Agent skill · research science · aiskillstore
hypogenic
Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.
Why this skill is useful
Adds automated hypothesis generation scripts and systematic testing methods that significantly enhance research capabilities.
What it needs
Requires hypogenic installed locally. About 9k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
Hypogenic Overview Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. The framework supports three approaches: HypoGeniC (data-driven hypothesis generation), HypoRefine (synergistic literature and data integration), and Union methods (mechanistic combination of literature and data-driven hypotheses). Quick Start Get started with Hypogenic in minutes: Or use Python API: When to Use This Skill Use this skill when working on: Generating scientific hypotheses from observational datasets Testing multiple competing hypotheses systematically Combining literature insights with empirical patterns Accelerating research discovery through automated hypothesis ideation Domains requiring hypothesis-driven analysis: deception detection, AI-generated content identification, mental health indicators, predictive modeling, or other empirical research Key Features Automated Hypothesis Generation Generate 10-20+ testable hypotheses from data in minutes Iterative refinement based on validation performance Support for both API-based (OpenAI, Anthropic) and local LLMs Literature Integration Extract insights from research papers via PDF processing Combine theoretical foundations with empirical patterns Systematic literature-to-hypothesis pipeline with GROBID Performance Optimization Redis caching reduces API costs for repeated experiments Parallel processing for large-scale hypothesis testing Adaptive refinement focuses on challenging examples Flexible Configuration Template-based prompt engineering with variable injection Custom label extraction for domain-specific tasks Modular architecture for easy extension Proven Results 8.97% improvement over few-shot baselines 15.75% improvement over literature-only approaches 80-84% hypothesis diversity (non-redundant insights) Human evaluators report significant decision-making improvements Core Capabilities 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/hypogenic