pmf-pulse

Aggregate cross-source product and market feedback into validated PMF signals.

1|1|Updated Mar 13, 2026
One-click install
npx skills add https://github.com/jp-solumhealth/jpstack --skill pmf-pulse
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: pmf-pulse
Source: https://github.com/jp-solumhealth/jpstack/tree/main/pmf-pulse
Command: npx skills add https://github.com/jp-solumhealth/jpstack --skill pmf-pulse

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

PMF Pulse removes uncertainty about product-market fit by aggregating and validating signals from customer calls, CRM activity, prospect replies, public forums, job postings, and competitor data so founders get prioritized, evidence-backed product recommendations instead of anecdotes.

Core Features & Use Cases

  • Multi-source signal aggregation: Pulls transcripts and summaries from Fireflies, deal activity from HubSpot, prospect objections from Apollo, Reddit threads, Indeed/LinkedIn job signals, and competitor SEO and web data.
  • Cross-source validation & scoring: Scores Pull, Retention, Word-of-Mouth, Willingness-to-Pay, Must-Have, and Market Timing signals and surface patterns where multiple sources converge.
  • Actionable intelligence report: Produces a ranked feature request stack, validated opportunities, churn/expansion lists, competitor gaps, and prioritized next actions for founders and PMs.
  • Use Case: Run a 30-day PMF check to validate whether recurring customer complaints and hiring churn justify building an automated prior-authorization workflow and identify the highest-impact feature to ship next.

Quick Start

Run a 30-day PMF check across Fireflies, HubSpot, Apollo, Reddit, Indeed, and Ahrefs and produce a ranked intelligence report with top validated pain points and the single highest-priority action to take today.

Frequently Asked Questions about pmf-pulse

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

FAQPage Schema
How do I measure product-market fit using customer call transcripts and CRM data?▼

Assessing product-market fit involves scoring six core dimensions: Pull, Retention, Word-of-Mouth, Willingness-to-Pay, Must-Have, and Market Timing. The Skill cross-references these signals across customer calls, CRM deals, and public forums to validate where multiple data sources converge into a strong PMF indicator.

Can I run a PMF check across Reddit threads and competitor web data simultaneously?▼

Yes, you can run a PMF check across Reddit threads and competitor web data simultaneously. The Skill aggregates voice-of-customer insights from public forums alongside competitor SEO lookups to surface validated pain points and competitor gaps within a single cross-referenced report.

What is the best way to validate customer complaints and hiring churn before building a new feature?▼

The best way to validate customer complaints and hiring churn is to cross-reference job postings from Indeed or LinkedIn with prospect objections from Apollo and CRM deal activity. This multi-source validation confirms whether recurring complaints justify building a proposed feature.

How do I generate a ranked feature request stack from prospect replies and market intelligence?▼

You generate a ranked feature request stack by aggregating prospect replies, competitor data, and customer feedback, scoring the findings, and ranking them into a deliverable intelligence report. This highlights validated opportunities and the highest-impact feature to ship next.

Does this PMF intelligence approach require fetching transcripts from specific platforms like Fireflies?▼

Yes, this PMF intelligence approach requires fetching transcripts and summaries from Fireflies, querying CRM systems like HubSpot, and running targeted web lookups. These cross-source data fetches are necessary to cross-reference signals and score findings accurately.

When should I not rely on a single data source for product-market fit validation?▼

You should not rely on a single data source for product-market fit validation when you need evidence-backed recommendations instead of anecdotes. Cross-source validation across calls, CRM, and forums is required to surface patterns where multiple independent sources converge on the same pain point.