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 scientific-hypogenic-blurjp-imageprepmcp-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 shows commands to install and run basic hypothesis generation and inference via CLI: "hypogenic_generation --config ./data/your_task/config.yaml --method hypogenic --num_hypotheses 20" and "hypogenic_inference --config ./data/your_task/config.yaml --hypotheses output/hypotheses.json". It also provides a Python API example using BaseTask to generate hypotheses and run inference.
- Three core capabilities are described:
- HypoGeniC: data-driven generation with steps to initialize, refine, and replace poor hypotheses based on performance.
- HypoRefine: literature and data integration by extracting insights from papers (typically 10 papers), generating theory-grounded and data-driven hypotheses, then refining both banks.
- Union Methods: combine literature-only hypotheses with outputs from HypoGeniC or HypoRefine, with variants for literature ∪ HypoGeniC and literature ∪ HypoRefine.
- Installation and optional dependencies outline installing the package, Redis caching, and tools for literature processing (GROBID) with commands to clone example datasets.
- CLI Usage sections detail hypotheses generation and inference parameters, including task config path, model choice, method, number of hypotheses, and output paths. Python API usage demonstrates creating a task, generating hypotheses, and performing inference, with extract_label function guidance.
- Workflow examples illustrate scenarios for data-driven generation, literature-informed testing, and comprehensive union methods, plus steps for generating and testing hypotheses and analyzing results.
When to use it
Use when you want to generate scientific hypotheses from observational data, test multiple competing hypotheses, combine literature insights with empirical patterns, and accelerate research through automated ideation. Domains include deception detection, AI-generated content identification, mental health indicators, predictive modeling, and other empirical research.
What it can touch
- Tools: claude-code is listed as a declared tool. The CLI and Python API interact with system commands and Python modules as shown in the examples.
Caveats
- License: MIT license is stated.
- Important notes mention optional dependencies such as Redis server, s2orc-doc2json, and GROBID for PDF processing, along with required datasets and prompts templates. No guarantees of specific outcomes are provided beyond the described capabilities and pipeline.
# 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 scientific-hypogenic-blurjp-imageprepmcp-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.