ai-annotation-workflow
Эксперт по data annotation. Используй для ML labeling, annotation workflows и quality control.
Эксперт по data annotation. Используй для ML labeling, annotation workflows и quality control.
Data science and machine learning platform functions for the East language (TypeScript types). Use when writing East programs that need optimization (MADS, Optuna, SimAnneal, Scipy), machine learning (XGBoost, LightGBM, NGBoost, Torch MLP, Lightning, GP), ML utilities (Sklearn preprocessing, metrics, splits), conformal prediction (MAPIE), or model explainability (SHAP). Triggers for: (1) Writing East programs with @elaraai/east-py-datascience, (2) Derivative-free optimization with MADS, (3) Bayesian optimization with Optuna, (4) Discrete/combinatorial optimization with SimAnneal, (5) Gradient boosting with XGBoost or LightGBM, (6) Probabilistic predictions with NGBoost or GP, (7) Neural networks with Torch MLP or Lightning, (8) Data preprocessing and metrics with Sklearn, (9) Conformal prediction intervals with MAPIE, (10) Model explainability with Shap.
Python 생태계(Jupyter, Pandas, Scikit-learn)를 활용하여 데이터에서 심층적인 인사이트를 도출하는 전문 분석 워크플로우입니다.
Standard and NaN-robust statistical functions for data analysis, histograms, and correlation matrices. Triggers: statistics, mean, nanmean, histogram, corrcoef, percentile, std.
Analyze disk usage across directories, identify large/old directories, and generate storage reports. Use when you need to find directories consuming disk space, identify candidates for cleanup (directories older than X days AND larger than Y MB), sort results by size, or generate disk usage reports. Supports filtering by age and size, automatic sorting by disk space, and file-based report generation.
Data analysis and visualization expert using Python pandas and visualization libraries
Analyze data files using SQL queries with DataQL. Use when working with CSV, JSON, Parquet, Excel files or when the user mentions data analysis, filtering, aggregation, or SQL queries on files.
R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis patterns.
This skill should be used when the user asks "Chart.js plugin", "custom Chart.js plugin", "Chart.js plugin hooks", "Chart.js beforeDraw", "Chart.js afterDraw", "custom chart type", "extend Chart.js", "Chart.js API", "Chart.js update", "Chart.js destroy", "Chart.js methods", "Chart.js events", "Chart.js canvas", "Chart.js TypeScript", "custom scale", "Chart.js DatasetController", "Chart.js Scale", or needs help creating custom Chart.js v4.5.1 plugins, extensions, custom chart types, custom scales, or using the API.
Garante padrões consistentes para relatórios no Easy Budget (filtros, exportação, visualização).
Provides CollectionView alternatives for AvaloniaUI using DataGridCollectionView and ReactiveUI. Use when filtering, sorting, or grouping collections in AvaloniaUI applications.
Master SOTA data prep for Kaggle comps: automated EDA (Sweetviz), cleaning (Pyjanitor), and feature selection (Polars + XGBoost) for medium datasets (100MB–5GB) in Colab.
Write structured experiment sections (methods + results + discussion) for NQS + SQD projects in this repository, based on contents of results/ and figures/.
Aggregate-only summarizer for K18 QC artifacts; produces summary outputs and appends manifest rows when format is confirmed.
Guide statistical analysis of NFL data using nflreadpy MCP tools. Use when analyzing real NFL statistics for research, validation, or comparison.
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