Knowledge Synthesizer Skill

The Knowledge Synthesizer connects dots across different data sources, products, and domains to generate insights that wouldn't be visible from any single perspective.

Core Capabilities

1. Cross-Product Insights

•Identify bundling opportunities across your catalog
•Detect cross-selling patterns
•Portfolio-level optimization
•Category performance correlation
•Complementary product discovery

2. External Knowledge Integration

•Monitor industry blogs and forums
•Track competitor announcements
•Academic research application
•Market trend reports
•Amazon policy changes

3. Trend Detection

•Early identification of emerging trends
•Search term momentum analysis
•Category growth/decline signals
•Seasonal pattern prediction
•Consumer behavior shifts

4. Multi-Source Data Synthesis

•Combine PPC data + reviews + search trends
•Social media sentiment + sales correlation
•Competitor pricing + your performance
•Supply chain + demand forecasting
•Economic indicators + category performance

5. Insight Generation

•Non-obvious pattern discovery
•Causal relationship identification
•Opportunity scoring
•Risk signal aggregation
•Actionable recommendation synthesis

Cross-Product Analysis

Bundling Opportunity Detection

json

{
  "action": "find_bundling_opportunities",
  "product_catalog": ["ASIN_A", "ASIN_B", "ASIN_C", "ASIN_D"],
  "min_co_purchase_rate": 0.15
}

Output:

json

{
  "bundling_opportunities": [
    {
      "products": ["ASIN_A", "ASIN_C"],
      "co_purchase_rate": 0.28,
      "estimated_bundle_demand": 450,
      "pricing_recommendation": {
        "individual_total": 49.98,
        "suggested_bundle": 44.99,
        "discount": "10%"
      },
      "confidence": 0.82,
      "insight": "Customers who buy ASIN_A frequently search for ASIN_C within 7 days"
    }
  ]
}

Portfolio Optimization

json

{
  "action": "optimize_portfolio",
  "products": ["ASIN_A", "ASIN_B", "ASIN_C"],
  "total_budget": 5000,
  "objective": "maximize_profit"
}

Output:

json

{
  "recommended_allocation": {
    "ASIN_A": {"budget": 2500, "reason": "Highest ROAS, growing category"},
    "ASIN_B": {"budget": 1500, "reason": "Stable performer, defensive position"},
    "ASIN_C": {"budget": 1000, "reason": "Emerging product, test allocation"}
  },
  "expected_outcomes": {
    "total_sales": 22500,
    "total_profit": 5600,
    "portfolio_acos": 0.22
  },
  "insights": [
    "ASIN_A and ASIN_C share customer base - coordinate campaigns",
    "ASIN_B cannibalizes ASIN_A by 8% - consider differentiation"
  ]
}

External Knowledge Integration

Market Trend Monitoring

json

{
  "action": "monitor_market_trends",
  "category": "electronics",
  "sources": ["amazon_search_trends", "google_trends", "industry_blogs"],
  "lookback_days": 30
}

Output:

json

{
  "emerging_trends": [
    {
      "trend": "USB-C charging cables",
      "momentum": "+45% search volume",
      "stage": "early_growth",
      "opportunity_score": 0.78,
      "recommendation": "Consider expanding USB-C product line",
      "supporting_data": {
        "google_trends": "+52% (30d)",
        "amazon_search_rank": "Rising to #3 in category",
        "competitor_activity": "3 new launches this month"
      }
    }
  ],
  "declining_trends": [
    {
      "trend": "Micro-USB accessories",
      "momentum": "-22% search volume",
      "recommendation": "Reduce inventory, phase out campaigns"
    }
  ]
}

GitHub Repository Search

json

{
  "action": "search_github_repos",
  "topic": "amazon advertising optimization",
  "limit": 5
}

Output:

json

{
  "repositories": [
    {
      "title": "amazon-ads-api-python",
      "url": "https://github.com/amzn/amazon-ads-api-python",
      "description": "Official Python SDK for Amazon Ads API",
      "source": "github"
    },
    {
      "title": "ads-optimizer",
      "url": "https://github.com/example/ads-optimizer",
      "description": "ML-based bid optimization tool",
      "source": "github"
    }
  ]
}

Competitive Intelligence Synthesis

json

{
  "action": "synthesize_competitive_intel",
  "your_asins": ["ASIN_A"],
  "competitor_asins": ["COMP_1", "COMP_2", "COMP_3"],
  "data_sources": ["pricing", "reviews", "sponsored_ads", "search_rank"]
}

Output:

json

{
  "competitive_insights": [
    {
      "insight": "COMP_1 dropped price by 15% and increased ad spend by 40%",
      "impact_on_you": "Your conversion rate dropped 12% in same period",
      "recommendation": "Consider price adjustment or differentiation messaging",
      "urgency": "high"
    },
    {
      "insight": "COMP_2 has 3.2x more reviews mentioning 'fast shipping'",
      "opportunity": "Emphasize your FBA Prime advantage in ad copy",
      "estimated_impact": "+8% CTR"
    }
  ],
  "market_position": {
    "price_rank": 2,
    "review_rank": 4,
    "sponsored_visibility": 3,
    "organic_rank": 2
  }
}

Trend Detection

Search Term Momentum

json

{
  "action": "detect_search_momentum",
  "keywords": ["wireless charger", "fast charging", "USB-C cable"],
  "timeframe": "last_90_days"
}

Output:

json

{
  "momentum_analysis": [
    {
      "keyword": "wireless charger",
      "trend": "accelerating",
      "velocity": "+3.2% per week",
      "current_volume": "high",
      "forecast_30d": "+12%",
      "recommendation": "Increase bids by 15-20%",
      "confidence": 0.84
    },
    {
      "keyword": "fast charging",
      "trend": "plateauing",
      "velocity": "+0.5% per week",
      "recommendation": "Maintain current strategy",
      "confidence": 0.91
    }
  ]
}

Multi-Source Synthesis

Review Sentiment + Sales Correlation

json

{
  "action": "correlate_sentiment_sales",
  "asin": "ASIN_A",
  "timeframe": "last_180_days"
}

Output:

json

{
  "correlation_analysis": {
    "sentiment_sales_correlation": 0.67,
    "key_findings": [
      {
        "finding": "Negative reviews mentioning 'durability' spike 3 days before sales drops",
        "correlation": 0.72,
        "lag_days": 3,
        "recommendation": "Monitor 'durability' mentions as early warning signal"
      },
      {
        "finding": "Positive reviews mentioning 'value' correlate with +15% sales within 7 days",
        "recommendation": "Encourage reviews highlighting value proposition"
      }
    ],
    "sentiment_breakdown": {
      "positive": 0.78,
      "neutral": 0.15,
      "negative": 0.07
    },
    "actionable_insights": [
      "Address durability concerns in product description",
      "Highlight value in ad copy to match positive review themes"
    ]
  }
}

Insight Generation

Non-Obvious Pattern Discovery

json

{
  "action": "discover_patterns",
  "data_sources": ["campaigns", "products", "market_data"],
  "min_confidence": 0.7
}

Output:

json

{
  "discovered_patterns": [
    {
      "pattern": "Products with 'Prime' badge in title have 23% higher CTR",
      "confidence": 0.89,
      "sample_size": 1247,
      "recommendation": "Add 'Prime' to titles where applicable",
      "estimated_impact": "+$850/month"
    },
    {
      "pattern": "Campaigns paused on Sundays recover 18% slower than weekday pauses",
      "confidence": 0.76,
      "recommendation": "Avoid Sunday campaign changes",
      "insight": "Weekend shoppers have different behavior patterns"
    },
    {
      "pattern": "Products in 'Electronics > Accessories' perform 2.3x better with video ads",
      "confidence": 0.82,
      "recommendation": "Prioritize video creative for accessories category"
    }
  ]
}

Usage Patterns

Pattern 1: Portfolio Strategy

code

USER: "How should I allocate my $10K budget across my 5 products?"

KNOWLEDGE SYNTHESIZER:
1. Analyze cross-product relationships
2. Check market trends for each category
3. Review competitor activity
4. Synthesize insights:
   - Product A & C share 40% of customers → coordinate timing
   - Product B category declining -15% → reduce allocation
   - Product D in emerging trend → increase allocation
5. Recommend allocation with reasoning

Pattern 2: Early Trend Detection

code

DAILY SCAN:
1. Monitor search trends across categories
2. Check competitor launches
3. Analyze review sentiment shifts
4. Detect: "Eco-friendly" mentions up 35% in your category
5. Alert: "Emerging trend detected: sustainability focus"
6. Recommend: "Consider eco-friendly messaging in ads"

Pattern 3: Competitive Response

code

TRIGGER: Competitor price drop detected

KNOWLEDGE SYNTHESIZER:
1. Analyze: Competitor dropped price 20%
2. Correlate: Your sales down 15% in same period
3. Check: Competitor reviews mention "great value"
4. Synthesize: Price is key differentiator for this product
5. Recommend: "Match price or emphasize quality/features"

Database Schema

sql

-- From server/updates/05_tier2_meta_skills_tables.sql

synthesized_insights (
  insight_type,
  source_data,           -- Which data sources contributed
  insight_description,
  confidence,
  actionable_recommendations,
  created_at
)

external_knowledge (
  source,                -- 'blog', 'forum', 'research_paper', 'trend_report'
  content,
  relevance_score,
  fetched_at
)

Integration with Other Skills

Feeds from:

•market-researcher: Product and competitor data
•grok-admaster-operator: Campaign performance
•memory-palace: Historical patterns
•External APIs: Google Trends, social media, news

Feeds to:

•evolution-engine: Insights for strategy evolution
•simulation-lab: Market scenarios to test
•narrative-architect: Insights for reporting

Files

code

.agent/skills/knowledge-synthesizer/
├── SKILL.md
├── scripts/
│   ├── cross_product_analyzer.py     # Portfolio insights
│   ├── trend_detector.py              # Momentum analysis
│   └── external_knowledge_scraper.py  # Web scraping
└── resources/
    └── data_sources.json              # External source configs

Example Invocation

code

USER: "Why are my sales down this week?"

KNOWLEDGE SYNTHESIZER:
1. Analyze your campaign data: Spend stable, CTR down 8%
2. Check competitor activity: 2 competitors launched new products
3. Review search trends: Category search volume down 5%
4. Sentiment analysis: Your reviews stable, no quality issues
5. External factors: Amazon Prime Day announced (customers waiting)
6. SYNTHESIS: "Sales down due to combination of:
   - New competition (40% impact)
   - Market-wide slowdown pre-Prime Day (35% impact)
   - Seasonal dip (25% impact)
   Recommendation: Maintain current spend, prepare Prime Day strategy, monitor new competitors closely."

Notes

•Synthesizer runs daily scans automatically
•High-confidence insights trigger proactive alerts
•All insights include confidence scores
•External data sources are rate-limited
•Privacy-compliant data collection only

This skill transforms data into wisdom, revealing opportunities hidden in plain sight.