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Agent skill

Hypogenic

by aipoch in aipoch/medical-research-skills

Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to…

MITAuto-check passedResearch & Science

Install Hypogenic

skills CLI
$ npx skills add aipoch/medical-research-skills --skill hypogenic -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aipoch/medical-research-skills hypogenic --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/'scientific-skills/Protocol Design/hypogenic' .claude/skills/hypogenic && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
hypogenic
GitHub stars
2k
Token cost
~1.6k tokens
SKILL.md length
459 words
Files
3 (incl. references)
Skills in repo
567
Repo updated
First seen
Licence
MIT

At a glance

Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to…

  • Works in 4 steps: Prepare a dataset (HuggingFace-style JSON) → Create ./data/my_task/config.yaml → Run generation + inference (CLI) → …
  • You need systematic exploration of empirical patterns (e.g.
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Calls uv

What it does

Hypogenic is an agent skill from aipoch/medical-research-skills. Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to combine literature insights with data-driven hypothesis evaluation.

Its SKILL.md is about 1.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `hypogenic_audit_result_v1.json` and `references/config_template.yaml`).

It sits in Research & Science, covering Hypothesis generation and Anomaly detection. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • You need systematic exploration of empirical patterns (e.g.
  • Fraud detection
  • Content analysis) and want to combine literature insights with data-driven hypothesis evaluation

Example prompts

  • “/hypogenic”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Prepare a dataset (HuggingFace-style JSON)
  2. Create ./data/my_task/config.yaml
  3. Run generation + inference (CLI)
  4. Run the same workflow (Python API)

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Hypogenic loads about 1.6k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 68 tokens; SKILL.md has 459 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~68
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 459 words, ~1,636 tokens.

Download SKILL.mdSave it as .claude/skills/hypogenic/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
hypogenic
description
Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to combine literature insights with data-driven hypothesis evaluation.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

When to Use

  • Exploratory analysis on a new dataset where you want the model to propose multiple testable hypotheses from observed patterns (e.g., AI-generated text detection).
  • Benchmarking competing explanations by generating a hypothesis bank and evaluating them consistently on validation/test splits.
  • Literature-informed research where you want to extract claims from papers and refine them against real data (e.g., deception cues in reviews).
  • High-coverage hypothesis discovery when you need both theory-driven and data-driven hypotheses, then merge/deduplicate them (Union workflows).
  • Hypothesis-driven classification/regression pipelines for domains like fraud detection, content moderation, mental health indicators, or other empirical studies using tabular/JSON datasets.

Key Features

  • Automated hypothesis generation (HypoGeniC): iteratively proposes and improves hypotheses using dataset feedback.
  • Literature + data integration (HypoRefine): extracts literature insights from PDFs and refines hypotheses jointly with empirical signals.
  • Union method: mechanically merges literature-only hypotheses with HypoGeniC/HypoRefine outputs to maximize coverage and reduce redundancy.
  • Config-driven prompting: YAML templates with variable injection (e.g., ${text_features_1}, ${num_hypotheses}) for generation and inference.
  • Scalable experimentation: optional Redis caching, parallelism, and adaptive selection focusing on hard examples.

Dependencies

  • hypogenic (install via PyPI; version depends on your environment)
  • Optional (recommended for cost/performance):
    • redis (server; used for caching repeated LLM calls)
  • Optional (required for literature/PDF workflows such as HypoRefine):
    • GROBID (service; used for PDF preprocessing)
    • s2orc-doc2json (PDF-to-structured conversion used in literature pipelines)

Install:

bash
uv pip install hypogenic

Example Usage

The following example is a minimal end-to-end workflow (dataset + config + CLI + Python). Adjust paths and prompts for your task.

1) Prepare a dataset (HuggingFace-style JSON)

Create three files:

  • ./data/my_task_train.json
  • ./data/my_task_val.json
  • ./data/my_task_test.json

Example schema (feature keys can be renamed, but must match your config placeholders):

json
{
  "text_features_1": ["Text A1", "Text A2"],
  "text_features_2": ["Text B1", "Text B2"],
  "label": ["Class1", "Class2"]
}
2) Create ./data/my_task/config.yaml
yaml
task_name: my_task

train_data_path: ./data/my_task_train.json
val_data_path: ./data/my_task_val.json
test_data_path: ./data/my_task_test.json

prompt_templates:
  observations: |
    Feature 1: ${text_features_1}
    Feature 2: ${text_features_2}
    Label: ${label}

  batched_generation:
    system: |
      You are a scientific assistant. Propose testable, falsifiable hypotheses that map features to labels.
    user: |
      Given examples and labels, generate ${num_hypotheses} distinct hypotheses.
      Return a JSON list of hypotheses, each with a short name and a testable statement.

  inference:
    system: |
      You are a careful classifier. Use the provided hypothesis to predict the label.
    user: |
      Hypothesis: ${hypothesis}
      Feature 1: ${text_features_1}
      Feature 2: ${text_features_2}
      Output the final answer as: "final answer: <LABEL>"
3) Run generation + inference (CLI)
bash
# Generate hypotheses (HypoGeniC)
hypogenic_generation \
  --config ./data/my_task/config.yaml \
  --method hypogenic \
  --num_hypotheses 20

# Evaluate generated hypotheses
hypogenic_inference \
  --config ./data/my_task/config.yaml \
  --hypotheses ./output/hypotheses.json
4) Run the same workflow (Python API)
python
from hypogenic import BaseTask
import re

def extract_label(llm_output: str) -> str:
    m = re.search(r"final answer:\s*(.*)", llm_output, re.IGNORECASE)
    return m.group(1).strip() if m else llm_output.strip()

task = BaseTask(
    config_path="./data/my_task/config.yaml",
    extract_label=extract_label,
)

task.generate_hypotheses(
    method="hypogenic",
    num_hypotheses=20,
    output_path="./output/hypotheses.json",
)

results = task.inference(
    hypothesis_bank="./output/hypotheses.json",
    test_data="./data/my_task_test.json",
)

print(results)
Show full SKILL.md (182 more words)Show less

Implementation Details

Methods

  • HypoGeniC (data-driven)

    • Initializes hypotheses from a subset of training data.
    • Iteratively evaluates hypotheses on validation data and replaces underperforming ones.
    • Often uses hard/challenging samples to prompt improved hypotheses.

  • HypoRefine (literature + data)

    • Preprocesses PDFs into structured text (commonly via GROBID + conversion tooling).
    • Generates a literature-derived hypothesis bank and a data-derived hypothesis bank.
    • Refines both banks iteratively using performance feedback and relevance checks.

  • Union

    • Produces combined banks such as:
      • Literature ∪ HypoGeniC
      • Literature ∪ HypoRefine
    • Focuses on coverage and deduplication rather than deeper joint optimization.
Configuration and Prompt Parameters
  • Variable injection: prompt templates can reference dataset fields and runtime parameters:
    • ${text_features_1}, ${text_features_2}, … (from dataset JSON)
    • ${label} (ground truth label, typically used in observation templates)
    • ${num_hypotheses} (generation-time control)
    • ${hypothesis} (inference-time hypothesis text)
  • Label parsing (extract_label):
    • Accuracy depends on extracting a label string that exactly matches the dataset’s label values.
    • Default patterns often look for final answer: ...; customize for your output format.
Performance/Cost Controls (Optional)
  • Redis caching: reduces repeated LLM calls during iterative generation and evaluation.
  • Parallelism: speeds up hypothesis testing on large datasets.
  • Adaptive selection: prioritizes difficult examples to improve hypothesis quality over iterations.

© aipoch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in scientific-skills/Protocol Design/hypogenic of aipoch/medical-research-skills.

  • SKILL.md
  • hypogenic_audit_result_v1.json
  • references/config_template.yaml

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Hypogenic next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Hypogenic compared with similar skills
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Nature Paper CardYuan1z0825/nature-skills46k2 repos~2.1kAutomated safety check: PassApache-2.0
Hypothesis GenerationK-Dense-AI/claude-scientific-writer2.4k2 repos~3.9kAutomated safety check: PassMIT
Good QuestionRimagination/good-question3051 repos~4.3kAutomated safety check: PassMIT

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Categories

Questions about Hypogenic

What does Hypogenic do?

Automated LLM-driven hypothesis generation and testing for tabular datasets; use when you need systematic exploration of empirical patterns (e.g., fraud detection, content analysis) and want to…. Hypogenic is an agent skill from aipoch/medical-research-skills., fraud detection, content analysis) and want to combine literature insights with data-driven hypothesis evaluation.

When should I use Hypogenic?

Hypogenic fits situations like: you need systematic exploration of empirical patterns (e.g; fraud detection; content analysis) and want to combine literature insights with data-driven hypothesis evaluation.

How do I install Hypogenic in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill hypogenic -a claude-code`. Or copy the skill folder (scientific-skills/Protocol Design/hypogenic in aipoch/medical-research-skills) into .claude/skills/hypogenic in your project. Claude Code loads it when a task matches its description.

How do I install Hypogenic in Codex?

Run `npx skills add aipoch/medical-research-skills --skill hypogenic -a codex`. Or copy the skill folder (scientific-skills/Protocol Design/hypogenic in aipoch/medical-research-skills) into .agents/skills/hypogenic in your project. Codex loads it when a task matches its description.

Can I use Hypogenic in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aipoch/medical-research-skills --skill hypogenic -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hypogenic, .gemini/skills/hypogenic, .github/skills/hypogenic and .opencode/skills/hypogenic in your project.

What does Hypogenic need to run?

Going by SKILL.md and its folder, Hypogenic needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Hypogenic access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Hypogenic safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Hypogenic use?

Hypogenic is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Hypogenic use?

About 1.6k tokens (SKILL.md is roughly 6.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.1k tokens, read only when the agent opens those files.

What are the alternatives to Hypogenic?

Skills that share tags, products or a category with Hypogenic: High Stakes Analytics Decision Lab (limingrui679-design/high-stakes-analytics-decision-lab, 989 stars), Hypothesis Generation (spacering-net/codeg, 3.8k stars), Nature Paper Card (Yuan1z0825/nature-skills, 46k stars) and Hypothesis Generation (K-Dense-AI/claude-scientific-writer, 2.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Hypogenic?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,974 GitHub stars. The repository holds 567 skills in this directory. The repository was last updated on September 17, 2026.

Source: aipoch/medical-research-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.