Bootstrap Product Skill

Purpose: Transform minimal product briefings into rich, research-backed product management artifacts that are market-viable and technically sound.

Key Innovation: Domain-expert research agent conducts comprehensive research BEFORE questioning, reducing user burden from 17 questions to typically 8-12 questions while delivering higher-quality, validated recommendations.

Process Overview

1. Accept Product Briefing
   ↓
2. Conduct Domain-Expert Research (NEW)
   ├─ Context7: Technology documentation
   ├─ WebSearch: Market/architecture/security
   ├─ WebFetch: Deep-dive resources
   └─ Domain: Scientific/industry research
   ↓
3. Synthesize Research Report
   ↓
4. Ask Research-Informed Questions (8-12 instead of 17)
   ↓
5. Confirm Understanding (with research context)
   ↓
6. Generate Research-Enriched Artifacts (4 files)
   ↓
7. Update .context/ with Research Summary
   ↓
8. Provide Completion Summary with Citations

Step 1: Accept & Analyze Briefing

Input: Product briefing from user (can be minimal - e.g., "Build a collaborative document editor")

Actions:

• •Parse briefing for core concept, domain, technology hints
• •Extract research keywords: product type, domain, use case, tech stack clues
• •Identify what's missing that research can help fill

Example:

User: "Build a collaborative document editor"
→ Research keywords: "collaborative editing", "document editor", "real-time collaboration"
→ Technology areas: frontend frameworks, WebSocket libraries, rich text editors
→ Domain areas: market size, competitors (Google Docs, Notion), architecture patterns

Step 2: Conduct Domain-Expert Research

CRITICAL: Research happens BEFORE questioning to inform smarter questions and pre-populate artifacts.

2.1 Technology Documentation Research (Context7)

Purpose: Identify best practices and recommended technologies

Process:

1. •Identify 3-5 relevant technology candidates from briefing
2. •For each technology:
   code

   resolve-library-id(
     query="[Technology description]",
     libraryName="[framework name]"
   ) → libraryId
   
   query-docs(
     libraryId="[returned ID]",
     query="best practices for [specific use case]"
   ) → Documentation findings
   

3. •Document findings with library IDs and queries used

Limit: 3-5 Context7 queries maximum

2.2 Architecture Pattern Research (WebSearch + WebFetch)

Purpose: Research proven architecture patterns for this domain

Process:

1. •WebSearch for architecture patterns (5-8 queries):
   • •"[domain/use case] architecture patterns 2026"
   • •"[domain] scalability best practices 2026"
   • •"microservices vs monolith [use case] 2026"
2. •WebFetch 2-3 key resources:
   • •Architecture whitepapers
   • •Case studies from similar products
   • •Implementation guides

Limit: 5-8 WebSearch queries, 2-3 WebFetch resources

2.3 Security & Compliance Research (WebSearch + WebFetch)

Purpose: Identify regulatory requirements and security best practices

Process:

1. •WebSearch for compliance (5-8 queries):
   • •"GDPR compliance [domain] applications 2026"
   • •"HIPAA requirements [domain] 2026"
   • •"SOC2 compliance SaaS applications 2026"
   • •"OWASP top 10 [domain] security 2026"
2. •WebFetch official compliance documentation

Limit: 5-8 compliance/security searches

2.4 Domain Knowledge Research (WebSearch + WebFetch)

Purpose: Understand market, competitors, and domain-specific insights

Process:

1. •WebSearch for market intelligence (8-10 queries):
   • •"[product type] market size 2026"
   • •"[domain] industry trends 2026"
   • •"[use case] competitive landscape"
   • •"key competitors [product type]"
2. •WebFetch 2-4 resources:
   • •Market research reports
   • •Academic papers (if applicable)
   • •Industry analyses

Limit: 8-10 market/domain searches, 2-4 WebFetch resources

2.5 Research Synthesis

Output: Structured research report containing:

## Research Report

### Technology Research (Context7)
- [Library 1]: [Key findings]
- [Library 2]: [Key findings]
- Recommendation: [Suggested tech stack]

### Architecture Research
- Pattern recommendation: [e.g., Monolith for MVP, microservices later]
- Scalability approach: [Key patterns found]
- Case studies: [Similar products]

### Security & Compliance
- Required standards: [GDPR, HIPAA, SOC2, etc.]
- Security measures: [OWASP compliance, encryption, etc.]

### Domain Knowledge
- Market size: [TAM from research]
- Key competitors: [List with strengths/weaknesses]
- Industry trends: [Relevant trends]

### Research Gaps (Need User Input)
- [Question 1 that research couldn't answer]
- [Question 2 that requires user preference]
- [Question 3 that needs validation]

Step 3: Ask Research-Informed Questions

Strategy:

• •Review research report before asking ANY questions
• •Skip questions where research provides clear answers
• •Ask validation questions to confirm research findings
• •Focus on user preferences, constraints, and goals that research cannot determine
• •Reduce from 17 questions to typically 8-12 questions

Question Categories (see full command file for complete question framework):

1. •Product Essence (4 questions) - May be informed by domain research
2. •Market Context (4 questions) - May have data from market research
3. •Technical Constraints (3 questions) - Research identifies compliance needs
4. •Execution Context (3 questions) - Research informs timeline estimates
5. •Product Scope (3 questions) - Research identifies must-have features

Example (Collaborative Document Editor):

Research found:
- Market size: $5B TAM
- Competitors: Google Docs, Notion, Confluence
- Tech stack: React + WebSocket recommended
- Compliance: GDPR for EU customers
- Architecture: Operational Transform or CRDT patterns

Questions SKIPPED:
✗ "What's the market size?" (research found: $5B)
✗ "Who are competitors?" (research identified 3 major players)
✗ "Technology preferences?" (research suggests React + Socket.io)

Questions ASKED:
✓ "Do you need GDPR compliance?" (validate research finding)
✓ "What's your differentiation vs Google Docs?" (user vision)
✓ "Target scale?" (informs architecture choice)
✓ "MVP timeline?" (user constraint)
✓ "Team size?" (user constraint)

Result: 8 targeted questions instead of 17 generic ones

Step 4: Confirm Understanding

Present research-enhanced confirmation:

Let me confirm what I understand about your product:

**Product**: [Name/description]
**Core Problem**: [2-3 sentences]
**Target Users**: [User persona]
**Market Context**: [Size and competitors FROM RESEARCH]
**Key Differentiation**: [Unique value]
**Technical Approach**: [Architecture informed by Context7 research]
**Compliance Requirements**: [GDPR, HIPAA, SOC2 identified FROM RESEARCH]
**MVP Timeline**: [Timeline]
**Success Metrics**: [2-4 metrics]

**Research Conducted**:
- Technology: [Context7 libraries queried]
- Market: [Key findings]
- Security: [Standards identified]
- Domain: [Insights]

Is this correct? Please confirm or provide corrections.

Step 5: Generate Research-Enriched Artifacts

Generation Order (dependency-driven):

5.1 product.md (150-250 lines)

• •Product vision with market research citations
• •Competitive landscape FROM RESEARCH
• •Success metrics with industry benchmarks FROM RESEARCH

5.2 roadmap.md (200-250 lines)

• •Phases informed by architecture research
• •Timeline realistic based on technology research

5.3 architecture.md (200-300 lines)

• •Technology stack backed by Context7 documentation
• •Architecture pattern from research
• •Security measures from compliance research
• •EXTENSIVE Context7 citations

5.4 adr.md (100-150 lines)

• •ADR-001: Technology Stack (Context7-backed)
• •ADR-002: Architecture Pattern (research-validated)
• •ADR-003: Database Choice (comparative research)
• •ADR-004: Security & Compliance (regulatory research)
• •All ADRs include research citations

Progress Indicators:

Generating research-enriched artifacts...
✓ Created product.md (187 lines) - with market research
✓ Generated roadmap.md (223 lines) - with architecture research
✓ Designed architecture.md (298 lines) - with Context7 references
✓ Documented adr.md (156 lines) - with research-justified decisions

Step 6: Update .context/

notes.md (< 150 lines)

Add Product Bootstrap Summary including:

• •Product overview
• •Research Conducted section
• •Key Research Findings
• •Research Sources Summary
• •Key docs references

changelog.md (< 70 lines)

Add bootstrap entry including:

• •Decisions (7 key decisions)
• •Research Conducted section
• •Artifacts generated WITH research annotations
• •Rationale with research backing

handoff.md

Create comprehensive handoff including:

• •Product artifacts generated
• •Information gathered
• •Research Conducted section (detailed)
• •Important decisions
• •Next steps

Step 7: Provide Summary

Summary Format:

## Product Bootstrapping Complete!

### Product Overview
- **Name**: [Name]
- **Vision**: [One sentence]
- **Target**: [User segment]
- **MVP Timeline**: [Timeline]

### Generated Artifacts
- product.md (X lines) - with market research citations
- roadmap.md (X lines) - with architecture research
- architecture.md (X lines) - with Context7 references
- adr.md (X lines) - 4 ADRs with research justification

### Research Conducted
**Context7**: [X] libraries documented
**WebSearch**: [Y] searches (market/architecture/security)
**WebFetch**: [Z] deep-dive resources

**Impact**:
- Questions reduced from 17 to [actual]
- All decisions research-backed
- Full citation traceability

### Next Steps
1. Review artifacts and research citations
2. Validate findings against domain expertise
3. Begin MVP development planning

Important Guidelines

DO:

• •✅ Conduct research BEFORE asking questions
• •✅ Skip questions that research confidently answered
• •✅ Include research citations in ALL artifacts
• •✅ Use Context7 for all technology decisions
• •✅ Cite specific library IDs (/org/project format)
• •✅ Keep .context/ files under 500 lines
• •✅ Provide research sources summary

DON'T:

• •❌ Ask all 17 questions if research answered some
• •❌ Make technology recommendations without Context7 backing
• •❌ Skip research phase to save time
• •❌ Omit research citations from artifacts
• •❌ Exceed research query limits (causes token bloat)
• •❌ Generate artifacts without research validation

Research Query Limits (CRITICAL):

• •Context7: 3-5 libraries max
• •WebSearch: 5-8 per category (market, architecture, security)
• •WebFetch: 2-4 deep resources max
• •Enforce these to prevent token bloat and API overuse

Success Criteria

After execution:

• •✅ 4 comprehensive product files generated (600-1000 lines total)
• •✅ All artifacts include research citations
• •✅ Technology decisions backed by Context7 documentation
• •✅ Architectural decisions validated by industry research
• •✅ Compliance requirements identified proactively
• •✅ Questions reduced to 8-12 based on research coverage
• •✅ .context/ files updated with research summary
• •✅ All .context/ files under 500 lines
• •✅ Full citation traceability for all recommendations

Templates

Note: This skill uses abbreviated templates. For complete templates with all sections and examples, see:

• •.claude/commands/bootstrap-product.md (full command file, ~2000 lines)

The full command file contains:

• •Detailed question framework (all 17 questions with research annotations)
• •Complete artifact templates (product.md, roadmap.md, architecture.md, adr.md)
• •Research integration instructions
• •Example execution flows

Command Version: For explicit invocation, use /bootstrap-product [briefing] Skill Version: This file - activated by semantic triggers for product planning conversations