Skill UI

Guides you through building custom Semgrep rules with a testing-first workflow, AST analysis, and taint-mode preference to catch vulnerabilities or bug patterns while steering clear of overly broad or unverified shortcuts.

PyMC is a powerful Python library for probabilistic programming and advanced Bayesian inference. This comprehensive guide details the full workflow for building, fitting, and validating complex Bayesian models. Learn to implement hierarchical structures, perform MCMC sampling (NUTS), conduct posterior predictive checks (PPC), and effectively quantify uncertainty using industry best practices.

This skill provides a comprehensive guide for creating production-quality custom Semgrep rules. It teaches best practices for detecting security vulnerabilities, enforcing robust coding standards, and identifying specific bug patterns. By emphasizing a test-first approach and utilizing advanced techniques like taint mode, users can write highly accurate static analysis rules that drastically reduce false positives.

This comprehensive guide covers essential security patterns for Perl development, addressing common vulnerabilities like SQL Injection, XSS, Path Traversal, and Shell Injection. It details best practices including using Perl's Taint Mode, implementing strict allow-list validation, performing safe file operations, and executing external commands securely to ensure robust and resilient applications.

This structured prompt guides an AI model to proactively analyze a user's question and systematically list all necessary files for accurate answering. It forces the AI to categorize required context into 'Must See' (critical), 'Should See' (helpful), 'Already Have,' and 'Uncertainties.' Ideal for complex coding tasks, debugging, or feature implementation that spans multiple codebase files.

scvi-tools is a comprehensive Python framework for probabilistic deep generative models in single-cell genomics. Built on PyTorch and PyTorch Lightning, it enables batch correction, transfer learning, differential expression analysis with uncertainty, and multi-modal data integration. Best for advanced modeling of single-cell RNA-seq, ATAC-seq, CITE-seq, spatial transcriptomics, and specialized modalities like methylation and cytometry.

A comprehensive methodology for discovering potential software vulnerabilities. It outlines advanced techniques including static analysis, dynamic analysis, fuzzing, taint tracking, and modern attack surface mapping (e.g., kernel, drivers). Ideal for professional code audits, bug bounty hunting, and building robust vulnerability research pipelines.

This comprehensive guide covers essential security patterns for Perl development, addressing common vulnerabilities like SQL Injection, XSS, Path Traversal, and Shell Injection. It details best practices including using Perl's Taint Mode, implementing strict allow-list validation, performing safe file operations, and executing external commands securely to ensure robust and resilient applications.

A comprehensive set of guidelines for generating maps, figures, and tables for academic journals. This guide emphasizes cartographic standards, statistical rigor, and maximizing the visual argument, ensuring that all exhibits are reproducible, legible in print, and adhere to strict word count limitations. It covers everything from projection choice and data normalization to model plot interpretation and handling spatial uncertainty.

This skill guides researchers on the mandatory, rigorous evaluation of complex systems models (e.g., AgSy). It details best practices for model validation, emphasizing the comparison of observed vs. simulated data using independent, held-out datasets. Key components covered include standard fit statistics (RMSE, NSE), sensitivity analysis (identifying core drivers), uncertainty propagation, and multi-objective trade-off/scenario analysis to ensure scientific credibility.

This guide provides rigorous guidelines for creating high-quality figures and tables for complex systems manuscripts, especially in agricultural science. It emphasizes shifting focus from single-factor bar charts to systemic visualizations that reveal interactions, trade-offs (e.g., Pareto fronts), model dynamics, resource flows (Sankey diagrams), and model performance (observed-vs-simulated fit). Exhibits must be self-contained, reproducible, and convey uncertainty.

This skill guides researchers on how to transform complex agricultural systems model results into actionable, honest, and scoped implications for decision-makers. It teaches authors to move beyond merely restating results by identifying the specific actors, articulating the necessary trade-offs, and clearly defining the system boundaries and uncertainty limits, ensuring the paper informs a decision rather than just demonstrating methods.