Total 50,401 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
Build production-grade interactive dashboards with Plotly Dash - enterprise features, callbacks, and scalable deployment
For the creation, review, refactoring, and presentation of .ipynb Notebooks (Jupyter / JupyterLab / Google Colab / VS Code). Covers engineered directory structures, efficient token processing, demonstration/sharing patterns, and reproducible workflows with uv/venv.
Retrieve a full listing of actively traded currency pairs in the global forex market using Octagon MCP. Use when researching forex markets, understanding currency pair categories, analyzing major/minor/exotic pairs, and identifying trading opportunities.
Patterns for building, maintaining, and scaling bioinformatics workflows. Covers Nextflow, Snakemake, WDL/Cromwell, container orchestration, and best practices for reproducible computational biology. Use when ", " mentioned.
Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.
Use to interpret qualitative feedback, trends, and risks across community channels.
Best practices for doing quick exploratory data analysis with minimal code and a Pandas .plot like API using HoloViews hvPlot.
Enrich a CSV with any data field using a waterfall pattern: try multiple providers in sequence, stop at the first successful match. Prevents paying for duplicate lookups and maximizes fill rates. Triggers: - "enrich my lead list" - "add [field] to my CSV" - "waterfall enrichment" - "try multiple providers to find [data]" Requires: Deepline CLI — https://code.deepline.com
Calculate risk-based position sizes for long stock trades. Use when user asks about position sizing, how many shares to buy, risk per trade, Kelly criterion, ATR-based sizing, or portfolio risk allocation. Supports stop-loss distance calculation, volatility scaling, and sector concentration checks.
Assist Claude in running PyWGCNA through omicverse—preprocessing expression matrices, constructing co-expression modules, visualising eigengenes, and extracting hub genes.
MANDATORY — invoke this skill BEFORE making any Blockscout MCP tool calls or writing any blockchain data scripts, even when the Blockscout MCP server is already configured. Provides architectural rules, execution-strategy decisions, MCP REST API conventions for scripts, endpoint reference files, response transformation requirements, and output conventions that are not available from MCP tool descriptions alone. Use when the user asks about on-chain data, blockchain analysis, wallet balances, token transfers, contract interactions, on-chain metrics, wants to use the Blockscout API, or needs to build software that retrieves blockchain data via Blockscout. Covers all EVM chains.
Generate comprehensive stock analysis report (PDF or markdown) with trend, PMCC, and fundamental analysis