Agent skill

market-research-aggregator

Market intelligence aggregation skill for synthesizing market data from multiple sources

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Forks 31

Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/decision-intelligence/skills/market-research-aggregator

Metadata

Additional technical details for this skill

domain
business
category
knowledge-management
priority
medium
specialization
decision-intelligence
tools libraries
[
    "pandas",
    "requests",
    "custom integrations"
]

SKILL.md

Market Research Aggregator

Overview

The Market Research Aggregator skill provides capabilities for collecting, synthesizing, and analyzing market intelligence from multiple sources. It enables systematic market sizing, trend identification, and opportunity assessment by combining data from various research providers and public sources.

Capabilities

  • Data source integration
  • Market size estimation (TAM, SAM, SOM)
  • Growth rate calculation
  • Trend identification
  • Segment analysis
  • Geographic breakdown
  • Confidence level assessment
  • Source citation management

Used By Processes

  • Market Sizing and Opportunity Assessment
  • Geographic Market Analysis
  • Industry Trend Analysis

Usage

Market Definition

python
# Define market to analyze
market_definition = {
    "name": "Enterprise Project Management Software",
    "description": "Software solutions for managing projects, resources, and portfolios in large organizations",
    "scope": {
        "product_types": ["On-premise", "Cloud-based", "Hybrid"],
        "customer_segments": ["Enterprise (>1000 employees)"],
        "excluded": ["SMB solutions", "Personal productivity tools"]
    },
    "geographic_scope": ["North America", "Europe", "Asia Pacific"],
    "time_frame": {"historical": "2019-2023", "forecast": "2024-2028"},
    "currency": "USD",
    "units": "Revenue"
}

Data Sources

python
# Configure data sources
data_sources = {
    "primary_research": [
        {
            "source": "Gartner",
            "report_name": "Market Share: Enterprise Project Management Software",
            "date": "2024-01",
            "data_type": "market_share",
            "reliability": "high"
        },
        {
            "source": "IDC",
            "report_name": "Worldwide Project Management Software Forecast",
            "date": "2023-12",
            "data_type": "market_forecast",
            "reliability": "high"
        }
    ],
    "secondary_research": [
        {
            "source": "Industry Association Reports",
            "reliability": "medium"
        },
        {
            "source": "Company Annual Reports",
            "reliability": "high"
        }
    ],
    "public_data": [
        {
            "source": "Government Statistics",
            "data_type": "industry_employment",
            "reliability": "high"
        }
    ]
}

Market Sizing (TAM/SAM/SOM)

python
# Market size calculation
market_sizing = {
    "tam": {
        "definition": "Total addressable market for all project management software globally",
        "methodology": "top_down",
        "calculation": {
            "total_enterprises": 500000,
            "adoption_rate": 0.85,
            "average_spend": 150000,
            "result": 63750000000
        },
        "sources": ["Gartner", "IDC"],
        "confidence": "high"
    },
    "sam": {
        "definition": "Serviceable addressable market in target geographies for enterprise segment",
        "methodology": "bottom_up",
        "calculation": {
            "target_enterprises": 75000,
            "product_fit_rate": 0.60,
            "average_spend": 200000,
            "result": 9000000000
        },
        "confidence": "medium"
    },
    "som": {
        "definition": "Serviceable obtainable market based on competitive position",
        "methodology": "competitive_analysis",
        "calculation": {
            "sam": 9000000000,
            "realistic_share": 0.05,
            "result": 450000000
        },
        "time_horizon": "5 years",
        "confidence": "medium"
    }
}

Trend Analysis

python
# Identify and track trends
trend_analysis = {
    "trends": [
        {
            "name": "AI-powered project management",
            "direction": "accelerating",
            "impact": "high",
            "timeline": "2024-2027",
            "evidence": [
                "75% of vendors adding AI features",
                "40% budget increase for AI capabilities"
            ],
            "implications": ["Feature differentiation", "Pricing pressure", "Skill requirements"]
        },
        {
            "name": "Consolidation of point solutions",
            "direction": "steady",
            "impact": "medium",
            "evidence": ["M&A activity up 30%", "Platform play preference"]
        }
    ],
    "methodology": "expert_consensus",
    "update_frequency": "quarterly"
}

Input Schema

json
{
  "operation": "define|collect|size|analyze|report",
  "market_definition": {
    "name": "string",
    "scope": "object",
    "geographic_scope": ["string"],
    "time_frame": "object"
  },
  "data_sources": {
    "primary": ["object"],
    "secondary": ["object"],
    "public": ["object"]
  },
  "analysis_request": {
    "type": "sizing|trends|segments|geography|competitive",
    "parameters": "object"
  }
}

Output Schema

json
{
  "market_overview": {
    "name": "string",
    "current_size": "number",
    "growth_rate": "number",
    "forecast_period": "string"
  },
  "market_sizing": {
    "TAM": {"value": "number", "confidence": "string"},
    "SAM": {"value": "number", "confidence": "string"},
    "SOM": {"value": "number", "confidence": "string"}
  },
  "segments": [
    {
      "name": "string",
      "size": "number",
      "growth_rate": "number",
      "share": "number"
    }
  ],
  "trends": ["object"],
  "data_quality": {
    "sources_used": "number",
    "data_freshness": "string",
    "confidence_overall": "string",
    "gaps_identified": ["string"]
  },
  "citations": ["object"]
}

Market Sizing Methodologies

Method Approach Best For
Top-Down Start with total market, narrow down Mature markets with good data
Bottom-Up Build from unit economics New markets, specific segments
Value-Based Based on customer value delivered Innovative solutions
Competitive Sum of competitor revenues Markets with public companies

Best Practices

  1. Use multiple sources and methodologies
  2. Clearly document assumptions
  3. Indicate confidence levels for all estimates
  4. Update market data at least quarterly
  5. Triangulate from multiple approaches
  6. Account for market definition differences across sources
  7. Track methodology changes over time for comparability

Data Quality Assessment

Dimension Assessment Criteria
Accuracy Source reliability, methodology
Completeness Coverage of market segments
Timeliness Data recency
Consistency Agreement across sources
Relevance Alignment with market definition

Integration Points

  • Feeds into Market Intelligence Analyst agent
  • Connects with Competitive Intelligence Tracker for share data
  • Supports Time Series Forecaster for projections
  • Integrates with Decision Visualization for market maps

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