deal-scoring-engine

Business

Automated deal scoring based on thesis alignment, market size, team, and traction metrics

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Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/domains/business/venture-capital/skills/deal-scoring-engine/SKILL.md

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Deal Scoring Engine

Overview

The Deal Scoring Engine skill provides automated, consistent evaluation of investment opportunities against defined criteria. It generates composite scores based on thesis alignment, market opportunity, team quality, and business traction to support pipeline prioritization and investment decisions.

Capabilities

Thesis Alignment Scoring

Match opportunities against fund investment thesis
Sector, stage, and geography fit assessment
Strategic priority alignment scoring
Anti-thesis and exclusion criteria flagging

Market Opportunity Assessment

TAM/SAM/SOM scoring based on market data
Market growth rate and timing assessment
Competitive intensity evaluation
Regulatory and macro environment scoring

Team Evaluation Scoring

Founder background and experience assessment
Domain expertise and market knowledge scoring
Team completeness and capability gaps
Track record and references scoring

Traction and Metrics Scoring

Revenue and growth rate benchmarking
Unit economics (LTV/CAC, margins) scoring
Engagement and retention metrics assessment
Capital efficiency and burn rate evaluation

Composite Score Generation

Weighted composite scoring with configurable weights
Stage-appropriate scoring models (seed vs. growth)
Sector-specific scoring adjustments
Historical score calibration against outcomes

Usage

Score New Deal

Input: Company data, metrics, team information
Process: Apply scoring models across dimensions
Output: Composite score, dimension scores, flags, recommendations

Configure Scoring Model

Input: Scoring criteria, weights, thresholds
Process: Update scoring model parameters
Output: Configured scoring model, validation results

Benchmark Against Portfolio

Input: Deal scores, portfolio company scores
Process: Compare against portfolio at similar stage
Output: Relative ranking, percentile position, comparisons

Calibrate Model

Input: Historical deals and outcomes
Process: Analyze predictive accuracy, adjust weights
Output: Calibration report, recommended adjustments

Scoring Dimensions

Dimension	Weight Range	Key Factors
Thesis Fit	15-25%	Sector, stage, geography, strategy
Market	20-30%	TAM, growth, competition, timing
Team	25-35%	Experience, domain, completeness
Traction	20-30%	Revenue, growth, unit economics

Integration Points

Deal Flow Tracker: Embed scores in pipeline management
Proactive Deal Sourcing: Score for outreach prioritization
IC Memo Generator: Include scores in investment memos
Market Sizer: Feed market data into scoring

Best Practices

Calibrate scoring models quarterly against outcomes
Use stage-appropriate models (early vs. late stage)
Document override decisions when departing from scores
Maintain transparency on scoring methodology
Avoid over-reliance on scores for complex decisions