Agent skill · data analytics · onewave-ai
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.
Why this skill is useful
Adds a comprehensive lead scoring model generation process with specific methodologies, validation metrics, and a structured output that the AI cannot generate on its own.
What it needs
About 2k tokens when loaded. Last updated 2026-07-15. 242 stars on the source repository.
What this skill does
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. No vanity scores. The model exists to prioritize rep time. If the score does not change rep behavior, it is not useful. Workflow 1. Gather inputs. Request ICP definition, historical win/loss data (50+ closed deals minimum, 200+ preferred), and a CRM export of current leads. Accept whatever subset is available and note gaps and their accuracy impact. See references/inputs.md for the full input checklist. 2. Run the analysis process. Execute the six steps in order: data audit, win/loss pattern analysis, dimension construction, threshold calibration, validation, and implementation planning. Do not skip steps. See references/inputs.md for the detailed procedure. 3. Build the four-dimension model. Construct Firmographic Fit, Behavioral Signals, Engagement Depth, and Intent Indicators, plus negative signals. Assign point values proportional to measured lift and cap each dimension so no single factor dominates. 4. Calibrate thresholds. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills onewave-ai/lead-scoring-model