gsd-verify-work

Validate built features through conversational user-acceptance testing with phase logs.

Updated Apr 2, 2026

Search Tags:#testing #logs #phases #conversational #uat #feature-validation

Ready to run

npx skills add https://github.com/ev3rlit/magam-canonical-archive --skill gsd-verify-work-ev3rlit

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: gsd-verify-work
Source: https://github.com/ev3rlit/magam-canonical-archive/tree/main/.codex/skills/gsd-verify-work
Command: npx skills add https://github.com/ev3rlit/magam-canonical-archive --skill gsd-verify-work-ev3rlit

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validates features through conversational user-acceptance testing (UAT), reducing manual QA overhead by guiding stakeholders through end-to-end validation with persistent phase logs.

Core Features & Use Cases

Conversational UAT orchestration for feature validation
Automatic issue diagnosis and remediation planning across testing phases
Phase-based logging and tracking with generated {phase_num}-UAT.md reports

Quick Start

Describe the feature to verify and start a UAT session to generate the phase log.

Frequently Asked Questions about gsd-verify-work

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

FAQPage Schema

How do I run conversational UAT to validate built features?▼

Conversational UAT validates built features by guiding stakeholders through end-to-end testing sessions where a tester describes expected behavior and the system verifies outcomes, identifies gaps, and records results across iterations.

What is phase-based logging in user acceptance testing?▼

Phase-based logging in user acceptance testing tracks validation progress across iterations by generating structured phase logs, such as {phase_num}-UAT.md reports, to maintain persistent state tracking and document automated issue diagnosis results.

How do I automate issue diagnosis and remediation planning during feature validation?▼

Automated issue diagnosis and remediation planning occur during feature validation by applying end-to-end testing workflows that identify gaps in expected behavior and generate actionable remediation plans recorded within persistent phase logs.

Can I use conversational UAT for end-to-end testing without manual QA overhead?▼

Conversational UAT reduces manual QA overhead by orchestrating end-to-end testing workflows where stakeholders describe expected behavior, allowing the system to verify outcomes, diagnose issues, and plan remediation across persistent testing phases.

What are the limitations of conversational user acceptance testing for feature validation?▼

Conversational user acceptance testing for feature validation relies on stakeholders accurately describing expected behavior, meaning complex edge cases or undocumented interactions may remain unidentified unless explicitly covered within the conversational testing phases.