Total 55,531 skills, Data Processing has 2838 skills
Showing 12 of 2838 skills
Subscribe to AI and tech RSS feeds and persist normalized metadata into SQLite using mature Python tooling (feedparser + sqlite3). Use when adding feed URLs/OPML sources, running incremental sync with deduplication, and storing entry metadata without full-text extraction or summarization.
Pandas data manipulation with DataFrames. Use for data analysis.
Use this skill for AIRR-seq (Adaptive Immune Receptor Repertoire / VDJ-seq) data analysis with immunarch + immundata in R, including ingestion, receptor schema design, immutable transformations, clonality/diversity/public overlap metrics, and Seurat/AnnData integration.
Google BigQuery for analytics, ML, and data warehousing. Use for large-scale analytics.
Centralized transformation logic for consistent data shaping across API routes. Includes aggregators, rankers, trend calculators, and data sanitizers.
Grid-based geographic clustering with O(n) performance, medoid finding for map markers, and multi-factor risk scoring from event density, sentiment, and recency.
Daily compression of time-series data with merge logic for multiple pipeline runs, structured aggregation for dashboards, and storage estimation for capacity planning.
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
Metabase REST API automation and troubleshooting: authenticate (API key preferred, session fallback), export/upsert questions (cards) and dashboards, standardize visualization_settings, and run/export results.
Calculate statistical significance for A/B tests. Sample size estimation, power analysis, and conversion rate comparisons with confidence intervals.
Implements comprehensive backtesting capabilities for Pine Script indicators and strategies. Use when adding performance metrics, trade analysis, equity curves, win rates, drawdown tracking, or statistical validation. Triggers on "backtest", "performance", "metrics", "win rate", "drawdown", or testing requests.
Use when designing, tuning, or auditing revenue forecast models.