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Found 323 Skills
End-to-end epidemiological data analysis — from research question to statistical report. Covers study design assessment, dataset discovery and download, data wrangling, confounder adjustment, regression modeling, sensitivity analysis, visualization, and biological interpretation. Integrates ToolUniverse tools for dataset discovery, literature search, and biological context with Python code execution for data analysis. Use whenever users ask to analyze health data, study disease risk factors, assess exposure-outcome relationships, or conduct observational epidemiology. Also use when users want to run regression on clinical/survey data, calculate odds ratios or hazard ratios from a dataset, adjust for confounders, or produce a Table 1. If the task involves downloading a health dataset and running statistical analysis on it, this is the right skill.
Vendor-neutral skill to score customer churn risk from account signals and produce prioritized retention actions.
This skill should be used when the user asks for 'TRX price', 'TRON token price', 'price chart on TRON', 'K-line data for USDT/TRX', 'TRON trade history', 'TRON whale activity', 'large transfers on TRON', 'smart money on TRON', 'TRON DEX volume', or mentions checking real-time prices, candlestick data, trading volume, whale monitoring, or smart money signals on the TRON network. For token search and metadata, use tron-token. For swap execution, use tron-swap.
Stereonet plots for structural geology using matplotlib. Create lower-hemisphere stereographic projections for orientation data. Use when Claude needs to: (1) Create stereonet plots for structural data, (2) Plot planes as great circles or poles, (3) Plot lineations with trend/plunge, (4) Generate density contours for orientations, (5) Calculate mean orientations and statistics, (6) Analyze fold axes with pi-diagrams, (7) Convert between strike/dip and trend/plunge formats.
Guide for implementing Syncfusion Windows Forms Pivot Grid control for data analysis and pivot table functionality. Use this skill when implementing pivot tables, data summarization, cross-tabulated data, or analytical dashboards in Windows Forms applications. Covers data binding, pivot configuration, filtering, sorting, grouping, calculations, conditional formatting, and exporting.
HK Stock Dividend Tracker. Monitor dividend policies, dividend history, dividend yields and other metrics of Hong Kong-listed companies. Used for income investing and dividend strategy analysis.
EDA, dashboards, Matplotlib, Seaborn, Plotly, and BI tools. Use for creating visualizations, exploratory analysis, or dashboards.
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.
Expert in quantitative finance, algorithmic trading, and financial data analysis using Python (Pandas/NumPy), statistical modeling, and machine learning.
Build robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.
Query Ethereum network data via ethpandaops CLI or MCP server. Use when analyzing blockchain data, block timing, attestations, validator performance, network health, or infrastructure metrics. Provides access to ClickHouse (blockchain data), Prometheus (metrics), Loki (logs), and Dora (explorer APIs).
Generate objective reference check reports about the user from real AI collaboration data — session history, git logs, GitHub profile, and memory files. Like a colleague writing a professional reference, but grounded in actual shared work. Use whenever the user asks to evaluate them as a developer, wants a reference letter, work style analysis, introduced by my agents content, interview prep from collaboration history, or blog topics from past discussions. Triggers on: write a reference, analyze my work patterns, what do you think of me, 나에 대한 레퍼런스 써줘, 내 작업 스타일 분석해줘. Not for general code review, architecture docs, cover letters, or codebase-only analysis.