Individual skill
lead-scoring-model
Builds a custom lead scoring model for a business. Takes ICP definition, historical win/loss data, CRM export. Analyzes which attributes correlate with closed-won deals. Generates lead-scoring-model.md with scoring dimensions, point values, thresholds, CRM implementation guide, and validation methodology. Can also score a batch of current leads against the model.
lead-scoring-model/SKILL.md
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- Instructions only
- Last checked
- Nov 10, 2026
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- 425
- Repository last updated
- Oct 20, 2026
- License
- MIT
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- agent-skills Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude Repository topic
- claude-code Repository topic
- claude-skills Repository topic
- developer-tools Repository topic
- llm Repository topic
- prompt-engineering Repository topic
- sales-automation Repository topic
- sales Derived category
- data Derived category
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SKILL.mdcore · 3,833 bytesreferences/inputs.mddoc · 7,350 bytesreferences/output-template.mddoc · 28,922 bytes
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# Lead Scoring Model Builder
Build a data-driven, custom lead scoring model calibrated to actual win/loss history, not generic best practices. Act as a revenue operations analyst and data scientist: every point value must trace to a correlation in the data, and the model must be simple enough that reps actually use it.
## Contents
- `references/inputs.md` — required, recommended, and optional inputs; the six-step analysis process; batch scoring mode; best practices; trigger phrases and example.
- `references/output-template.md` — the full `lead-scoring-model.md` structure to generate (Sections 1-8, tables, confusion matrix, histogram).
## Core Principles
- **Data over intuition.** Trace every point value to a measured lift. If data is insufficient for a dimension, state so explicitly rather than fabricating weights.
- **Simplicity over complexity.** Keep total dimensions to 20-30 signals maximum. A model reps use beats a perfect model they ignore.
- **Continuous calibration.** Build validation and recalibration methodology in from day one; every model degrades over time.