hypogenic
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
npx skills add majiayu000/claude-skill-registry --skill hypogenic-hxk622-tokendance-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Hypogenic provides automated hypothesis generation and testing using large language models to accelerate scientific discovery. It 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).
How it works
- Quick Start includes installing the package, cloning datasets, and running hypothesis generation with a specified method (hypogenic, hyporefine, or union) and a number of hypotheses, followed by inference on the generated hypotheses.
- CLI usage exposes commands: hypogenic_generation and hypogenic_inference with required parameters such as config file path, method, number of hypotheses, and output paths.
- Python API offers a BaseTask class to configure, generate hypotheses, and perform inference, including a customizable extract_label function to parse LLM outputs.
- Core processes include three approaches:
- HypoGeniC: generate hypotheses from observational data with iterative refinement; best for exploratory data pattern discovery.
- HypoRefine: integrate literature insights with data to generate theory-grounded hypotheses; best for theory-validation tasks.
- Union Methods: combine literature-derived hypotheses with data-driven outputs; variants include Literature ∪ HypoGeniC and Literature ∪ HypoRefine for broader coverage.
- Dataset and configuration requirements detail the expected JSON format, required keys, and template-driven configuration with placeholders and role-based prompts.
- Literature processing steps (HypoRefine/Union) cover preprocessing PDFs via GROBID and converting to structured data for hypothesis extraction.
When to use it
Use this skill when you need to generate and test multiple hypotheses from observational datasets, synthesize literature-informed and data-driven ideas, or accelerate hypothesis-driven analysis in domains like deception detection, AI-generated content identification, mental health indicators, or predictive modeling.
What it can touch
- Command-line tools: hypogenic_generation, hypogenic_inference
- Python API: BaseTask, extract_label function, task.inference, task.generate_hypotheses
- Dataset and configuration files: config.yaml, <TASK>_train.json, <TASK>_val.json, <TASK>_test.json
- Optional dependencies and components: Redis (caching), s2orc-doc2json, GROBID for literature processing
Caveats
- Installation and usage require Python environment with package 'hypogenic' and optional dependencies configured as described.
- Extracted labels must match the dataset format for correct accuracy calculation; users should implement a domain-specific extract_label function.
- PDF literature processing relies on external tools (GROBID) and may require setup steps such as starting the GROBID service.
# 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: ```bash # Install the package uv pip install hypogenic # Clone example datasets git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data # Run basic hypothesis generation hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20 # Run inference on generated hypotheses hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json ``` **Or use Python API:** ```python from hypogenic import BaseTask # Create task with your configuration task = BaseTask(config_path="./data/your_task/config.yaml") # Generate hypotheses task.generate_hypotheses(method="hypogenic", num_hypotheses=20) # Run inference results = task.inference(hypothesis_bank="./output/hypotheses.json") ``` ## Whe
- Overview
- Quick Start
- When to Use This Skill
- Key Features
- Core Capabilities
- 1. HypoGeniC: Data-Driven Hypothesis Generation
- 2. HypoRefine: Literature and Data Integration
- 3. Union Methods
- Installation
- Dataset Format
- Configuration
- Literature Processing (HypoRefine/Union Methods)
- CLI Usage
- Hypothesis Generation
Install the package uv pip install hypogenic Clone example datasets git clone https://github.com/ChicagoHAI/HypoGeniC-datasets.git ./data Run basic hypothesis generation hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20 Run inference on generated hypotheses hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json For HypoGeniC examples For HypoRefine/Union examples
What does the hypogenic skill do?
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill hypogenic-hxk622-tokendance-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.