What is the Decision Trees skill for Claude Code?

It is a specialized skill designed to help developers create, audit, and simulate complex branching logic and eligibility rules for personalization engines and GTM workflows.

How does this skill handle missing data in decision flows?

The skill utilizes a Fallback Design framework that ensures every decision branch has a safe default action when data signals are missing or risk flags are triggered.

Can I use this skill for A/B testing?

Yes, the framework includes specific instructions for embedding experiment hooks at key decision points to track performance against guardrail metrics.

Is this skill suitable for RevOps and Sales plays?

Absolutely. It is part of the gtm-agents collection, specifically built to handle logic for sales plays, marketing campaigns, and customer success automation.

Personalization Decision Trees

Name: Personalization Decision Trees
Author: gtmagents

bygtmagents

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Marketing Automation

Designs and audits branching logic, eligibility rules, and fallback paths for dynamic Go-to-Market workflows.

About

The Personalization Decision Trees skill provides a structured framework for architecting complex branching logic within sales, marketing, and customer success workflows. It enables developers to map decision nodes directly to business KPIs, prioritize deterministic signals for reliability, and implement robust fallback paths for scenarios where data is missing. By embedding experiment hooks and simulation checklists, this skill ensures that dynamic user experiences across web, email, and in-app platforms are both optimized for performance and resilient against logic gaps or compliance risks.

Key Features

Embedded experiment hooks for A/B testing with guardrail metrics.
Automated fallback path design for missing data or high-risk scenarios.
Deterministic signal hierarchy to prioritize data freshness and reliability.
29 GitHub stars
Logic simulation and validation checklists for pre-deployment auditing.
Objective-driven logic mapping tied to business KPIs and user intent.

Use Cases

Simulating rule-based model updates to validate expected paths before production.
Auditing existing GTM decision flows for coverage gaps or compliance issues.
Planning complex branching logic for dynamic in-app or web experiences.

About

Key Features

Embedded experiment hooks for A/B testing with guardrail metrics.
Automated fallback path design for missing data or high-risk scenarios.
Deterministic signal hierarchy to prioritize data freshness and reliability.
29 GitHub stars
Logic simulation and validation checklists for pre-deployment auditing.
Objective-driven logic mapping tied to business KPIs and user intent.

Use Cases

Simulating rule-based model updates to validate expected paths before production.
Auditing existing GTM decision flows for coverage gaps or compliance issues.
Planning complex branching logic for dynamic in-app or web experiences.