Total 30,738 skills, Data Processing has 1471 skills
Showing 12 of 1471 skills
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
Expert SQL query writing, optimization, and database schema design with support for PostgreSQL, MySQL, SQLite, and SQL Server. Use when working with databases for: (1) Writing complex SQL queries with joins, subqueries, and window functions, (2) Optimizing slow queries and analyzing execution plans, (3) Designing database schemas with proper normalization, (4) Creating indexes and improving query performance, (5) Writing migrations and handling schema changes, (6) Debugging SQL errors and query issues
Automate Mixpanel tasks via Rube MCP (Composio): events, segmentation, funnels, cohorts, user profiles, JQL queries. Always search tools first for current schemas.
Market regime detection and regime-specific trading strategies. Use when analyzing market conditions to select appropriate strategy.
Provides trading strategies for cryptocurrencies based on Binance market data, calculated technical analysis indicators, and aggregated market sentiment from crypto RSS news feeds. Use when users ask for trading advice, strategy recommendations, or analysis combining price data, TA, and sentiment for crypto assets like ETH, BTC, or altcoins.
Skill for detecting institutional order flow patterns (absorption, exhaustion, imbalance, sweep) from L2 market depth and trade data.
Use when building trading systems, backtesting strategies, implementing execution algorithms, or analyzing market microstructure - covers strategy development, risk management, and production deploymentUse when ", " mentioned.
Designs effective KPI dashboards with proper metric selection, visual hierarchy, and data visualization best practices. Use when building executive dashboards, creating analytics views, or presenting business metrics.
QA an analysis before sharing with stakeholders — methodology checks, accuracy verification, and bias detection. Use when reviewing an analysis for errors, checking for survivorship bias, validating aggregation logic, or preparing documentation for reproducibility.
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.