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eagle-product-diagnostics

Validate UX design intent against analytics events and database outcomes.

3|Updated Mar 25, 2026
Search Tags:#report generation#funnel analysis#triangulation#ux validation#product diagnostics#analytics events#db outcomes
One-click install
npx skills add https://github.com/eagleisbatman/eagle-skills --skill eagle-product-diagnostics-eagleisbatman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eagle-product-diagnostics
Source: https://github.com/eagleisbatman/eagle-skills/tree/main/eagle-product-diagnostics
Command: npx skills add https://github.com/eagleisbatman/eagle-skills --skill eagle-product-diagnostics-eagleisbatman

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

When product metrics aren’t moving, it’s hard to tell whether the issue is the UX design, the way users behave, or a backend/business outcome failure; this Skill closes that loop by validating UX findings against instrumented events and database ground truth.

Core Features & Use Cases

  • Three-layer validation: reconciles design intent (UX/PRD), instrumented behavior (analytics events/funnels), and outcome truth (DB/business metrics) into PASS/FAIL/PARTIAL verdicts.
  • Event-to-flow mapping & funnel diagnostics: builds event-to-screen/step mappings, computes step conversions and drop-offs, and highlights unmeasured transitions.
  • Stakeholder-ready reporting: generates HTML (default), Word (.docx), and/or Excel (.xlsx) reports using the project’s templates and design systems.
  • Disagreement analysis: when layers contradict, the Skill flags the inconsistency and guides what to investigate next (instrumentation, server failures, wrong success queries, discoverability, etc.).

Quick Start

Ask: "Use Product Diagnostics to validate why this feature isn’t hitting its goals by combining the goal definition, my analytics funnel/events export, and the database outcomes for the same date range, and output the report in HTML."

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 feature's metrics are failing using funnel analysis and event data?▼

Funnel analysis and event data diagnose metric failures by triangulating UX design intent against instrumented analytics behavior and database outcomes, generating PASS, FAIL, or PARTIAL verdicts to pinpoint whether the issue stems from UX, user behavior, or backend results.

What is the best way to correlate analytics drop-offs with real database outcomes?▼

Correlating analytics drop-offs with database outcomes requires a three-layer validation approach that reconciles instrumented funnel events against DB ground truth metrics, flagging disagreements between user behavior and actual goal achievement to guide your next investigation.

Can I use CSV exports and dashboard screenshots to map analytics events to UX flows?▼

CSV exports, JSON files, dashboard screenshots, and plain lists are all supported structured inputs for mapping analytics events to UX flows, allowing you to compute step conversions, identify drop-offs, and highlight unmeasured transitions.

How do I generate stakeholder-ready reports for product diagnostics and hypothesis validation?▼

Stakeholder-ready reports for product diagnostics are generated in HTML, Word, or Excel formats, translating validated UX findings, event-to-screen mappings, and disagreement analysis into structured documents suitable for team review.

What should I investigate when UX design intent contradicts instrumented analytics events?▼

When UX design intent contradicts instrumented analytics events, investigate instrumentation gaps, server failures, incorrect success queries, or discoverability issues, as the Skill explicitly flags these inconsistencies and guides what to examine next.

Do I need SQL queries and database metrics to validate product analytics behavior?▼

Database metrics, SQL queries, or DB exports are required as outcome data to validate product analytics behavior, providing the ground truth needed to reconcile against your event data and UX goal definitions for accurate hypothesis validation.

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