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eda-hypothesis-experiment-designer

Produces falsifiable hypotheses and experiment matrices from natural-language inputs.

8|3|Updated Mar 6, 2026
Search Tags:#planning#metrics#hypothesis#eda#experiment-design#confounders
One-click install
npx skills add https://github.com/Mr-Fang-VLSI/EDAgent --skill eda-hypothesis-experiment-designer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: eda-hypothesis-experiment-designer
Source: https://github.com/Mr-Fang-VLSI/EDAgent/tree/main/skills/eda-hypothesis-experiment-designer
Command: npx skills add https://github.com/Mr-Fang-VLSI/EDAgent --skill eda-hypothesis-experiment-designer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill helps researchers convert abstract research ideas into falsifiable hypotheses and structured experiment plans, ensuring measurable outcomes and clear evaluation criteria.

Core Features & Use Cases

  • Extract core assumptions from idea memos and translate them into testable hypotheses.
  • Build a complete experiment matrix with hypotheses IDs, manipulations, baselines, metrics, thresholds, and confounder mitigation.
  • Define minimum sample panels, runtime budgets, and promotion gates to move from hypothesis to implementation.
  • Provide outputs such as hypothesis_experiment_matrix.tsv, experiment_design_note.md, and promotion_gate.md to support documentation and governance.

Quick Start

Provide your research idea memo and let this skill generate falsifiable hypotheses and a complete experiment matrix with metrics, controls, and confounder mitigation.

Frequently Asked Questions about eda-hypothesis-experiment-designer

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

FAQPage Schema
How do I convert research ideas into testable hypotheses and experiment plans?▼

To convert research ideas into testable hypotheses and experiment plans, provide your idea memo to generate falsifiable hypotheses, structured experiment matrices with metrics, baselines, and confounder mitigation controls.

What is confounder mitigation in experiment design?▼

Confounder mitigation in experiment design involves defining explicit controls and baselines within your experiment matrix to isolate variables, ensuring measurable outcomes accurately reflect the hypothesis rather than external factors.

How do I set promotion gates for moving from hypothesis validation to implementation?▼

Set promotion gates by defining minimum sample panels, runtime budgets, and explicit pass/fail thresholds within your experiment design to govern when a validated hypothesis moves to implementation.

Can I use this experiment design workflow for early EDA research planning without dependencies?▼

Yes, you can use this workflow for early EDA research planning without dependencies, as it loads reference rules as needed to support end-to-end plan development before implementation.

What artifacts are generated for experiment design documentation and governance?▼

Generated artifacts include hypothesis_experiment_matrix.tsv, experiment_design_note.md, and promotion_gate.md to support structured documentation, measurable outcomes, and governance throughout the experiment lifecycle.

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