The Scientific Validation skill provides a structured framework for Claude to objectively test claims from any source, including books, research papers, or intuitive strategies. By enforcing mandatory pre-registration, power analysis, and sensitivity testing, it ensures that findings are statistically significant and robust rather than anecdotal. It is particularly valuable for quantitative developers, researchers, and data scientists who need to move beyond intuition to empirical evidence using techniques like Bayesian analysis, walk-forward testing, and adversarial self-critique.
Key Features
01Rigorous bias prevention including walk-forward testing for time-series data.
02Integrated adversarial review agent to challenge methodologies and findings.
03Mandatory hypothesis pre-registration to prevent data snooping and p-hacking.
04Sensitivity analysis requiring results to survive ±20% parameter variations.
05Automated power analysis to determine required sample sizes for 80% statistical power.
0611 GitHub stars
