/product

Common Usage Patterns

This document shows real-world workflows for using the Product Agent skill effectively.

Pattern 1: Quick Idea Validation

Use Case: You have a single app idea and want quick validation before investing time.

What to do: Provide the idea and let the skill run its analysis.

What to Check:

1. severity_score — Is it 6+?
2. opportunity — Does it say "STRONG" or "MODERATE"?
3. recommendation — Does it say "BUILD" or "PROCEED WITH CAUTION"?

Decision Making:

• Score 7+, STRONG opportunity, BUILD verdict — Green light
• Score 4-6, MODERATE opportunity, CAUTION verdict — Needs differentiation strategy
• Score <4, WEAK opportunity, DON'T BUILD verdict — Red light

Pattern 2: Comparing Multiple Ideas

Use Case: You have 3-5 ideas and want to pick the best one.

What to do: Run discovery on each idea, then compare:

• Severity scores (higher = better)
• Opportunity assessments (STRONG > MODERATE > WEAK)
• Recommendation verdicts
• Current solutions (fewer/weaker competitors = better)

Example:

Idea A: Severity 7/10, STRONG, BUILD
Idea B: Severity 4/10, WEAK, DON'T BUILD
Idea C: Severity 6/10, MODERATE, PROCEED WITH CAUTION

Winner: Idea A (clear green light)

Pattern 3: Deep Market Analysis

Use Case: You're serious about an idea and want comprehensive analysis.

What to do:

1. Run product-agent discovery with detailed context (platform, target user)
2. Follow up with competitive-analysis skill for competitor deep-dive
3. Follow up with market-research skill for TAM/SAM/SOM

Review Checklist:

• Read complete recommendation (all paragraphs)
• Analyze all pain_points (are they real?)
• Research each item in current_solutions (visit websites)
• Verify opportunity assessment (do independent research)
• Consider frequency (daily = good, weekly = less urgent)

Pattern 4: Iterative Refinement

Use Case: Initial analysis suggests "don't build", but you want to explore pivots.

Example flow:

Initial idea: "Note-taking app for quick capture" Result: "DO NOT BUILD — market saturated"

Pivot attempts:

1. "Note-taking app specifically for academic research with citation management" (targeting researchers)
2. "Voice-first note capture for field workers who can't use keyboards" (different use case)
3. "Notes that auto-organize into project contexts using AI" (unique workflow)

Look for:

• Severity score improving (4+ to 6+)
• Opportunity changing (WEAK to MODERATE)
• Fewer/weaker competitors in the niche
• More specific pain points

Pattern 5: Stakeholder Presentation

Use Case: Need to present findings to team/stakeholders.

What to do:

1. Run discovery analysis
2. Save the JSON output
3. Create a summary highlighting:
   • Problem statement
   • Severity score
   • Key competitors
   • Market opportunity
   • Recommendation with reasoning

Present:

1. Walk through key sections
2. Focus on recommendation and opportunity
3. Discuss risks and mitigation

Pattern 6: Documentation for Decisions

Use Case: Document why you chose/rejected an idea.

What to do:

1. Run analysis for each idea considered
2. Save results alongside your project documentation
3. Include both accepted and rejected ideas with reasoning

Benefit:

• Historical record of decision rationale
• Reference for future similar ideas
• Onboarding for new team members

Anti-Patterns (Don't Do This)

Ignoring "Don't Build" Recommendations

Bad: Agent says "DO NOT BUILD — saturated market." You think "But I'll make mine simpler!"

Why it fails: The analysis considered the market. If it says don't build, there's usually a very good reason.

Not Reading the Full Recommendation

Bad: Check severity_score (7/10) and conclude "Great, let's build!"

Why it fails: Score alone doesn't tell the story. Read the full recommendation field.

Not Providing Context

Bad: "Task app"

Better: "Task manager with AI auto-prioritization and calendar integration for busy professionals on iOS"

Why: More context = better analysis.

Building Despite Weak Validation

Bad: Severity 3/10, WEAK opportunity, "DO NOT BUILD" — but you build anyway.

Why it fails: If it's a learning project, fine. But don't expect commercial success.

Quick Reference

Remember: Product Agent saves you time by being brutally honest. Trust the analysis.