Stage 2: ANALYZE GAPS

Score opportunities from discovery and identify high-value gaps.

Prerequisites

Requires outputs/discover/raw-opportunities-*.json from Stage 1.

Parameters

Parse from user input:

• --phase: casual | regular | power (affects scoring weights)
• --limit: Max opportunities to analyze (default: 50)

Process

Use the gap-analyzer agent to:

1. Load latest discovery data from outputs/discover/

2. For each item, score on:

   Metric	Description	Range
   sentiment	Parse reviews for complaints, frustrations	0-10
   staleness	Months since last update (>6mo = high)	0-10
   price_value	Price vs feature set and reviews	0-10
   promise_delta	Gap between description and reality	0-10
   competition	How many similar tools exist	0-10
   fit	Multi-agent benefit, not commodity CRUD	0-10

3. Calculate opportunity score:

   opportunity_score = (
     sentiment * 0.25 +
     staleness * 0.15 +
     price_value * 0.15 +
     promise_delta * 0.20 +
     (10 - competition) * 0.10 +
     fit * 0.15
   )
   

4. Apply phase weighting:

   • casual: Prioritize lower competition, simpler builds
   • regular: Balanced approach
   • power: Prioritize complexity moats, premium potential

5. Generate gap summary and differentiation angle for each

6. Rank and filter top 10-20 opportunities

7. Save to outputs/analyze/gap-opportunities-{date}.json

Output Schema

{
  "analyzed_at": "2025-11-25T11:00:00Z",
  "phase": "casual",
  "opportunities": [
    {
      "name": "notion-database-sync",
      "marketplace": "apify",
      "opportunity_score": 8.2,
      "scores": {
        "sentiment": 7,
        "staleness": 9,
        "price_value": 8,
        "promise_delta": 8,
        "competition": 6,
        "fit": 7
      },
      "gap_summary": "Existing tools are slow, don't handle large DBs",
      "differentiation_angle": "Batch processing with progress tracking"
    }
  ]
}

Next Step

Run /mcp-pipeline:validate to validate gaps against Reddit pain signals.