Do I need separate API keys for data providers?

No, all enrichment runs through the Deepline CLI, which manages access to over 28 GTM data providers without requiring individual API keys.

Can I use this for niche B2B industries?

Yes, the skill uses vertical-specific configurations for keywords, tools, and job roles, making it adaptable to sectors from DevTools to AR automation.

What is a 'lift score' in this ICP analysis?

A lift score measures the frequency of a signal in 'Won' accounts versus 'Lost' accounts, identifying which markers are unique to buyers rather than the general market.

What data sources does this skill use for discovery?

The skill extracts data from multi-page website content (via Exa) and job listings (via Crustdata) through the Deepline CLI to identify first-party signals.

How many accounts are required for a significant analysis?

A minimum of 20 'Won' and 10 'Lost' domains is recommended to ensure the statistical significance of the lift scores and signal identification.

Niche Signal Discovery for ICP

Name: Niche Signal Discovery for ICP
Author: getaero-io

bygetaero-io

•

Automatización de Marketing

Identifies unique signals that differentiate successful customers from lost leads to optimize account scoring and GTM strategy.

The Niche Signal Discovery skill enables GTM teams and developers to perform deep ICP analysis by extracting first-party data from multi-page website content and job listings. By comparing 'Closed Won' and 'Closed Lost' accounts, it calculates Laplace-smoothed lift scores to uncover high-intent signals such as specific tech stacks, compliance maturity markers, and niche hiring patterns. This skill streamlines the creation of data-driven account scoring models, helping teams move beyond generic firmographics to prioritize prospects who exhibit the specific operational behaviors of their best customers.

Características Principales

015 GitHub stars

02Signal reliability hierarchy prioritizing high-intent indicators like job roles and compliance

03Differential analysis between Won and Lost accounts to identify unique markers

04Automated multi-page website and job listing extraction via Deepline CLI

05Laplace-smoothed lift scoring for statistically significant signal identification

06Vertical-specific configuration for keywords, tech stacks, and job roles

Casos de Uso

01Refining Ideal Customer Profiles (ICP) based on actual buyer behaviors versus non-buyers

02Building data-driven account scoring models for high-velocity outbound sales

03Identifying migration opportunities by detecting specific competitor tech stacks and pain points

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add getaero-io/gtm-eng-skills niche-signal-discovery

For use in Claude.ai and ChatGPT

Características Principales

015 GitHub stars

02Signal reliability hierarchy prioritizing high-intent indicators like job roles and compliance

03Differential analysis between Won and Lost accounts to identify unique markers

04Automated multi-page website and job listing extraction via Deepline CLI

05Laplace-smoothed lift scoring for statistically significant signal identification

06Vertical-specific configuration for keywords, tech stacks, and job roles

Casos de Uso

01Refining Ideal Customer Profiles (ICP) based on actual buyer behaviors versus non-buyers

02Building data-driven account scoring models for high-velocity outbound sales

03Identifying migration opportunities by detecting specific competitor tech stacks and pain points