Validate research data for NaN, Inf, zero variance, and range issues.
npx skills add https://github.com/smestern/sciagent --skill scientific-rigor
Please help me install this Agent Skill. Skill: scientific-rigor Source: https://github.com/smestern/sciagent/tree/main/dist/sciagent/skills/scientific-rigor Command: npx skills add https://github.com/smestern/sciagent --skill scientific-rigor
This Skill automates the enforcement of scientific rigor in research workflows, reducing the risk of errors and ensuring reproducibility.
Run the scientific-rigor skill on your dataset to automatically check for common data issues.
High-intent search queries and answers about installing and using this skill.
Automating data validation flags NaN, Inf, and zero variance values to prevent corrupted inputs from skewing analysis. It scans datasets to detect suspicious smoothness and ensures real data is used before analysis begins.
Checking for synthetic data blocks fabricated results by enforcing a tool priority hierarchy that validates real data usage. A code scanner identifies and prevents synthetic data generation before analysis pipelines execute.
Enforcing scientific rigor in neurophysiology workflows uses automated bounds checking to flag data points outside expected physiological ranges. This ensures robust, reliable datasets for reproducible research outcomes.
Automated research validation works without external dependencies, relying solely on internal scripts. It embeds scientific best practices into system prompts to independently enforce reproducibility and data integrity.
Datasets fail reproducibility due to suspicious smoothness or domain-specific value range issues undetected by standard checks. Automated bounds checking and variance validation identify these hidden anomalies.
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