manual-test

Monitor Meetily dev or production logs during live feature testing and generate KPI reports.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/sujithatzackriya/skills --skill manual-test-sujithatzackriya
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: manual-test
Source: https://github.com/sujithatzackriya/skills/tree/main/skills/manual-test
Command: npx skills add https://github.com/sujithatzackriya/skills --skill manual-test-sujithatzackriya

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill reduces guesswork and delays when testing a Meetily feature by watching real-time application logs and turning them into a structured, KPI-based test report.

Core Features & Use Cases

  • Live log monitoring during real user interaction: Watches dev or production Meetily logs while you record, transcribe, summarize, download models, export results, or exercise UI flows.
  • 6 KPI tracking with issue surfacing: Monitors audio processing, transcript processing, LLM processing, recording lifecycle, Tauri IPC events, and model management, and flags problems as they appear.
  • Deterministic test reporting with timelines: Produces a monitoring log with interval data, severity-classified issues, completion sequence timeline, and final pass/fail assessment.
  • Safety guardrails for operational context: Ensures observations are persisted to the monitoring log before context compaction to prevent losing the source of truth.

Quick Start

Run manual testing for the Meetily feature you are about to use, specifying the build type (dev or production), the duration, and what you are focusing on so Claude can monitor the logs and generate the KPI report.

Frequently Asked Questions about manual-test

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

FAQPage Schema
How do I monitor live application logs to extract KPIs during manual testing?

Live log monitoring tracks audio, transcript, LLM, recording lifecycle, Tauri IPC, and model management KPIs in real time. It watches dev or production logs while you interact with the app, flagging issues as they appear and turning them into a structured test report.

What is interval-based KPI extraction for real-time feature validation?

Interval-based KPI extraction captures metrics across audio processing, transcript generation, and LLM operations at set time intervals. It enables deterministic test reporting with timelines, severity-classified issues, and final pass/fail assessments without replacing missing KPI data.

How do I test transcription and summary generation flows with real-time log monitoring?

Run live operational testing while interacting with the app. The monitor watches logs for transcript and LLM processing KPIs, surfacing problems and generating a structured report with completion sequence timelines and severity classifications.

Does this live testing approach work with both dev and production builds?

Live operational testing supports both dev and production builds. Specify the build type, duration, and focus area before testing, and it monitors logs across recording, model download/loading, meeting detection, exports, and UI flows requiring real-time validation.

What are the limitations of real-time log monitoring for meeting detection and UI flows?

Real-time log monitoring does not replace missing KPI data and relies on persisted observations before context compaction to prevent losing the source of truth. It classifies issue severity but requires active user interaction to trigger the UI flows being validated.