gaql-queries
Activate when user needs custom Google Ads data analysis, advanced reporting, or specific metric queries. Provides GAQL query building and execution guidance.
Activate when user needs custom Google Ads data analysis, advanced reporting, or specific metric queries. Provides GAQL query building and execution guidance.
데이터 분석/머신러닝 노트북의 결과를 분석하여 표준화된 Model Card 보고서(Markdown)를 자동 생성합니다.
Visualization is communication. Chart selection, encoding hierarchy, accessibility, rendering performance. Use established algorithms - these problems are solved.
Analyze datasets using pandas, generate reports, and create visualizations
Comprehensive guide to A/B testing, multivariate testing, statistical significance, and experiment analysis for data-driven product decisions
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
Extracts, transforms, and analyzes NBA statistics using the nba_api Python library. Use when working with NBA player stats, team data, game logs, shot charts, league statistics, or any NBA-related data engineering tasks. Supports both stats.nba.com endpoints and static player/team lookups.
Analyze examples and mock data for name diversity, understanding the context and purpose before suggesting changes. Use when reviewing test data, documentation, or seed data.
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
사용자의 주간 학습 기록(마크다운 파일들)을 분석하여 통계 및 복습용 자료를 생성할 때 사용한다.
Modern tidyverse patterns, style guide, and migration guidance for R development. Use this skill when writing R code with dplyr, reviewing tidyverse code, updating legacy R code to modern patterns, or enforcing consistent style. Covers native pipe usage, join_by() syntax, .by grouping, pick/across/reframe operations, tidy selection, stringr patterns, naming conventions, spacing, and migration from base R or older tidyverse APIs.
Plotly visualization patterns for statistical and scientific charts. Use when creating interactive visualizations, statistical plots (scatter, box, violin, heatmaps), UpSet plots for set intersections, network graphs, or exporting figures to HTML/PNG/PDF/SVG formats. Covers both Plotly Express (high-level) and Graph Objects (low-level) APIs.
Generate charts (line, bar) from data and save as image files.
Converting backtest visualizations from bar indices/timesteps to actual datetime axes for clearer time context
This skill should be used when the user asks "Chart.js axes", "Chart.js scales", "Chart.js x-axis", "Chart.js y-axis", "Chart.js time axis", "Chart.js logarithmic scale", "Chart.js axis labels", "Chart.js ticks", "Chart.js grid lines", "Chart.js multiple axes", "Chart.js dual axis", "Chart.js axis title", "Chart.js axis range", "Chart.js min max", "stacked axes", "Chart.js radial axis", or needs help configuring Chart.js v4.5.1 axes and scales.
Validates True Positive Rate and False Positive Rate gaps across demographics using AgentDB metrics
Extract and prepare study data for meta-analysis including effect size calculation, variance estimation, and handling missing data. Use when users need to convert reported statistics into analyzable format or calculate effect sizes from raw data.
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales
Deep methodology knowledge for pairwise meta-analysis including fixed vs random effects, heterogeneity assessment, publication bias, and sensitivity analysis. Use when conducting or reviewing pairwise MA.
Edit or create interactive Plotly.js graph components in Svelte 5. Use when asked to create a graph, add a visualization, build a chart, make a plot, visualize data, add slider controls to a graph, or create a new graph page. Handles 60fps slider updates, throttling, data precomputation, and proper Svelte 5 reactivity patterns.
Visualization designer. Creates dashboard schemas without computing metrics.
Building table filters with query parameters, model search, and supported query patterns