eagle-product-diagnostics

Triangulate design intent, event behavior, and database outcomes to diagnose feature underperformance.

Updated Apr 6, 2026
One-click install
npx skills add https://github.com/letxbrace-droid/inrunparis --skill eagle-product-diagnostics
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: eagle-product-diagnostics
Source: https://github.com/letxbrace-droid/inrunparis/tree/main/.agents/skills/eagle-product-diagnostics
Command: npx skills add https://github.com/letxbrace-droid/inrunparis --skill eagle-product-diagnostics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

When a feature underperforms, it can be unclear whether the root cause is a UX flaw, a tracking/instrumentation issue, or a real backend/value failure—this Skill helps you identify the actual bottleneck by triangulating across design intent, event behavior, and outcome truth.

Core Features & Use Cases

  • Three-layer validation (UX → Events → DB outcomes) to produce PASS/FAIL/PARTIAL verdicts per goal.
  • Event-to-flow funnel mapping that calculates step conversions, drop-offs, and time between steps from analytics exports.
  • Disagreement analysis to explain what it means when layers contradict and what to investigate next.
  • Actionable reporting in HTML (default), Word (.docx), and/or Excel (.xlsx) formats for stakeholder-ready diagnostics.

Quick Start

Ask it to validate a product hypothesis by providing your goal definitions, analytics event data (CSV/JSON/dashboard screenshots), and outcome/DB metrics, and it will generate a triangulated diagnostics report with funnels, verdicts, and prioritized next actions.

Frequently Asked Questions about eagle-product-diagnostics

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

FAQPage Schema
How do I diagnose why a product feature is underperforming using analytics and UX data?

Diagnose feature underperformance by triangulating UX design intent against analytics event behavior and database outcomes to identify bottlenecks. The process validates funnel data, assigns PASS/FAIL/PARTIAL verdicts, and explains contradictions when UX, events, and DB metrics disagree.

When do I need to triangulate UX design intent with analytics events and database outcomes?

Triangulate UX intent with analytics and database outcomes when a feature underperforms and the root cause is unclear. This technique isolates the actual bottleneck by validating design assumptions against instrumented event behavior and real backend value.

How do I map analytics events to funnel steps to calculate drop-offs and conversions?

Map analytics events to funnel steps by providing goal definitions and event taxonomy exports. The process calculates step conversions, drop-offs, and time between steps, assigning PASS/FAIL/PARTIAL verdicts based on the instrumented behavior data.

Can I use CSV exports and dashboard screenshots to validate product hypothesis tests?

You can validate product hypothesis tests using CSV or JSON analytics exports, dashboard screenshots, and database metrics. The process requires goal definitions, event taxonomy, and outcome data to compute conversions and quantify impact.

What does it mean when UX findings, analytics events, and database outcomes disagree?

When UX, analytics, and database layers disagree, it indicates a contradiction between design intent, tracked behavior, and real backend value. A disagreement analysis explains what the conflict means and identifies what to investigate next to resolve the bottleneck.

How do I generate stakeholder-ready reports for product diagnostics in HTML or Excel?

Generate stakeholder-ready product diagnostics reports in HTML, Word, or Excel formats by processing goal definitions and outcome data. The output includes funnels, PASS/FAIL/PARTIAL verdicts, and prioritized next actions for presentation.