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Equip AI research agents with composable computational-science methods and preregistration workflows
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "science-superpowers" yet — see the docs or source repo.
Create a computational-science preregistration plan for the topic 'the impact of climate change on urban heat island effects,' including research questions, hypotheses, data sources, variable definitions, analysis methods, exclusion criteria, risks, and reproducibility requirements.
A structured preregistration document ready for disciplined execution and reproducibility.
Break down the research task 'identify disease biomarkers using public gene expression data' into composable steps, listing literature search, data acquisition, cleaning, modeling, validation, result logging, and reproducibility checks in order.
A modular research workflow checklist that an AI research agent can execute step by step.
Define a reproducible analysis protocol for a computational social science project, covering folder structure, data versioning, random seed settings, experiment logs, result reporting templates, and intermediate artifacts to save at each step.
A practical reproducibility protocol that standardizes research execution and auditability.
Use LLM agents for literature search, experiment planning, and scientific writing.
Access and extend reusable AI skills for scientific research workflows.
Equip any AI model with open-source research and engineering skills.
Compose and orchestrate AI tools into reusable intelligent workflows.
Break down complex research questions and produce structured, credibility-scored reports.
Access biomedical AI skills for omics analysis, clinical AI, and protein design.