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Found 326 Skills
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Extract and analyze data from invoices, receipts, bank statements, and financial documents. Categorize expenses, track recurring charges, and generate expense reports. Use when user provides financial PDFs or images.
Stakeholder-ready summary document for any Intelligems A/B test. Combines verdict, financial impact, segment analysis, and recommendations into a single shareable brief.
Analyze TCM constitution data, identify constitution types, evaluate constitution characteristics, and provide personalized health preservation suggestions. Support correlation analysis with health data such as nutrition, exercise, and sleep.
Generate and optimize SQL queries for data retrieval and analysis
Analyzes order data and order processing in Magento 2. Use when analyzing orders, retrieving order information, assessing order processing performance, or troubleshooting order issues. Masters order lifecycle analysis, payment data inspection, and fulfillment diagnostics.
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Calculates genetic risk profiles, interprets PRS percentiles, and assesses disease predisposition across conditions including type 2 diabetes, coronary artery disease, and Alzheimer's disease. Use when asked to calculate polygenic risk scores, interpret genetic risk for complex diseases, build custom PRS from GWAS data, or answer questions like "What is my genetic predisposition to breast cancer?"
Compare two CSV files and generate a unified diff file showing line-by-line differences.
Detects market top probability using O'Neil Distribution Days, Minervini Leading Stock Deterioration, and Monty Defensive Sector Rotation. Generates a 0-100 composite score with risk zone classification. Use when user asks about market top risk, distribution days, defensive rotation, leadership breakdown, or whether to reduce equity exposure. Focuses on 2-8 week tactical timing signals for 10-20% corrections.
What tokens is smart money accumulating before they pump? Token screener with SM filter cross-referenced against netflow.
Event attribution and explanation. Use this skill whenever the user asks for the reason behind a price move. Trigger phrases include: why did X crash, what just happened, why is it pumping, what caused. MCP tools: news_events_get_latest_events, info_marketsnapshot_get_market_snapshot, news_events_get_event_detail, info_onchain_get_token_onchain, news_feed_search_news.
Analyze stock correlations to find related companies and trading pairs. Use this skill whenever the user asks about correlated stocks, related companies, sector peers, trading pairs, or how two or more stocks move together. Triggers include: "what correlates with NVDA", "find stocks related to AMD", "correlation between AAPL and MSFT", "what moves with", "sector peers", "pair trading", "correlated stocks", "when NVDA drops what else drops", "find me a pair for", "stocks that move together", "beta to", "relative performance", "which stocks follow AMD", "supply chain partners", "correlation matrix", "co-movement", "related tickers", "sympathy plays", "if GOOGL moves what else moves", "semiconductor peers", "compare correlation", "hedging pair", "sector clustering", "realized correlation", "rolling correlation", or any request about finding stocks that move in tandem or inversely. Also triggers when the user mentions well-known pairs like AMD/NVDA, GOOGL/AVGO, LITE/COHR and wants to understand or find similar relationships. Always use this skill even if the user only provides one ticker — infer that they want to find correlated peers.