src/datapro/data/skills/survey-analytics/SKILL.md
Core statistical analysis and pipeline automation for survey datasets. Use for: (1) Running standard Crosstabs, NPS, Top-Box calculations, (2) Generating complete EDA or Analytics notebooks, (3) Quantitative and qualitative processing of questionnaire data.
npx skillsauth add pablodiegoo/data-pro-skill survey-analyticsInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill provides fundamental data operations exclusively tailored for Survey and Quantitative research.
quant_analyzer.py: Standard statistical aggregations.qual_analyzer.py: Basic qualitative text processing.eda_notebook_generator.py: Generates automated Exploratory Data Analysis notebooks.advanced_analytics_generator.py: Scaffolds advanced statistical notebooks.survey_report_generator.py: Generates initial EDA using 100% Native Markdown and Mermaid.js.final_report_generator.py: Generates a senior-level analytical report with automatic hypotheses testing.crosstabs.py: Cross-tabulation matrices with significance testing.turf_analysis.py: Total Unduplicated Reach and Frequency analysis.survey_pca.py: Principal Component Analysis for dimension reduction in block questions.qualitative_categorizer.py: NLP/LLM-aided categorizer for open-ended survey text.Refer to the references/ folder for specific guidelines on:
explicit_weight_handling.md)survey_governance.md)statistical_test_selector.md) — Decision tree for choosing the right testtesting
Comprehensive time-series validation and analysis suite. Handles backtesting of trading and non-trading strategies with support for walk-forward validation (training vs testing windows), performance metric calculation (Sharpe, Drawdown, Win Rate), and event-driven resource allocation simulation. Use for: (1) Validating sequential logic on time-series data, (2) Calculating risk-adjusted performance, (3) Simulating constraints in resource distribution, (4) Detecting look-ahead bias through walk-forward testing.
development
Business-level frameworks and actionable reporting for executives. Use for: (1) Plotting Priority Matrices, (2) Generating Pain Curves, (3) Conversion Funnels, (4) Removing Halo Effects to uncover true sentiment.
testing
Tactical and highly interpretable Machine Learning. Use for: (1) Extracting Feature Importance via Random Forest, (2) Running Permutation Tests, (3) Handling Imbalanced Data (SMOTE).
testing
Normalization and state-mapping of municipal data and generation of professional choropleth maps of Brazil (UF/State level). Use for: (1) Detecting city/state strings and normalizing names, (2) Attaching regional metadata, (3) Generating professional maps integrating survey data with shapefiles.