Total 50,402 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
Reference skill for CDF Data Modeling API best practices. Covers concurrency limits (avoiding 429s), pagination patterns for instances.list and instances.query, batching write operations, search vs filter guidance, and the QueuedTaskRunner (Semaphore) utility for controlling concurrent requests. Triggers: DMS limits, 429 error, rate limit, pagination, cursor, nextCursor, batching, semaphore, QueuedTaskRunner, cdfTaskRunner, instances.search, instances.list, instances.query, instances.upsert, concurrency, deadlock.
Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.
Access PUDL table data plus table/column/source metadata in Jupyter or Marimo notebooks for debugging and visualization. Use when users ask what a table contains, how to read it, or how columns are defined.
Construcción y optimización cuantitativa de portafolios: Markowitz (scipy.optimize + Monte Carlo), Black-Litterman (prior CAPM, views absolutas/relativas, posterior bayesiano), HRP/HERC/NCO (clustering jerárquico, risk parity, NCO con restricciones). Todo flat numpy + scipy, sin Riskfolio-Lib ni PyPortfolioOpt.
Market Data API de Alpaca: acciones, crypto, opciones. Historical y real-time data para 5000+ stocks.
Reconcile Venmo business transactions and separate personal from business.
Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.
Bitcoin bottom-timing judgment model. By tracking 6 core indicators (RSI technical oversold, volume dry-up, MVRV ratio, social media fear index, miner shutdown price, long-term holder behavior), it comprehensively evaluates whether Bitcoin has entered a bottom-fishing zone and outputs a bottom-fishing rating and position-building recommendations. When users mention topics such as Bitcoin bottom-fishing, whether BTC has bottomed out, Bitcoin oversold, MVRV, miner shutdown price, long-term holder LTH, Bitcoin fear index, whether to buy Bitcoin, BTC position entry timing, crypto market bottom signals, Bitcoin cycle bottom, etc., be sure to use this skill. Even if the user simply asks "Can I buy the dip on Bitcoin now?" or "Has BTC finished dropping?", this skill should be triggered to provide a structured analysis framework.
Data pipeline and ETL automation - extract, transform, load workflows for data integration and analytics
Plotly Chart Generator - Auto-activating skill for Visual Content. Triggers on: plotly chart generator, plotly chart generator Part of the Visual Content skill category.
Diagnose ClickHouse INSERT performance, batch sizing, part creation patterns, and ingestion bottlenecks. Use for slow inserts and data pipeline issues.
Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).