closedloop:pm-prep

Name: closedloop:pm-prep
Availability: InStock
Author: closedloop-ai-org

Automate pre-call discovery briefs from customer conversations for product managers.

1|Updated Mar 25, 2026

Search Tags:#discovery #product-management #pre-call #segment-analysis #customer-insights #mom-test #competitive-context

Ready to run

npx skills add https://github.com/closedloop-ai-org/claude-code-skills --skill closedloop-pm-prep

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: closedloop:pm-prep
Source: https://github.com/closedloop-ai-org/claude-code-skills/tree/main/skills/pm-prep
Command: npx skills add https://github.com/closedloop-ai-org/claude-code-skills --skill closedloop-pm-prep

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Pre-call discovery briefs help product teams transform customer conversations into structured, actionable insights. They focus on learning goals, knowledge gaps, and data-driven questions so each call becomes a research instrument rather than a transactional exchange.

Core Features & Use Cases

Reframes customer feedback into underlying needs and outcomes, enabling prioritization.
Orchestrates multi-agent workflows to surface insights about competitive context, segment peers, and per-person dynamics.
Generates concrete, Mom-Test compliant questions to validate assumptions with customers.

Quick Start

Provide a company name using /closedloop:pm-prep to generate the briefing.

Frequently Asked Questions about closedloop:pm-prep

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

FAQPage Schema

How do I prepare pre-call discovery briefs for customer conversations?▼

Pre-call discovery briefs are generated by turning customer conversations into structured insights. The workflow analyzes recent call threads, segment peers, and competitive context to surface underlying needs and decision criteria.

How do I generate Mom-Test compliant questions to validate product assumptions?▼

Mom-Test compliant questions are generated by reframing customer feedback into underlying needs and outcomes. The system orchestrates workflows to produce concrete, data-driven questions that validate assumptions with customers.

What customer insights do I need to generate a product management discovery briefing?▼

Generating a discovery briefing requires access to customer insights, conversations, and signals. Providing a company name initiates the deterministic multi-agent workflow that surfaces per-person dynamics and knowledge gaps.

Can I compare segment peers and competitive context during pre-call preparation?▼

Comparing segment peers and competitive context is supported during pre-call preparation. The multi-agent workflow orchestrates these insights to help product teams prioritize learning goals and surface decision criteria.

What is the best way to turn customer feedback into underlying needs for prioritization?▼

Turning customer feedback into underlying needs is achieved by automating pre-call discovery preparation. This approach transforms transactional exchanges into research instruments by focusing on learning goals and knowledge gaps.