Skill UI

This skill guides the operation, consolidation, and maintenance of complex working systems within Google Workspace (Docs, Sheets, Slides). It emphasizes treating files as integrated systems—such as trackers or planning hubs—requiring careful inspection, precise edits, and structural cleanup rather than simple content rewriting. Use it for consolidating scattered data, cleaning shared spreadsheets, or ensuring the correct canonical version of a document is utilized.

A structured method for transforming experience (especially failures and complex tasks) into actionable, reusable knowledge. Instead of simple diary entries, this skill guides the user to extract root causes, identify underlying patterns, and articulate what they must do differently next time, thereby accelerating professional learning and debugging efficiency.

This utility is designed to reconcile the ADR (Architecture Decision Record) index when source files have been deleted or modified, preventing the accumulation of stale or dangling reference rows. Unlike simple upserts, this tool performs a hard purge of the entire index namespace (adr-patterns and adr-edges) and then fully rebuilds the index from the files currently present on disk. It is crucial for maintaining data integrity after deletions.

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.

This skill transforms raw marketing data into comprehensive, narrative-driven performance reports. It goes beyond simple metrics tracking by performing trend analysis, detecting anomalies, benchmarking against industry standards, and generating actionable, prioritized recommendations. Ideal for stakeholders needing an executive summary or detailed channel commentary.

Provides comprehensive guidelines for designing, auditing, and reporting high-rigor multi-agent experiments, especially in game theory and self-play systems. It emphasizes testing the *interaction* claim—such as convergence to equilibrium, truthful behavior, or emergent cooperation—against held-out opponents, populations, and strategic deviations, moving beyond simple single-agent leaderboard scores.

A comprehensive guide on writing literature reviews and related work sections, specifically tailored for high-impact journals like Acta Automatica Sinica (AAS). It teaches authors how to frame their research by focusing on the incremental contribution (delta), moving beyond simple literature listing to establish clear gaps, contrasting current limitations, and rigorously defining the work's novelty in a structured manner.

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.

A comprehensive guide for authors submitting materials science and physical metallurgy manuscripts to Acta Materialia. It emphasizes the necessity of establishing a clear processing–structure–property mechanism, requiring rigorous, quantitative characterization (e.g., TEM, XRD) and detailed mechanistic explanations over simple reports of improved performance. Use this tool for fit assessment and manuscript re-framing.

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.

This guide details how to construct a sophisticated and rigorous robustness suite for top-tier academic econometrics journals (e.g., AEJ: Applied). It moves beyond simple regression tables, providing targeted checks against common methodological threats such as omitted variables, specification search, functional form choices, and incorrect statistical inference. The goal is to systematically demonstrate that the main point estimate is stable, reliable, and not an artifact of the research design.

This framework provides a rigorous methodology for conducting systematic literature reviews and comprehensive academic syntheses. It guides researchers through building transparent coverage, designing robust coding matrices, and choosing the correct integration method (narrative, systematic, or bibliometric) to move beyond simple summarization and create an auditable intellectual map of a field.