Automate statistical validation workflows for quantitative research.
npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill statistics
Please help me install this Agent Skill. Skill: statistics Source: https://github.com/GhostOf0days/codex-quant-skills/tree/main/statistics Command: npx skills add https://github.com/GhostOf0days/codex-quant-skills --skill statistics
This Skill addresses the critical need for rigorous statistical validation in quantitative research, ensuring that findings are reliable, reproducible, and free from common pitfalls like false discoveries or assumption violations.
Run the statistics validation script on the input data file 'trading_data.csv' and save the output to 'validation_report.json'.
High-intent search queries and answers about installing and using this skill.
To validate statistical assumptions, this Skill automates the execution of formal hypothesis testing and model validation scripts. It analyzes input data to verify that underlying statistical assumptions hold true, outputting a comprehensive validation report.
Uncertainty quantification is the process of estimating confidence intervals for model parameters and performance metrics. It applies to model validation by statistically proving whether observed results are significant or likely due to random variance.
You can automate hypothesis testing by running the statistical validation script on your trading data file. The Skill formalizes significance testing, estimates performance confidence intervals, and flags assumption violations before deployment.
Yes, this Skill supports production monitoring of statistical validity by continuously running distribution analysis and assumption checks. It ensures that deployed models maintain their original statistical properties and inference reliability over time.
The best way to avoid false discoveries is to enforce rigorous statistical validation workflows. This Skill accomplishes that by automating parameter estimation, distribution analysis, and formal hypothesis testing to ensure findings are reproducible.
No external dependencies are required to run parameter estimation and distribution analysis. The Skill operates independently using its internal scripts and references to generate reliable statistical inference and validation outputs.
Mohamed-ElkahefGenerate interactive Plotly visualizations and statistical profiles from CSV datasets.
iani-kuliCalibrate LLM-based evaluators against human-labeled ground truth using TPR and TNR metrics.
alongor666Develop Vue 3 components with Pinia stores and ECharts charts.
Zeyuyang-0420Construct and sample Bayesian models with PyMC for probabilistic inference.
