rt-verna

Diagnose acquisition, retention, and monetization loop failures in B2B SaaS PLG strategies.

28|11|Updated Apr 11, 2026
One-click install
npx skills add https://github.com/risingdream/roundtable --skill rt-verna
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rt-verna
Source: https://github.com/risingdream/roundtable/tree/main/skills/growth/rt-verna
Command: npx skills add https://github.com/risingdream/roundtable --skill rt-verna

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

rt-verna helps you design and refine product-led growth that actually drives acquisition, retention, and monetization instead of relying on vague “make it free” tactics.

Core Features & Use Cases

  • PLG trilogy diagnosis: Pinpoints which loop—acquisition, retention, or monetization—is broken and what to fix first.
  • Account-level intent guidance: Replaces PQL-only thinking with team signals like feature adoption patterns, integration stickiness, and usage velocity.
  • AI-native PLG strategy: Adapts freemium and onboarding for faster time-to-value and agent-driven usage in 2025–2026.

Quick Start

Ask the AI to “Analyze our PLG motion for $ARGUMENTS and tell me what to measure first, which loop is failing, and the next 2-week actions.”

Frequently Asked Questions about rt-verna

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I diagnose which product-led growth loop is failing for my B2B SaaS?

Analyze account-level intent signals to replace PQL-only thinking by tracking team feature adoption patterns, integration stickiness, and usage velocity, mapping these signals to actionable PLG experiments.

How do I set freemium boundaries for an AI-native SaaS product?

Set freemium boundaries for AI-native SaaS by adapting onboarding for faster time-to-value and agent-driven usage, ensuring free tier limits align with your monetization loop and retention curve goals.

What is a data-first anti-framework approach to PLG retention?

Yes, you can map account-level intent signals to actionable PLG experiments by analyzing feature adoption patterns and usage velocity, translating team behaviors into targeted 2-week retention and monetization actions.

How do I measure time-to-value for AI-native onboarding in a PLG model?

Measure time-to-value for AI-native onboarding by tracking agent-driven usage velocity and activation rates, diagnosing delays in the acquisition loop, and mapping findings to freemium boundary adjustments.