Skill UI

This comprehensive tool conducts a deep audit of local search engine optimization (SEO) health. It evaluates Google Business Profile (GBP) completeness, NAP consistency, citation presence, on-site location pages, and local schema. The output is a prioritized, actionable report designed to improve local pack rankings and boost foot traffic or service inquiries.

This skill converts Portable Document Format (.pdf) files into Markdown format, making the content easily analyzable, summarized, searchable, or extracted. It is designed to handle various user requests involving PDFs, such as summarizing reports, extracting data, or processing folders of documents. It can also be invoked alongside sibling skills for batch processing mixed file types (PDF, DOCX, XLSX).

This guide outlines the strict rules for dividing a research paper into the main body and supplementary materials (artifact/appendices), particularly for database systems conferences like EDBT. It emphasizes that all decision-critical evidence must be presented within the reviewed pages, ensuring reviewers can judge the work without needing to open external files. It covers page budget discipline, reproducibility standards, and practical partitioning techniques.

This advanced routing skill guides researchers through the complex process of submitting manuscripts in agriculture, environment, and earth science. It does not replace a single journal profile, but rather classifies the work by sub-field, contribution type, and evidence shape. By evaluating pre-routing gates (e.g., evidence strength, scale match), it determines the best specialist venue for re-framing and submission.

This tool guides researchers through the final pre-submission checklist for academic journals like EPSL. It verifies critical mechanical elements—such as word count adherence, data availability statements with DOIs, declarations of competing interests, and file format compliance—before uploading to the journal's editorial manager. It ensures the manuscript passes all technical gates.

This guide outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.

A comprehensive guide for ensuring the reproducibility of data mining research for top-tier conferences like ICDM. It addresses the unique challenges of triple-blind review and strict page limits by detailing necessary artifacts, such as configuration files, seed lists, and anonymized packages. Learn how to achieve Rerunnable, Rebuildable, or Attested reproducibility tiers to solidify your scientific claims.

This guide details the comprehensive process for preparing security research artifacts for post-acceptance evaluation at major conferences like IEEE S&P. It covers the three types of evaluation badges (Available, Functional, Results Reproduced), strict handling constraints for exploits and malware, and best practices for packaging the submission (e.g., Dockerfiles, README structure) to ensure external evaluators can reliably reproduce findings within a limited time frame.

This package ensures full reproducibility for international macroeconomics research. It is designed for projects utilizing complex, multi-source, and often restricted data (such as IMF or central bank files). It mandates capturing the entire data lineage, pinning the execution environment (R, Python, Stata), and documenting access instructions, allowing any authorized replicator to rebuild the analysis from scratch.

A comprehensive guide for structuring the evidence bundle required for robotics research submissions. It details the critical components—including hardware ledgers, time-stamped logs, configuration files, code, and data—necessary to ensure that scientific claims are fully auditable and reproducible, addressing the needs of skeptical reviewers.

This guide outlines the mandatory data and code deposit policy for articles published in the Journal of Applied Econometrics (JAE). Authors must submit a complete, non-confidential set of input data (plain ASCII/CSV), programs that replicate every result, and a detailed README file. It emphasizes strict adherence to archival format rules, ensuring the research is fully reproducible for the academic community.

A comprehensive guide and package structure for preparing empirical law and economics manuscripts for journal submission, ensuring full data and code reproducibility prior to publication. It mandates the assembly of data files, transformation scripts, a master execution script, and detailed README documentation, explicitly addressing the unique challenges of restricted or proprietary legal data sources (e.g., sealed court records).