spec-init

SKILL: spec-init

Overview

Unified skill that guides spec creation through structured, interactive process.

Wraps these existing skills:

• context-compressor (progressive disclosure mode for requirements gathering)
• plan-generator (plan from spec)

Workflow

1. Type Detection

Question: "What are you building?"

Auto-detect from description:

• Feature: "Build X functionality" → type: feature
• Bug: "Fix X issue" → type: bug
• Chore: "Update X component" → type: chore
• Refactor: "Reorganize X" → type: refactor
• Docs: "Document X" → type: docs

2. Progressive Disclosure v2 (Adaptive, 5-7 questions)

Invoke context-compressor (progressive disclosure mode) with adaptive algorithm:

const { AdaptiveQuestioner } = require('.claude/lib/utils/adaptive-discloser.cjs');
const { ContextAccumulator } = require('.claude/lib/utils/context-accumulator.cjs');

// Determine domain from detected type
const domainMap = {
  feature: 'general',
  bug: 'debugging',
  chore: 'general',
  refactor: 'architecture',
  docs: 'documentation',
};
const domain = domainMap[detectedType] || 'general';

const aq = new AdaptiveQuestioner(domain);
const ca = new ContextAccumulator();

let history = [];
let questionCount = 0;

while (questionCount < 7) {
  const context = ca.getContext();
  const result = await aq.getNextQuestion(context, history);

  // Check if we should stop early
  const readiness = await aq.detectOptimalStop(history, context);
  if (readiness.shouldStop) {
    break;
  }

  // Ask the question
  const answer = await AskUserQuestion({ question: result.question });

  // Store answer with metadata
  ca.addAnswer(result.question, answer, { domain, priority: 'HIGH' });
  history.push({ question: result.question, answer });

  questionCount++;
}

// Summary from accumulated context
const summary = ca.buildSummary();

Key Improvements over v1:

• Adaptive questioning (skips redundant questions)
• Context-aware (learns from answers)
• Memory-integrated (leverages learnings.md)
• Optimal stopping (5-7 questions typical, down from 10-12)
• Quality scoring (detects when ready for spec generation)

3. Spec Template Generation

Auto-populate spec from answers:

# SPEC: [Feature Name]

## 1. Overview

**Title**: [From question 1]
**Type**: [Detected type]
**Objective**: [User summary]

**User Story**: As a [user type], I want [capability], so that [benefit]

**Acceptance Criteria**: [From question 5]

## 2. Problem Statement

- **Current State**: [From question 1 answers]
- **Pain Points**: [Extracted from answers]
- **Impact**: [Quantified if possible]

## 3. Proposed Solution

- **Approach**: [From user input]
- **Key Features**: [From answers]
- **Scope**: [What's in/out]

## 4. Implementation Approach

- **Phase 1**: [Design/spike if needed]
- **Phase 2**: [Core implementation]
- **Phase 3**: [Testing]
- **Phase 4**: [Documentation]

## 5. Success Metrics

- **Quantitative**: [From question 3]
- **Qualitative**: [User satisfaction]
- **Timeline**: [From question 4]

## 6. Effort Estimate

- **Design**: 1 day
- **Implementation**: 3 days
- **Testing**: 2 days
- **Documentation**: 1 day
- **Total**: 7 days

## 7. Dependencies

- **Required**: [Extracted from context]
- **Blocking**: [What must complete first]
- **Risk**: [Key risks identified]

## 8. Acceptance Criteria Checklist

- [ ] Feature implemented per spec
- [ ] All tests passing
- [ ] Documentation updated
- [ ] No breaking changes
- [ ] Performance targets met

4. Validation

Validate spec against schema:

• Validate spec completeness inline
• Check: all required sections present
• Check: at least 3 acceptance criteria
• Check: effort estimate in days

5. Plan Suggestion

After spec approved:

• Suggest: "Ready for planner to create plan?"
• If yes: Show Skill({ skill: "plan-generator", args: { specPath: "..." } })
• If no: Allow editing spec

6. Storage

Save spec to:

.claude/context/artifacts/specs/[feature-name]-spec-YYYYMMDD.md

Track metadata:

• trackId: auto-generated
• type: detected
• status: "new"
• created_at: timestamp

Usage Examples

Example 1: Quick Feature

User: "I want to add dark mode to the UI"

spec-init workflow:
1. Detect: type = "feature"
2. Ask: 5 questions about dark mode
3. User answers in <5 minutes
4. Generate spec
5. Validate against schema
6. Store and offer plan generation

Example 2: Bug Fix

User: "There's a memory leak in the scheduler"

spec-init workflow:
1. Detect: type = "bug"
2. Ask: 5 questions (reproduce steps, impact, etc)
3. Generate bug fix spec
4. Suggest acceptance criteria
5. Ready for planner

Output

• Generated spec markdown (saved)
• Track metadata JSON
• Plan generation suggestion
• Next steps guidance

Integration Points

• context-compressor (progressive disclosure mode for requirements gathering)
• plan-generator (next step)
• track-metadata schema (metadata)

Iron Laws

1. NEVER ask more than 7 clarifying questions — detect optimal stopping and generate the spec
2. ALWAYS detect the intent type (feature/bug/chore/refactor/docs) before any questioning
3. NEVER generate a spec without validating all required sections are populated
4. ALWAYS save the spec to .claude/context/artifacts/specs/ with the correct naming convention
5. NEVER skip track metadata — every spec must include trackId, type, status, and created_at

Anti-Patterns

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

• New pattern -> .claude/context/memory/learnings.md
• Issue found -> .claude/context/memory/issues.md
• Decision made -> .claude/context/memory/decisions.md

ASSUME INTERRUPTION: If it's not in memory, it didn't happen.