Skill UI

This skill performs a comprehensive, setup-wide health check for agentic developers. It audits various components—including skills, memory, tools, configuration, and workflow habits—to identify inefficiencies and potential improvements. It provides a detailed report and guides the user through applying targeted fixes, ensuring the agent environment remains lean and optimized.

A comprehensive skill to extract and transcribe subtitles from any YouTube video URL. It utilizes a two-stage approach: first, attempting fast extraction via the yt-dlp CLI tool; failing that, it falls back to advanced browser automation using Chrome DevTools MCP to scrape the transcript panel content directly from the webpage DOM. The output is always a clean, timestamped plain text file.

A read-only macOS and Windows storage analysis assistant that scans disk usage, classifies large items by cleanup risk, and generates an interactive HTML report with copyable commands for safe cleanup decisions.

Performs a deep, comprehensive audit of a brand's current digital marketing presence across all active channels, including paid search, social, email, SEO, and compliance. It inventories what is currently live, identifies performance gaps, flags budget leaks, and provides a clean, actionable baseline document required before any major marketing planning or strategy refresh. Ideal for agency onboarding or post-acquisition audits.

Orchestrates the complete, multi-channel launch process for an approved campaign plan. This skill manages all launch dependencies—from pre-launch checklist validation and asset readiness checks (PR, content, creative) to sequential activation across paid ads, organic channels, email automation, and CRM record creation. It ensures a clean, coordinated 'go live' event, coordinating all aspects beyond just paid advertising.

A comprehensive tool for importing and validating leads into a CRM. It performs advanced data scrubbing, deduplication, and mandatory compliance checks (GDPR, CCPA). Leads are automatically scored and qualified (MQL/SAL), ensuring that only high-quality, clean data reaches the sales team, while low-scoring leads are automatically routed into nurture sequences. Supports CSV, JSON, and manual sources.

A comprehensive guide for authors preparing empirical research manuscripts (especially in accounting). It details best practices for structuring main tables (keeping them lean), transforming statistical coefficients into interpretable economic magnitudes, and properly visualizing dynamic effects using event study graphs. It emphasizes moving beyond simple significance stars to provide deep academic context.

This guide helps researchers determine if their empirical economic manuscript is suitable for submission to AEJ: Economic Policy. It focuses on transforming a clean empirical finding into a policy-driven narrative by identifying a concrete policy lever, stating a clear policy question, and quantifying the welfare or cost-benefit implications.

This guide provides rigorous methodological standards for authors submitting economic papers to the China Economic Quarterly (CEQ). It emphasizes aligning research with international top-field standards, requiring clean causal identification, structural modeling, and advanced econometric techniques (e.g., modern DID, IV, RDD). Use this to strengthen your paper's technical rigor and ensure its contribution is methodologically sound for top-tier Chinese journals.

A comprehensive guide for researchers on developing robust and original economic topics, particularly within the context of Chinese industrial economics. It emphasizes structuring research around clean policy quasi-experiments, quantifiable empirical facts, and defining 3-5 distinct marginal contributions to ensure scholarly depth and avoid common pitfalls.

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.