Ralphie Spec Generation (V2 Format)

Generate comprehensive specs using the V2 format with task IDs, status tracking, and size-based budgeting through structured interviews with the user.

When to Use

• User is present and can answer questions
• Complex projects needing clarification
• When requirements need discussion

80/20 Integration

This skill works with Ralphie's compound engineering features:

• Research Phase (optional): If Ralphie ran ralphie spec with research, findings are in .ralphie/research-context.md
• Analysis Phase (optional): After spec generation, Ralphie may analyze for gaps (saved to .ralphie/analysis.md)
• Interactive Review: Since user is present, gaps are discussed and resolved in real-time

The skill checks for these files and incorporates findings, but doesn't trigger research/analysis itself (that's the CLI's job).

Output Location

Write specs to .ralphie/specs/active/{name}.md:

• Generate a kebab-case filename from the description
• Example: "user authentication" → .ralphie/specs/active/user-authentication.md
• Only one spec allowed in .ralphie/specs/active/ at a time

Workflow

Check Research → Interview User → Explore Codebase → Draft Tasks → Size Review → Write V2 Spec → Check Analysis → Review Gaps → Present for Approval

80/20 Philosophy: Research and analysis are part of the 80% (planning). Incorporate their findings to make specs thorough.

Step 0: Check for Research Context (Optional)

Before interviewing, check if Ralphie ran research:

cat .ralphie/research-context.md 2>/dev/null

If the file exists:

• Read it to understand codebase patterns, conventions, and framework best practices
• Use findings to inform your interview questions
• Reference research in Context section of final spec

If it doesn't exist, skip this step—interview will gather requirements directly from user.

Step 1: Interview User

Use AskUserQuestion to gather requirements in batches of 2-4 questions.

Batch 1: Project Foundation

AskUserQuestion({
  questions: [
    {
      question: "What type of project is this?",
      header: "Type",
      multiSelect: false,
      options: [
        { label: "CLI tool", description: "Command-line application" },
        { label: "Web API", description: "REST/GraphQL backend" },
        { label: "Library", description: "Reusable package" },
        { label: "Full-stack", description: "Frontend + backend" }
      ]
    },
    {
      question: "What language/framework?",
      header: "Stack",
      multiSelect: false,
      options: [
        { label: "TypeScript/Node.js (Recommended)", description: "Modern JS with types" },
        { label: "Python", description: "Great for data, ML, scripting" },
        { label: "Go", description: "Fast, good for systems" },
        { label: "Rust", description: "Memory-safe systems" }
      ]
    }
  ]
})

Batch 2: Requirements

AskUserQuestion({
  questions: [
    {
      question: "What is the primary use case?",
      header: "Use Case",
      multiSelect: false,
      options: [
        { label: "Internal tool", description: "Used by your team" },
        { label: "Public product", description: "External users" },
        { label: "Library/SDK", description: "For other developers" },
        { label: "Learning/experiment", description: "Personal project" }
      ]
    },
    {
      question: "What's the testing expectation?",
      header: "Testing",
      multiSelect: false,
      options: [
        { label: "Unit tests only (Recommended)", description: "Test individual functions" },
        { label: "Unit + Integration", description: "Test components together" },
        { label: "Full coverage", description: "Unit + Integration + E2E" },
        { label: "Minimal/none", description: "Prototype or spike" }
      ]
    }
  ]
})

Batch 3: Scope

Follow up based on previous answers:

• "What external services or APIs does this integrate with?"
• "Are there auth requirements? (none / basic / OAuth)"
• "What's the priority: MVP or full feature set?"

Interview Tips

• If answers are vague, ask for specific examples
• Clarify: "Is X a must-have or nice-to-have?"
• Don't proceed until core requirements are clear

Step 2: Explore Codebase

If this is a brownfield project (existing code), explore it:

2.1 Check for Existing Code

ls package.json pyproject.toml go.mod Cargo.toml 2>/dev/null

2.2 Understand Structure

Glob("src/**/*")
Glob("lib/**/*")

2.3 Read Context

• README.md - Project description
• CLAUDE.md - AI instructions
• specs/lessons.md - Past learnings to apply
• Main entry points

2.4 Detect Patterns

Grep("export function", path="src/")
Grep("(describe|test)\\(", path="tests/")

Step 3: Draft Tasks with Sizes

Before writing the spec, draft tasks and estimate sizes.

Size Guidelines

Draft Format

Create a mental or written draft:

T001: Setup project structure [S]
T002: Implement core data model [M]
T003: Add authentication [L]
T004: Create API endpoints [M]
T005: Add tests [M]

Step 4: Size Review with User

Present the task breakdown and ask for size confirmation:

AskUserQuestion({
  questions: [
    {
      question: "Here's my task breakdown with size estimates. Do these sizes seem accurate?",
      header: "Sizes",
      multiSelect: false,
      options: [
        { label: "Looks good", description: "Proceed with these estimates" },
        { label: "Some too small", description: "I'll bump up specific tasks" },
        { label: "Some too large", description: "Let's split into smaller tasks" },
        { label: "Discuss changes", description: "I have specific feedback" }
      ]
    }
  ]
})

Show the summary:

## Proposed Tasks

| ID | Task | Size | Points |
|----|------|------|--------|
| T001 | Setup project structure | S | 1 |
| T002 | Implement core data model | M | 2 |
| T003 | Add authentication | L | 4 |
| T004 | Create API endpoints | M | 2 |
| T005 | Add tests | M | 2 |

**Total: 11 points** (~3 iterations at 4 pts/iteration)

Adjust based on feedback before writing the spec.

Step 5: Write V2 Spec

Format

# Feature Name

Goal: One-sentence description of what this achieves when complete.

## Context

Background information for the agent implementing this spec:
- What problem does this solve?
- What existing code/patterns should it follow?
- Any constraints or requirements?
- (If research was conducted) Key findings from codebase research

## Tasks

### T001: First task title
- Status: pending
- Size: S

**Deliverables:**
- What to build (WHAT, not HOW)
- Another deliverable

**Verify:** `npm test -- something`

---

### T002: Second task title
- Status: pending
- Size: M

**Deliverables:**
- Deliverable description
- Another deliverable

**Verify:** `curl localhost:3000/api` returns 200

---

### T003: Third task title
- Status: pending
- Size: S

**Deliverables:**
- Deliverable description

**Verify:** `npm run type-check` passes

---

## Acceptance Criteria

- WHEN user does X, THEN Y happens
- WHEN condition Z, THEN expected outcome

## Notes

<!-- AI updates this section during implementation -->

### Interview Summary
- Project type: [from interview]
- Stack: [from interview]
- Key decisions: [from interview]

V2 Format Rules

Content Rules

Step 6: Check for Analysis (Optional)

After writing the spec, check if Ralphie ran analysis:

cat .ralphie/analysis.md 2>/dev/null

If the file exists:

• Read it to identify any gaps, edge cases, or missing elements
• Analysis may include:
  • Missing error handling scenarios
  • Unclear user flows
  • Missing verify commands or test coverage
  • Tasks that should be split
  • Integration points not addressed

If gaps found:

1. Review each concern
2. Decide with user whether to:
   • Revise spec to address gaps
   • Accept as-is (document why in spec Notes)
   • Add follow-up tasks

If analysis doesn't exist, skip to Step 7.

Step 7: Present for Approval

Write spec to .ralphie/specs/active/{name}.md and present summary:

## Spec Created

`.ralphie/specs/active/{name}.md` created with X tasks (Y size points total).

### Task Summary
| ID | Task | Size |
|----|------|------|
| T001 | First task | S |
| T002 | Second task | M |
...

### Estimated Effort
- Total points: Y
- Iterations needed: ~Z (at 4 pts/iteration)

### Research Findings (if available)
- [Key pattern 1 from research-context.md]
- [Key pattern 2 from research-context.md]

### Analysis Results (if available)
- ✅ All critical flows covered
- ⚠️ [Gap 1] - [How we addressed it or why it's acceptable]
- ⚠️ [Gap 2] - [How we addressed it or why it's acceptable]

### Key Decisions from Interview
- [Decision 1]
- [Decision 2]

Please review the spec. Ready to start with `ralphie run`?

The user reviews and approves. Since user is present, analysis gaps are discussed and resolved during the interview—no additional automated review needed.

Quick Reference