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Automated LLM-driven hypothesis generation and testing on tabular datasets.
Passed 23 of 25 test cases with the skill, and 7 of 25 without it.
and 18 more in the full report
16 fixed7 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 16 test cases the skill fixed. Same model, same prompt, with and without the skill.
In the `batched_generation` user prompt template inside `config.yaml`, I want the LLM to know how many hypotheses to produce. What exact placeholder variable should be included in the user prompt?
| Checked | Without skill | With skill |
|---|---|---|
| The variable placeholder is ${num_hypotheses}. | Missed | Passed |
The instructions your model reads when the skill loads.
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).
Get started with Hypogenic in minutes:
# 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:
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")
test cases passed, vs 7 / 25 without
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