hypogenic
Automates hypothesis generation and testing from tabular data using LLMs. Combines literature insights with empirical patterns through three methods: data-driven generation, literature integration, and hybrid approaches. Includes Redis caching, parallel processing, and configurable prompts for systematic research exploration.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install k-dense-ai-claude-scientific-skills-hypogenic
Repository
Skill path: scientific-skills/hypogenic
Automates hypothesis generation and testing from tabular data using LLMs. Combines literature insights with empirical patterns through three methods: data-driven generation, literature integration, and hybrid approaches. Includes Redis caching, parallel processing, and configurable prompts for systematic research exploration.
Open repositoryBest for
Primary workflow: Research & Ops.
Technical facets: Full Stack, Data / AI, Testing, Integration.
Target audience: Research teams looking for install-ready agent workflows..
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: K-Dense-AI.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install hypogenic into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/K-Dense-AI/claude-scientific-skills before adding hypogenic to shared team environments
- Use hypogenic for research workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.