ai-annotation-workflow
ะญะบัะฟะตัั ะฟะพ data annotation. ะัะฟะพะปัะทัะน ะดะปั ML labeling, annotation workflows ะธ quality control.
ะญะบัะฟะตัั ะฟะพ data annotation. ะัะฟะพะปัะทัะน ะดะปั ML labeling, annotation workflows ะธ 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 ์ํ๊ณ(Jupyter, Pandas, Scikit-learn)๋ฅผ ํ์ฉํ์ฌ ๋ฐ์ดํฐ์์ ์ฌ์ธต์ ์ธ ์ธ์ฌ์ดํธ๋ฅผ ๋์ถํ๋ ์ ๋ฌธ ๋ถ์ ์ํฌํ๋ก์ฐ์ ๋๋ค.
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.
์กฐ์ฌ์ง์ญ ์ฃผ๋ณ ์ ์ฌ ์ ์ ๋งค์นญ ๋ฐ ๋น๊ต ๋ถ์
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.
ใในใ็ตๆๅ ฑๅๆธใไฝๆใใใใในใๅฎไบๅพใฎ็ตๆๅ ฑๅใๅ่ณชใฌใใฅใผๆใซไฝฟใใ
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 padrรตes consistentes para relatรณrios no Easy Budget (filtros, exportaรงรฃo, visualizaรงรฃo).
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 (100MBโ5GB) 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.
๋ฐ์ดํฐ ๋ถ์/๋จธ์ ๋ฌ๋ ๋ ธํธ๋ถ์ ๊ฒฐ๊ณผ๋ฅผ ๋ถ์ํ์ฌ ํ์คํ๋ Model Card ๋ณด๊ณ ์(Markdown)๋ฅผ ์๋ ์์ฑํฉ๋๋ค.
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