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.
npx skills add K-Dense-AI/scientific-agent-skills --skill hypogenic --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.
# 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, da
- Scope and scientific boundary
- Default workflow: local review first
- Reproducible installation
- Safe configuration
- Dataset and prompt-text safety
- Run and cost planning
- Upstream CLI and API facts
- Local output inspection
- Evaluation without model calls
- Provider privacy gate
- References
- Bundled local tools
uv venv --python 3.12 .venv uv pip install "hypogenic==0.3.5" python3 scripts/validate_config.py run \ uv run --with "pyyaml==6.0.2" \ python scripts/validate_config.py task \ python3 scripts/audit_dataset.py \ python3 scripts/plan_run.py \ hypogenic_generation --help hypogenic_inference --help python3 scripts/inspect_outputs.py hypotheses \
What does the hypogenic skill do?
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.
How do I install it?
Run `npx skills add K-Dense-AI/scientific-agent-skills --skill hypogenic --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 K-Dense-AI/scientific-agent-skills, a repository with 32,619 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.