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hypogenic

Generate and test scientific hypotheses from observational datasets using provided task configurations and label extraction logic.

Updated Jul 1, 2026
Search Tags:#prompt templates#hypothesis generation#dataset analysis#scientific discovery#literature integration#inference testing
One-click install
npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill hypogenic-jasrajtulsi
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: hypogenic
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/hypogenic
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill hypogenic-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Hypogenic reduces the manual effort of proposing, refining, and evaluating scientific hypotheses from observational data. It helps you move from raw examples to testable ideas faster, especially when you need both data-driven discovery and literature-informed reasoning.

Core Features & Use Cases

  • Automated hypothesis generation: Create multiple candidate hypotheses from labeled datasets and iterate on them with performance feedback.
  • Literature-assisted refinement: Combine empirical patterns with paper-derived insights to strengthen or diversify hypotheses.
  • Hypothesis testing and inference: Evaluate whether generated hypotheses hold on validation or test data using configurable prompts and label extraction.
  • Use case: A researcher studying deception, AI-generated content, or stress signals can use this Skill to generate hypotheses, test them against split datasets, and compare data-only, literature-only, and union strategies.

Quick Start

Ask the skill to generate and test hypotheses for your dataset using the provided configuration template, your train, validation, and test files, and a custom label extraction rule if your outputs need parsing.

Frequently Asked Questions about hypogenic

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

FAQPage Schema
How do I generate testable hypotheses from observational datasets?▼

To generate testable hypotheses from observational datasets, use automated hypothesis generation to create candidates from labeled data and iterate on them with performance feedback. This process reduces manual effort in proposing and evaluating scientific ideas.

Can I combine literature insights with empirical data patterns for scientific discovery?▼

Yes, literature-assisted refinement combines empirical patterns with paper-derived insights. This union workflow strengthens or diversifies hypotheses for tasks like deception detection or AI-content identification by integrating domain literature with observational data.

What do I need to configure before testing hypotheses on split datasets?▼

Testing hypotheses on split datasets requires task configuration files, train, validation, and test files, prompt templates, and a custom label extraction rule. These components execute hypothesis generation and inference testing.

Does automated hypothesis generation work for empirical classification problems like stress detection?▼

Automated hypothesis generation works for empirical classification problems like stress detection, deception detection, and AI-content identification. It evaluates whether generated hypotheses hold on validation or test data using configurable prompts and label extraction logic.

What is the best way to compare data-only and literature-only hypothesis generation strategies?▼

The best way to compare hypothesis generation strategies is using union workflows that test data-only, literature-only, and combined approaches against split datasets. This highlights performance differences across distinct refinement methodologies.

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