Does Hypogenic support local LLMs?

Yes, Hypogenic is designed to be flexible, supporting both major API providers like OpenAI and Anthropic as well as local LLM deployments.

What is HypoRefine within the Hypogenic skill?

HypoRefine is an agentic framework that synergistically combines insights extracted from existing research literature (PDFs) with patterns found in observational data to generate theory-grounded hypotheses.

What data format is required for Hypogenic tasks?

Datasets should follow the HuggingFace format in JSON, containing specific keys for text features and a 'label' key for ground truth, ensuring the training, validation, and test sets are consistent.

How does Redis improve the hypothesis generation process?

Redis provides a caching layer for LLM responses. This significantly reduces API costs and speeds up repeated experiments during the iterative refinement of hypotheses.

Hypogenic Research Assistant

Name: Hypogenic Research Assistant
Author: Microck

byMicrock

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Data Science & ML

Accelerates scientific discovery by automating the generation, refinement, and testing of research hypotheses using large language models and observational datasets.

About

Hypogenic is a specialized framework designed to streamline the scientific method by leveraging LLMs to generate testable hypotheses from data and existing literature. It supports data-driven discovery, synergistic literature integration via HypoRefine, and mechanistic union methods to ensure diverse and high-performing research insights. Whether you are analyzing deception detection, AI-generated content, or mental health indicators, this skill helps identify empirical patterns and validates competing theories with significantly improved accuracy and diversity compared to traditional few-shot baselines.

Key Features

Synergistic integration of research literature via GROBID PDF processing
Automated data-driven hypothesis generation with iterative refinement
Redis-backed caching to optimize API costs during large-scale experimentation
Systematic hypothesis testing and inference on empirical datasets
81 GitHub stars
Support for both API-based (OpenAI/Anthropic) and local LLM backends

Use Cases

Identifying subtle linguistic indicators for mental health analysis or deception detection
Accelerating exploratory research in domains where theoretical frameworks are still emerging
Generating and testing features for AI-generated content identification

About

Key Features

Synergistic integration of research literature via GROBID PDF processing
Automated data-driven hypothesis generation with iterative refinement
Redis-backed caching to optimize API costs during large-scale experimentation
Systematic hypothesis testing and inference on empirical datasets
81 GitHub stars
Support for both API-based (OpenAI/Anthropic) and local LLM backends

Use Cases

Identifying subtle linguistic indicators for mental health analysis or deception detection
Accelerating exploratory research in domains where theoretical frameworks are still emerging
Generating and testing features for AI-generated content identification