Skill UI

This skill transforms unstructured task lists into a disciplined, protected daily schedule. It implements the Deep Work methodology by classifying tasks into deep and shallow categories, budgeting shallow work time, and creating an energy-first time-blocked plan. It enforces strict limits, such as a 4-hour deep work ceiling, ensuring focus remains the protected resource throughout the day. Includes logging and end-of-day shutdown rituals.

This dashboard provides a comprehensive, single-view operational picture of campaign health. It monitors active campaigns by showing real-time health scores, logging auto-corrections (e.g., bid adjustments, budget reallocation), displaying current guardrail rules, and identifying campaigns needing human attention. It quantifies the estimated savings achieved through automated optimizations, helping users trust the automated system while focusing strategic effort on critical issues.

A structured tool for running A/B tests on marketing content variations. It allows users to compare different approaches—such as headline styles, CTA phrasing, or overall content strategies—by logging variants and evaluating them against technical quality scores (e.g., hallucination, readability). The system provides a comprehensive analysis, identifying a statistically grounded winner and offering actionable recommendations for content improvement.

A guide for researchers submitting to conferences like ACM Multimedia, detailing how to achieve robust result reproducibility. It covers the necessary steps—including logging code commits, data versions, media preprocessing parameters, and hardware details—to allow independent reviewers to successfully rebuild the reported findings and achieve official reproducibility badges.

A comprehensive guide for authors submitting computer vision papers (e.g., to ECCV). It outlines best practices for ensuring the full reproducibility of AI research, covering everything from detailed training recipes and hyperparameter logging to pinning foundation model dependencies and reporting variance honesty. Essential for academic publication integrity.

This guide provides comprehensive standards for conducting and reporting rigorous empirical and simulation evidence in econometrics. It covers designing Monte Carlo simulations, creating minimum evidence maps, ensuring reproducibility through detailed logging, and linking theoretical claims directly to finite-sample evidence. Essential for high-level statistical research.

This guide outlines best practices for achieving high levels of research reproducibility for academic papers, particularly in database and systems research. It details methods for pinning dataset versions, logging system environments, mapping empirical claims to verifiable evidence, and structuring availability statements. Adherence ensures the work is robust, transparent, and fully verifiable by the scientific community.

A comprehensive guide for establishing academic rigor in AI and data science research. It outlines best practices for achieving reproducibility, emphasizing the mandatory release and documentation of raw data, trained models, and qualitative findings. Key tools include datasheets, model cards, and detailed provenance logging, ensuring that research claims are fully auditable and consistent.

A comprehensive guide for academic researchers detailing how to achieve and disclose full reproducibility for Computer Vision and deep learning models. Covers essential practices such as protocol pinning for foundation models, detailed logging of datasets, hardware specifications, and variance honesty, ensuring results remain verifiable years after publication.

This guide outlines the rigorous standards for achieving robust reproducibility in robotics research papers, especially those involving physical hardware. It details a two-tier approach, defining results as either fully *rerunnable* (code/simulation) or fully *auditable* (hardware logs/protocols). Key requirements include creating comprehensive hardware specification ledgers, logging detailed sensor data (rosbags), separating deterministic from stochastic claims, and structuring the entire repository for peer review.

Provides a comprehensive, step-by-step workflow for conducting rigorous literature reviews across academic, biomedical, and technical domains. It guides users through defining research questions (PICO), developing systematic search protocols, evidence logging, source screening, structured data extraction, and synthesizing findings to build high-quality, reproducible review reports.

This tool de-identifies sensitive free-text columns (e.g., names, dates, identifiers) in local structured datasets (CSV, JSONL, Parquet) using advanced OpenMed models. It ensures strict privacy by generating a completely redacted dataset and a separate PHI-free aggregate summary, allowing for secure data sharing and analysis without compromising source privacy or logging sensitive values.