analysis▌

Statistical analysis is a systematic process for testing hypotheses and quantifying relationships. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, and Bayesian analyses with assumption checks and APA reporting. Apply this skill for academic research.

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

I help you systematically analyze competitors to inform strategic decisions. I compare products, features, pricing, market positioning, and go-to-market strategies.

Create professional EARNINGS UPDATE REPORTS analyzing quarterly results for companies already under coverage, following institutional standards (JPMorgan, Goldman Sachs, Morgan Stanley format).

You are an expert in competitive intelligence for mobile apps. Your goal is to perform a thorough analysis of the user's competitors and identify actionable opportunities to outperform them.

Data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib. \n \n Covers DataFrame and Series creation, indexing, filtering, and type conversions for structured data handling \n Includes data cleaning techniques: missing value handling, deduplication, string operations, and date/time parsing \n Provides GroupBy aggregation, pivot tables, multi-level indexing, and window functions for exploratory analysis \n Integrates Matplotlib and Seaborn for statistical plotting, trend

Systematic macro-environmental analysis across political, economic, social, technological, environmental, and legal forces. \n \n Structured six-factor framework (PESTEL) for identifying external opportunities and threats that could impact product strategy, market entry, or roadmap decisions \n Includes step-by-step guidance for scoping analysis, evaluating each factor with specific examples, and synthesizing insights into actionable strategic recommendations \n Designed for strategic planning h

Important: This skill assists with variance analysis workflows but does not provide financial advice. All analyses should be reviewed by qualified financial professionals before use in reporting.

Build intelligent agents for investment analysis, risk assessment, and portfolio recommendations. \n \n Integrates financial data collection via yfinance with technical analysis (moving averages, RSI, support/resistance) and fundamental analysis (profitability, valuation, and liquidity ratios) \n Includes risk assessment tools covering volatility, Value at Risk, Sharpe Ratio, and company-specific risk evaluation \n Generates investment recommendations (Strong Buy through Strong Sell) with confid

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.