Agent skill · research science · k-dense-ai
hypogenic
Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.
Why this skill is useful
Provides specific workflows and configurations for using the HypoGeniC package in hypothesis generation that the AI wouldn't reliably generate on its own.
What it needs
Requires python, uv installed locally. About 7k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
HypoGeniC Scope and scientific boundary This skill covers the ChicagoHAI software repository ChicagoHAI/hypothesis-generation and PyPI package hypogenic. HypoGeniC iteratively proposes and scores textual patterns from labeled data; HypoRefine adds literature-derived information; union workflows combine banks. Keep these boundaries explicit: The output is a bank of candidate textual hypotheses and task-prediction statistics. It is not experimental confirmation, causal evidence, a clinical conclusion, or proof of scientific novelty. Predictive accuracy on held-out examples assesses task utility, not truth of a mechanism. Independent scientific validation still needs domain review, suitable controls, preregistered tests where appropriate, and new evidence. For researcher-led formulation of mechanisms and falsifiable predictions, use ../hypothesis-generation/SKILL.md. For open-ended ideation, use the scientific brainstorming skill. Default workflow: local review first Never start a model call automatically. 1. Classify the request: HypoGeniC software use, general hypothesis formulation, or downstream scientific validation. 2. Record the exact package, source, dataset, model/provider, destination, split policy, output path, and budgets. 3. Validate the local run policy and official task config. 4. Audit dataset checksums, schemas, duplicates, and split leakage. 5. Generate a bounded cost/run plan. Review provider retention and current pricing outside the package. 6. Ask for separate confirmation before any external LLM call, model download, or upload of dataset text. 7. Inspect the resulting hypothesis bank locally. 8. Evaluate once on the preserved test split and report limitations. The bundled scripts are deterministic, bounded, local-only, and never import hypogenic, contact a model, load .env, enumerate the environment, or execute text found in configs, datasets, hypotheses, or results. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/hypogenic