Skill UI

TorchDrug is a modular PyTorch library for graph learning tasks including molecular property prediction, molecule generation, and knowledge graph reasoning. It provides datasets, models, and training engines for building workflows in computational chemistry and biology. Ensure compatibility with PyTorch 1.8-2.0 and Python 3.7-3.10 before implementation.

This guide provides comprehensive guidelines for performing vertical scaling on Qdrant instances. Vertical scaling involves upgrading the computational resources (CPU, RAM, or disk) of existing nodes rather than adding new nodes. It is the recommended first step when single nodes experience resource exhaustion (e.g., RAM usage > 80%, CPU saturation). The guide covers sizing guidelines, best practices for cloud deployment, and clearly defines the thresholds when horizontal scaling becomes necessary.

This skill dispatches computationally intensive quantitative trading jobs (such as multi-year walk-forward analysis, large Monte-Carlo simulations, parameter sweeping, or deep model training) to a managed cloud agent runtime. It is designed for large-scale, production-grade backtesting that exceeds local machine capacity, ensuring robust execution and artifact management via Anthropic's cloud services.

A comprehensive guide for revising NLP and Computational Linguistics manuscripts for submission to top-tier conferences like ACL. It mandates adopting a task-first, evidence-grounded approach, strictly scoping claims in the LLM era, and ensuring linguistic rigor in all examples and analyses. Learn how to structure, compress, and refine your arguments for maximum impact.

A comprehensive guide for authors submitting papers to the ACL conference. It assists in positioning the manuscript, verifying topic fit (NLP, language models, semantics), and ensuring the evidence meets the high standards required for top-tier computational linguistics venues. Covers structure, best practices, and submission checklists.

This guide provides a rigorous methodology for systematically gathering, reading, and synthesizing vast amounts of sociological research for comprehensive academic reviews. It focuses on ensuring comprehensive coverage across all methodological modes (quantitative, qualitative, computational, and theoretical), allowing users to build a robust evidence corpus and synthesis matrix rather than merely summarizing individual studies.

A comprehensive guide detailing the rigorous methodologies for conducting and reporting high-level academic research. It covers norms for quantitative, comparative-historical, and computational analyses, emphasizing transparency, reproducibility, robustness checks (e.g., confounders, alternative specifications), and linking claims directly to evidence, suitable for top-tier social science journals.

A comprehensive guide to structuring and defending research designs across various sociological methods, including quantitative, comparative-historical, ethnographic, and computational approaches. It emphasizes building a credible link between theoretical arguments and empirical evidence, critically addressing alternative hypotheses, and defining the scope of causal claims to meet the highest academic standards.

A comprehensive guide for executing and reporting quantitative, computational, or experimental analyses for top-tier academic journals (e.g., BJPS). It outlines stringent norms for ensuring methodological rigor, transparency, and reproducibility, covering topics like uncertainty reporting, robustness testing, and measurement equivalence. This skill is essential when structuring the results section of a manuscript, particularly when facing expert peer review or preparing for data deposition.

This comprehensive guide provides best practices for designing and presenting scientific experiments for high-impact academic journals. It covers structuring research around clear Research Questions (RQs), selecting strong and fair baselines, ensuring statistical rigor (mean ± standard deviation, significance testing), performing detailed ablation studies, and preventing common issues like data leakage and evaluation pollution. It is essential for authors aiming to validate complex computational findings.

This guide outlines the rigorous process of formalizing cognitive theories for academic manuscripts. It teaches researchers how to structure a computational or mathematical model, define interpretable parameters, and derive discriminating predictions that differentiate their theory from rival accounts. Essential for moving from a verbal hypothesis to a testable, falsifiable scientific model.

This skill provides a rigorous framework for researchers to evaluate whether their cognitive science project is suitable for high-impact, theory-driven journals. It guides users to elevate their contribution from single-effect reports to multi-experiment programs, formal computational models, and substantial theoretical advances, ensuring the work meets the necessary standard of integrative scope.