Skill UI

This comprehensive guide helps researchers defend their complex study designs—whether they involve lab experiments, large-scale surveys, or qualitative observations—against rigorous methodological critique. It emphasizes establishing a clear, credible link between theoretical mechanisms and empirical evidence, focusing specifically on the structure-individual link. It covers advanced inference techniques, identifying potential confounding variables, and articulating the design's limitations.

A comprehensive guide to assessing whether a mental health or psychiatry study is suitable for publication in The Lancet Psychiatry. It details the journal's high standards, focusing on large-scale population/epidemiological analyses, policy relevance, and rigorous methodology (e.g., RCTs, cohort studies). The skill helps authors re-frame their research question to meet global health and system-level impact requirements, covering reporting guidelines (CONSORT/STROBE/PRISMA) and submission structure.

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

A robust, on-device solution for processing large-scale clinical text corpora. Supports batch NER, PII extraction, and de-identification over thousands of notes. Features include sharding, checkpointing, resumable functionality, and streaming results to JSONL, ensuring PHI safety and constant memory usage for massive datasets.

A comprehensive guide for designing and building large-scale, resilient platforms using the evolutionary modular monolith approach. It enforces strong logical boundaries using DDD principles (bounded contexts, flat-by-aggregate) and implements necessary patterns like Anti-Corruption Layers and transactional outboxes. Use this when defining a system's structure, choosing between monolith and microservices, or ensuring robust decoupling from external vendors (e.g., ERP, AI).

A comprehensive guide for performing reverse engineering using Ghidra. This skill is essential for scenarios where commercial tools like IDA are unavailable, or when performing large-scale, automated headless analysis. It covers the entire workflow, from project setup and function decompilation to advanced headless scripting and integration with other exploit tools.