Skill UI

This skill provides comprehensive guidance for authors considering submitting a large-scale, monograph-length review to Physics Reports. It covers journal positioning, required scope across all physics subfields, the rigorous evidence bar, house style, and submission checklist. Use this tool to help structure and refine an article intended to become a definitive, self-contained academic reference.

This guide provides deep insights into the requirements for publishing in top-tier sociological journals. It emphasizes shifting the focus from mere econometric policy evaluation or variable listing to profound sociological problem consciousness and theoretical dialogue. It guides authors on structuring articles that engage with core social mechanisms, whether using large-scale quantitative datasets or rich qualitative ethnographic material.

A comprehensive guide for applying advanced quantitative methods (OLS, Multilevel, Survival Analysis) to large-scale social surveys (CGSS, CFPS, etc.). This guide emphasizes shifting the focus from purely statistical identification (econometric coefficient races) to deep sociological interpretation, focusing on revealing social mechanisms, structural heterogeneity, and real-world social consequences.

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.

Structured GDPR Article 35 Data Protection Impact Assessment guidance based on EDPB criteria, Opinion 28/2024, and national blacklists. Assists threshold decisions, full assessment (assets, necessity, proportionality, risk tracks, mitigations), Art. 36 consultation, and multi-jurisdictional analysis for high-risk processing like profiling, AI, biometrics, large-scale monitoring.

Hivemind is an advanced orchestration layer designed to manage complex tasks using a parallel swarm of external, low-cost workers. It simulates a structured team workflow (scout, coder, tester) to decompose large problems, execute subtasks in isolated worktrees, synthesize aggregated results, and perform consensus reviews. This dramatically reduces token costs and enhances reliability for large-scale coding, research, and testing tasks.

A systematic approach to restructuring code while guaranteeing that the external behavior remains identical. This methodology is crucial for large-scale changes, such as feature extraction, consolidation of logic, or complex ownership moves, where robust verification must bracket every structural edit.