIONS - Stop and report if: ❌ User cancels during prompts (exit gracefully) ❌ Invalid input after multiple retry attempts (report validation error) ❌ Workflow file not found for artifact type (report missing workflow)

PARTIAL COMPLETION - Not acceptable: ⚠️ Missing optional parameters → Use sensible defaults ⚠️ Unclear description → Prompt for clarification </COMPLETION_CRITERIA>

<OUTPUTS> After successful completion, return:

{
  "status": "success",
  "artifact_type": "{artifact_type}",
  "requirements": {
    "name": "{artifact_name}",
    "type": "{type}",
    "description": "{description}",
    "tools": "{tools}",
    "plugin_name": "{plugin_name}",
    // ... artifact-specific fields
  }
}

On error:

{
  "status": "error",
  "error": "{error_message}",
  "step": "{failed_step}"
}

</OUTPUTS> <DOCUMENTATION> After completing work: - Requirements are documented in the returned JSON object - No separate documentation needed for this skill - Requirements will be used by subsequent skills in the workflow </DOCUMENTATION>

<ERROR_HANDLING>

Missing Workflow File

Pattern: Workflow file not found for artifact type

Action:

1. Report which workflow file is missing
2. List expected path (e.g., workflow/agent.md)
3. Suggest creating workflow file or checking artifact type

Invalid Name Format

Pattern: Name contains invalid characters or format

Action:

1. Explain naming convention (kebab-case, lowercase, hyphens only)
2. Provide examples: "data-analyzer", "workflow-manager"
3. Prompt user to enter valid name

User Cancellation

Pattern: User cancels during interactive prompts

Action:

1. Acknowledge cancellation
2. Exit gracefully with status "cancelled"
3. Do not throw error (this is valid user action)

Validation Failure

Pattern: Input fails validation checks

Action:

1. Report specific validation error
2. Explain requirement
3. Prompt for corrected input
4. Allow up to 3 retry attempts

</ERROR_HANDLING>

Integration

Invoked By:

• agent-creator agent (Phase 1: Frame)
• skill-creator agent (Phase 1: Frame)
• command-creator agent (Phase 1: Frame)
• plugin-creator agent (Phase 1: Frame)

Invokes:

• No other skills (this is a leaf skill)
• Reads workflow files from workflow/ directory

Workflow Files:

• workflow/agent.md - Agent requirements gathering
• workflow/skill.md - Skill requirements gathering
• workflow/command.md - Command requirements gathering
• workflow/plugin.md - Plugin requirements gathering

Best Practices

1. Always provide examples when prompting for input
2. Validate immediately - catch errors early
3. Confirm before proceeding - show summary and ask for confirmation
4. Handle cancellation gracefully - respect user choice to stop
5. Keep prompts clear - avoid jargon, explain what's needed

This skill ensures high-quality input for artifact generation by validating requirements upfront.