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HypoGeniC Hypothesis Planning and Audit

Plans and audits HypoGeniC hypothesis-generation runs on labeled text datasets, locally and without model calls

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
15 files

What it does

When installed, the agent follows a cautious workflow for the hypogenic software, which uses language models to propose textual patterns that predict labels in a labeled text dataset. The agent checks run configs and dataset files, audits checksums, schemas, duplicates and train/validation/test leakage, and prepares a cost plan, all using bundled local scripts. It is instructed never to call a model, upload data or read API keys without separate confirmation, and it treats the output only as candidate hypotheses with predictive statistics, not as scientific proof.

Great for

  • Researchers who want to run HypoGeniC or HypoRefine on a labeled text dataset and need a checklist before spending money on model calls
  • Data scientists who want to verify a dataset split is free of leakage and duplicates before evaluation
  • Teams reviewing whether a HypoGeniC configuration is safe and reproducible

Skip it if

  • Formulating mechanistic or falsifiable scientific hypotheses by hand, which the skill explicitly redirects to a different skill (reason: its scope excludes manual formulation and scientific validation)
  • Treating generated hypotheses as confirmed findings (reason: predictive accuracy shows task utility, not truth)
  • Runs with sensitive or confidential dataset text sent to an external LLM provider without a reviewed retention policy (reason: real HypoGeniC runs contact providers and the skill leaves provider retention to the user)

Try asking

  • “Check my HypoGeniC task config and run policy before I start any model calls”
  • “Audit my dataset manifest for cross-split duplicates and label counts”
  • “Create a bounded cost plan for a HypoGeniC run on my deceptive-review dataset”
  • “Score my saved predictions on the locked test split and list limitations”
Step-by-step guide: how to use HypoGeniC Hypothesis Planning and AuditInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 15 original files, the MIT licence and an attribution note.

    hypogenic.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip hypogenic.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip hypogenic.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Run the bundled validation and audit scripts first; they are local and do not call models
  • Fill in current provider prices in a reviewed copy of the run config yourself, since the example leaves them null
  • Pin datasets to immutable revisions and install hypogenic from a hash-verified lockfile rather than an unpinned branch

What's inside

  • assets/dataset_manifest.example.json
  • assets/result.example.json
  • assets/run_config.example.json
  • assets/task_config.example.yaml
  • references/configuration.md
  • references/datasets.md
  • references/evaluation.md
  • references/security.md
  • references/sources.md
  • references/upstream.md
  • scripts/__init__.py
  • scripts/_common.py
  • scripts/audit_dataset.py
  • scripts/evaluate_local.py
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.