Skill UI

This comprehensive skill provides best practices for secure coding across multiple domains. It guides developers on managing secrets, validating user input, preventing SQL injection, implementing robust authentication and authorization (including RLS), and mitigating XSS attacks. Use this when handling sensitive data, APIs, or user-generated content to ensure the highest level of security.

An interactive tool designed to discover, select, and assemble specialized teams of AI agents. It supports structured agent collections across different domains (e.g., Engineering, Marketing). Users select multiple roles for a specific task, and the skill launches them in parallel, synthesizing their independent findings into a unified report, including consensus and conflict analysis.

A comprehensive guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

Identifies high-leverage backlink prospects by analyzing domains that link to multiple competitors but not the brand. It ranks these prospects using an opinionated priority score combining authority (DR), link overlap, and topical relevance. This tool is essential for planning targeted link-building campaigns, qualifying digital PR leads, or conducting deep quarterly competitive gap audits.

This guide details how to position academic manuscripts within a specific geographic scholarly debate, rather than simply citing domain literature. It teaches authors to identify the "closest prior account," articulate a cross-cutting geographic stake, and engage with core geographic theories to enhance the impact of their findings for an advanced scholarly audience.

This guide helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.

A comprehensive guide for authors submitting high-impact materials research. It details how to select expert referees (covering synthesis, device, and application domains) and provides actionable strategies to pre-empt common reviewer objections—such as claims of incremental advance, insufficient characterization, or weak benchmarking—thereby strengthening the manuscript's overall rigor and maximizing publication success.

This skill guides authors on how to position their manuscript within the complex, multidisciplinary literature of Agricultural Systems (AgSy). It ensures that the paper advances the conversation by engaging both the domain knowledge and the core modeling/systems methodologies. The process focuses on identifying unresolved systemic trade-offs, pinpointing the exact research gap, and clearly articulating the unique contribution among established alternatives.

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

This guide provides a rigorous, step-by-step framework for developing robust academic theories, particularly for prestigious journals like the Academy of Management Review (AMR). It shifts the focus from simple assertions to a structured argument by requiring explicit definition of constructs (including domain and scope), stating underlying assumptions, specifying relationship forms (e.g., mediating, causal), articulating the mechanism (the 'why'), and formulating falsifiable, numbered propositions. Mastering this process ensures the theory is comprehensive and academically sound.

This detailed guide outlines the structure and scientific rigor required for publishing high-quality papers in the domain of Computer Aided Verification (CAV). It instructs authors to transition their work from mere system demonstrations or benchmark reports to formally guaranteed, mathematically precise contributions, emphasizing clear scope definition, stated theorems, and proof ideas.

This detailed guide helps authors assess the scientific rigor and scope fit for manuscripts targeting Composites Part B: Engineering. It covers advanced domains like composite mechanics, manufacturing processes, multiscale modeling, and multifunctional materials. It emphasizes that a strong contribution must demonstrate a clear, mechanism-driven advance from material science to structural application, supported by rigorous, quantitative experimental evidence and model validation.