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Found 63 Skills
Identify stocks where blogger sentiment has changed significantly. Use when users want to find who changed their mind or detect sentiment reversals.
Specialized utility for advanced manipulation, analysis, and creation of spreadsheet files, including (but not limited to) XLSX, XLSM, CSV formats. Core functionalities include formula deployment, complex formatting (including automatic currency formatting for financial tasks), data visualization, and mandatory post-processing recalculation.
Best practices for creating comprehensive Jupyter notebook data analyses with statistical rigor, outlier handling, and publication-quality visualizations
Database operations including querying, schema exploration, and data analysis. Activates for tasks involving PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis, Elasticsearch, or ClickHouse databases.
Convert natural language queries to SQL. Use for database queries, data analysis, and reporting.
Stakeholder-ready summary document for any Intelligems A/B test. Combines verdict, financial impact, segment analysis, and recommendations into a single shareable brief.
Process this skill enables AI assistant to forecast future values based on historical time series data. it analyzes time-dependent data to identify trends, seasonality, and other patterns. use this skill when the user asks to predict future values of a time ser... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Test and prototype code in a sandboxed environment. Use for debugging, verifying logic, or installing packages.
Create advanced Excel pivot tables with calculated fields and slicers. Use when building data summaries or creating interactive dashboards. Trigger with phrases like 'excel pivot', 'create pivot table', 'data summary'.
Calculate price elasticity of demand to quantify how price changes affect sales volume. Use this skill when the user needs to estimate demand sensitivity, set optimal prices, or evaluate the revenue impact of price changes — even if they say 'how sensitive are customers to price', 'will a price increase hurt sales', or 'elasticity calculation'.
Apply Partial Least Squares SEM (PLS-SEM) with reflective and formative measurement models to maximize explained variance in endogenous constructs. Use this skill when the user has small samples, formative indicators, or exploratory models, needs to assess AVE/CR/HTMT, or when they ask 'should I use PLS or CB-SEM', 'how do I handle formative constructs', or 'what is the path coefficient significance'.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.