gpd-verify-work

Parse SKILL.md frontmatter and emit structured YAML metadata blocks.

1|Updated Mar 29, 2026
One-click install
npx skills add https://github.com/CharlieGreenman/roomtemp-superconductor-gpd --skill gpd-verify-work
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gpd-verify-work
Source: https://github.com/CharlieGreenman/roomtemp-superconductor-gpd/tree/main/.agents/skills/gpd-verify-work
Command: npx skills add https://github.com/CharlieGreenman/roomtemp-superconductor-gpd --skill gpd-verify-work

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Verifying scientific results requires systematic checks to ensure that derivations, numerical results, and conclusions align with established physics and contract agreements.

Core Features & Use Cases

  • Provides a repeatable workflow to perform dimensional analysis, limiting-case derivations, and numerical spot-checks with a persistent VERIFICATION.md ledger.
  • Integrates phase ROADMAP and SUMMARY.md artifacts to drive evidence-based verification against contract-pledged outcomes.
  • Supports interactive reviewer workflows to capture experiments, computations, and decisions in a single traceable file.

Quick Start

Run the verification workflow for a phase number to begin automated physics checks and generate a VERIFICATION.md.

Frequently Asked Questions about gpd-verify-work

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

FAQPage Schema
How do I verify physics derivations and numerical results systematically?

Physics verification requires systematic checks using dimensional analysis, limiting-case derivations, and numerical spot-checks to ensure derivations align with established physics. A persistent verification ledger captures experiments and decisions in a single traceable file.

What is the best way to perform dimensional analysis on scientific results?

Dimensional analysis is best performed through a repeatable workflow that checks derivations against established physics and contract-pledged outcomes. Integrating phase roadmap and summary artifacts drives evidence-based verification of numerical results.

How do I track verification decisions and computations for a research project?

Tracking verification decisions involves capturing experiments, computations, and decisions in a single traceable file. An interactive reviewer workflow generates a persistent verification ledger to maintain a record of all systematic checks.

Can I use phase roadmaps and summaries to drive evidence-based verification?

Evidence-based verification integrates phase roadmap and summary artifacts to check results against contract-pledged outcomes. This approach ensures that derivations, numerical results, and conclusions align with established physics agreements.

Does physics verification work without setting up a persistent ledger?

Physics verification relies on a persistent ledger to provide a repeatable workflow for systematic checks. Without this traceable file, capturing interactive reviewer experiments, computations, and decisions consistently across phases becomes difficult.

Why does my physics validation workflow lack traceability across project phases?

Validation workflows lack traceability when they omit a persistent verification ledger to capture decisions. Integrating phase roadmap and summary artifacts ensures systematic checks remain evidence-based and traceable across computations.