Skill UI

This comprehensive command orchestrates full campaign localization across multiple target markets. It adapts all assets (emails, ads, social posts, landing pages, video scripts, etc.) beyond simple translation, adjusting cultural references, ensuring regional compliance, and localizing SEO for global market expansion.

Automates the generation and delivery of detailed marketing performance reports from various sources (Google Ads, Meta Ads, GA, etc.). It calculates KPIs, identifies trends, detects anomalies, and generates actionable business recommendations. Reports can be formatted for weekly pulses, monthly reviews, or Quarterly Business Reviews (QBRs), and delivered via Slack, email, or Google Sheets.

This system intelligently assigns marketing tasks by matching them to team members based on their specific role, channel expertise, regional alignment, and current capacity. It manages team workloads, prevents skill mismatches, and proactively identifies capacity risks, ensuring every task is handled by the right person at the right time, especially useful for agencies managing multiple brands.

This guide assists authors targeting the ACM SIGMOD conference. It provides expert advice on positioning, framing, and structuring database systems papers. It emphasizes strong algorithmic or systems contributions, realistic workloads, and necessary evidence, while also advising on routing relative to similar venues like VLDB and ICDE.

A powerful skill designed to analyze raw ad campaign performance data from platforms like Google, Meta, and LinkedIn. It goes beyond simple metrics reporting, providing deep diagnosis by identifying underperforming ads, pinpointing budget waste, and calculating the true Customer Acquisition Cost (CAC). It generates concrete, actionable recommendations for what to cut, scale, or test next, optimizing your ad spend and maximizing ROAS.

Designed for advanced academic reviews, this framework guides the development of a unifying 'spine' that transcends simple literature summaries. It forces the writer to impose a novel organizational logic—such as a typology, process model, or integrating construct—thereby achieving genuine theory advancement and defining the field's future research agenda. Use this when the draft reads like a disconnected list of papers.

This guide provides detailed instructions for elevating academic prose from standard "lab-report" style to the sophisticated narrative craft expected by top-tier journals. It covers structuring the argument, maintaining theoretical focus, refining voice (process vs. variance), and perfecting the narrative arc to ensure the work reads as a compelling intellectual story, not just a collection of findings. Use this when your theory and analysis are settled, but the writing feels flat or dense.

This guide outlines the rigorous standards for evaluating systems research, particularly for top-tier conferences like ATC. It provides detailed instructions on designing reproducible experiments, establishing fair baselines, utilizing realistic workloads, and reporting critical metrics such as tail latency, variance, and resource cost, ensuring the highest level of scientific rigor.

A sophisticated guide for academic writers on structuring the literature review section of communication science manuscripts. It teaches authors how to move beyond simple summaries by actively positioning their hypotheses against existing empirical debates, identifying precise knowledge gaps, and adjudicating rival theoretical accounts. This skill ensures the work reads as a cumulative, theory-testing contribution suitable for top-tier academic journals.

This skill automatically converts structured data from Excel (.xlsx) workbooks into a clean, readable Markdown format. It is essential for enabling accurate analysis, summarization, and data extraction of spreadsheet contents, especially when the user asks to 'read', 'analyze', or 'compare' data without specifying a conversion method. It supports both single files and batch processing of entire folders.

This skill automates the entire GitHub Pull Request (PR) review cycle, specifically targeting Copilot Code Review comments. It runs a robust loop: automatically triggering reviews, triaging open threads, dispatching parallel fixes that adhere to repo conventions, committing changes, and resolving comments. It continues iterating until the PR is deemed clean and ready for merge, minimizing manual effort and ensuring high code quality.

This guide provides comprehensive instructions for creating a robust, reproducible artifact package for database systems research. It details best practices including containerization, pinning workloads, generating a permanent DOI-archived record, and designing the package to be successfully built and demonstrated on a reviewer's clean machine within a tight time budget. The goal is to strengthen scientific rigor and ensure permanent, open-access citation.