survey-analytics
npx machina-cli add skill pablodiegoo/Data-Pro-Skill/survey-analytics --openclawSurvey Analytics
This skill provides fundamental data operations exclusively tailored for Survey and Quantitative research.
Core Capabilities
1. Survey Processing & Automation
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.
2. Market Research Standards
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.
Governance & Rules
Refer to the references/ folder for specific guidelines on:
- Explicit Weight Handling (
explicit_weight_handling.md) - Survey Governance (
survey_governance.md) - Statistical Test Selector (
statistical_test_selector.md) — Decision tree for choosing the right test
Source
git clone https://github.com/pablodiegoo/Data-Pro-Skill/blob/main/src/datapro/data/skills/survey-analytics/SKILL.mdView on GitHub Overview
This skill provides fundamental data operations exclusively tailored for Survey and Quantitative research. It delivers core statistical analysis and pipeline automation for survey datasets, including Crosstabs, NPS, and Top-Box calculations, as well as automated EDA notebooks and qualitative processing.
How This Skill Works
The skill exposes modular components like quant_analyzer.py, qual_analyzer.py, eda_notebook_generator.py, advanced_analytics_generator.py, crosstabs.py, turf_analysis.py, survey_pca.py, and qualitative_categorizer.py to perform standard aggregations, text processing, and notebook generation. Governance and rules are referenced from the references folder, guiding explicit weight handling, survey governance, and the statistical test selector for choosing appropriate tests.
When to Use It
- When you need Crosstabs, NPS, or Top-Box calculations for survey groups
- When you want to auto-generate complete EDA or analytics notebooks for survey data
- When processing both quantitative and qualitative questionnaire responses
- When applying dimension reduction and advanced analytics scaffolding (PCA)
- When following market research standards with cross-tabs and TURF analysis
Quick Start
- Step 1: Load your survey dataset into the pipeline (quant and qualitative fields ready)
- Step 2: Run quant_analyzer.py for stats, qual_analyzer.py for text, or crosstabs.py for cross-tab results
- Step 3: Use eda_notebook_generator.py to generate a reproducible analytics notebook
Best Practices
- Follow explicit weight handling guidelines from the references folder
- Use crosstabs with significance testing for meaningful group comparisons
- Start with qual_analyzer for structured qualitative processing before categorization
- Leverage eda_notebook_generator to produce reproducible, shareable notebooks
- Consult the Statistical Test Selector to choose appropriate tests for data
Example Use Cases
- Telecom survey: compute Crosstabs and NPS by region and plan to identify gaps
- Auto-generate an EDA notebook for a product feedback survey to share with stakeholders
- Process open-ended responses with qualitative_categorizer and extract themes
- Run TURF analysis to estimate reach and frequency for ad tests
- Apply PCA on block questions to uncover overarching dimensions
