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gpd-list-phase-assumptions

Surfaces the AI's assumptions about a research phase before planning begins.

Updated Mar 18, 2026
Search Tags:#methodology#validation#roadmap#physics#assumptions#research planning#phase analysis
One-click install
npx skills add https://github.com/MichaelsEngineering/sentinel-multiscale-field-dynamics --skill gpd-list-phase-assumptions-michaelsengineering
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gpd-list-phase-assumptions
Source: https://github.com/MichaelsEngineering/sentinel-multiscale-field-dynamics/tree/main/.agents/skills/gpd-list-phase-assumptions
Command: npx skills add https://github.com/MichaelsEngineering/sentinel-multiscale-field-dynamics --skill gpd-list-phase-assumptions-michaelsengineering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Before planning a research phase, the AI often carries hidden assumptions about the physics, mathematics, numerics, and scope that may be wrong. This Skill makes those assumptions explicit so users can correct misconceptions early, before they propagate into plans and execution. ## Core Features & Use Cases - Nine-category assumption analysis: Surfaces physical, mathematical, approximation, computational, scope, anchor, expected-result, dependency, and user-binding assumptions, each with rationale and consequence-if-wrong. - Confidence and risk labeling: Marks assumptions as fairly confident, assumed, unclear, or risky, and names the weakest anchor, the earliest disconfirming check, and likely false progress. - Roadmap validation: Verifies the requested phase number against .gpd/ROADMAP.md and loads project state from .gpd/STATE.md before analyzing. - Use Case: A physics researcher about to plan Phase 3 of a turbulence study runs this Skill to see what regime, resolution, and benchmark anchors the AI assumes, then corrects a wrong coupling-regime assumption before any plan is written. ## Quick Start Ask the AI to list its assumptions for a specific phase, for example by invoking the skill with a phase number such as 3 after your roadmap exists in .gpd/ROADMAP.md.

Frequently Asked Questions about gpd-list-phase-assumptions

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

FAQPage Schema
How do I check the AI's assumptions before planning a research phase?▼

Invoke the skill with a phase number, such as phase 3, after your roadmap exists. The AI validates the phase against .gpd/ROADMAP.md, then presents grouped assumptions across physical, mathematical, computational, scope, and anchor categories with a 'What do you think?' prompt.

What kinds of assumptions does a phase assumption analysis cover?▼

It covers nine areas: physical regime and symmetries, mathematical framework, approximation schemes with validity ranges, computational methods and resolution, scope boundaries, anchor references and baselines, expected results and limiting cases, dependencies, and user guidance treated as binding.

Does this skill create any files or modify the project?▼

No. The output is purely conversational analysis ending with a feedback prompt. It reads .gpd/STATE.md and .gpd/ROADMAP.md for context but writes nothing, so corrections are carried into later discussion or planning steps.

What happens if the phase number is missing or not in the roadmap?▼

The workflow stops with an error. A missing argument returns a usage message, and an unknown phase lists the available phases from the roadmap, then exits without producing any analysis.

When should I list phase assumptions instead of planning directly?▼

Use it when a phase involves uncertain physics regimes, risky approximations, or weak benchmark anchors. It is designed to run before discuss-phase or plan-phase so wrong framings are caught before they shape the research plan.

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