four-risks

Four Risks Assessment

Apply Marty Cagan's Four Risks Framework to assess an issue before building.

Works with:

• Linear MCP - Reads issue details and adds assessment as comment
• GitHub MCP - Reads issue details and adds assessment as comment
• Manual - Describe the feature directly

Entry Point

When this skill is invoked, start with:

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 FOUR RISKS ASSESSMENT
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What are you assessing?

  1. Specific issue/feature
     → Provide issue ID or describe the feature

  2. Current sprint issues
     → Assess all issues in current sprint

  3. Quick risk check
     → Fast assessment on something you're considering

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What This Does

Evaluates a feature/project against the four critical risks:

• Value: Will customers use/buy this?
• Usability: Can users figure it out?
• Feasibility: Can we build it?
• Viability: Does it work for our business?

Usage

/four-risks [issue-id]

Examples:

• /four-risks ENG-245 - Assess specific issue
• /four-risks --current-sprint - Assess all current sprint issues
• /four-risks --add-comment - Add assessment as Linear comment

What Happens

1. Fetches issue details from Linear or GitHub (if MCPs configured)
2. Applies Four Risks framework:
   • Analyzes issue description
   • Asks clarifying questions if needed
   • Assesses each risk dimension
3. Returns risk assessment with:
   • Risk level for each dimension (High/Medium/Low)
   • Key questions to de-risk
   • Recommended discovery activities
4. Optionally adds comment to source issue with assessment (if using Linear)

Example Output

🎯 Four Risks Assessment: [ENG-245] AI-powered email composer

📊 RISK SUMMARY
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1️⃣ VALUE RISK: 🔴 HIGH
   Will customers use/buy this?

   ⚠️  Concerns:
   - No customer interviews validating demand
   - Competitive AI email tools exist (Grammarly, Jasper)
   - Unclear differentiation

   ✅ To de-risk:
   - Run 10 customer interviews about email pain points
   - Test prototype with 5 users
   - Validate willingness to pay

2️⃣ USABILITY RISK: 🟡 MEDIUM
   Can users figure it out?

   ⚠️  Concerns:
   - AI output needs review UX
   - Tone/voice customization complexity

   ✅ To de-risk:
   - Create clickable prototype
   - Run usability tests with 5 users
   - Test with non-technical users

3️⃣ FEASIBILITY RISK: 🟢 LOW
   Can we build it?

   ✅ Confidence:
   - Team has AI integration experience
   - OpenAI API well-documented
   - Spike completed successfully

   ⚠️ Minor concerns:
   - Inference costs at scale (needs modeling)

4️⃣ VIABILITY RISK: 🟡 MEDIUM
   Does it work for our business?

   ⚠️  Concerns:
   - Unit economics unclear (AI costs)
   - Legal review needed for AI-generated content
   - Competitive differentiation weak

   ✅ To de-risk:
   - Model costs at 10K, 100K, 1M emails/month
   - Legal review of AI content liability
   - Define unique value prop

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🎯 RECOMMENDATION: DO NOT BUILD YET

Highest risk: VALUE (customers may not want/pay for this)

📋 Discovery Plan:
Week 1: Customer interviews (10 users)
Week 2: Build throwaway prototype
Week 3: Usability testing (5 users)
Week 4: Unit economics modeling

Only proceed if:
✓ 60%+ of interviews validate strong need
✓ Prototype test shows clear value
✓ Unit economics support freemium model

Integration Options

With Linear MCP: Automatically fetches Linear issue details and can add assessment as comment.

With GitHub MCP: Automatically fetches GitHub issue details and can add assessment as comment.

Manual mode: Describe the feature and the command will assess it:

Run a four risks assessment on this feature: [describe feature]

Learn More

See the full Four Risks framework at: frameworks/discovery/four-risks.md

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Framework: Marty Cagan (SVPG) Best for: Pre-build validation, discovery planning, reducing waste AI-era adaptation: Prototype to test risks in hours, not weeks