Data Analysis Pack

We built this so you don't have to reinvent the wheel every time you pull a new dataset. If you're an engineer or researcher who has ever opened a Jupyter notebook, chained pd.read_csv() calls, and watched your environment drift into three different pandas versions, you know the exact pain we're solving. The real issue isn't the code—it's the workflow. Without a structured pipeline, you're manually bridging the gap between exploratory data analysis, hypothesis testing, regression modeling, and presentation. You're writing ad-hoc scripts that mix data cleaning with statistical inference, leaving assumptions undocumented and outputs unvalidated. Research on reproducible analytics workflows confirms that fragmented processes break auditability, forcing analysts to manually reconstruct transformation steps just to verify a single p-value ^[1]. When your process relies on tribal knowledge and scattered scripts, you aren't doing data analysis—you're doing digital archaeology.

We've seen teams waste entire sprints debugging SettingWithCopyWarnings, fighting ValueErrors from mismatched indices, and manually rebuilding correlation heatmaps for slide decks. You end up spending more time fighting the toolchain than answering the business question. The moment you realize you've been manually orchestrating EDA, hypothesis testing, and visualization without a standardized guardrail is the moment you know it's time to lock the workflow down.

What Fragmented Pipelines Cost in Hours and Trust

Let's talk about the actual bleed. When you skip standardized validation, NaNs slip into final tables. You run a linear regression without checking for multicollinearity or stationarity, then present a model that collapses on the first new data batch. The downstream cost isn't just the 15 to 20 hours you spent debugging environment conflicts and manual exports—it's the delayed decision cycle. A 2021 study on reproducible data analysis workflows found that poorly structured pipelines increase rework by up to 40% because statistical assumptions are never explicitly documented or enforced ^[1]. In business contexts, that translates to missed quarterly targets and misallocated resources. In research, it means desk-rejected manuscripts or retractions.

You're also ignoring visualization standards that could make your charts publication-ready on the first pass. Manual matplotlib tweaks lead to inconsistent DPI, non-accessible color palettes, and chart junk that obscures the actual signal ^[3]. Every time you manually adjust a seaborn theme for a stakeholder deck, you're trading engineering time for design guesswork. The longer you let this slide, the more your team normalizes broken outputs, and the harder it becomes to audit your own work. You start shipping reports that require three rounds of revision because the methodology section doesn't match the code, or the visualization config overrides conflict with the export pipeline. The cost compounds with every project.

A Hypothetical Fintech Team’s Three-Week Detour

Picture a data team at a mid-size fintech company. They're tasked with analyzing transaction logs to identify drivers of churn. They start with a 2GB parquet file. The analyst writes a quick EDA script, drops missing values with dropna(), and runs a quick OLS regression in statsmodels. They export a PNG of a correlation heatmap, paste it into a PowerPoint, and hand it to product. Two weeks later, the model flags a false positive because they never ran a KPSS stationarity test on the time-series component. The regression coefficients are inflated because they ignored heteroscedasticity. The visualization uses a default viridis palette that fails WCAG contrast checks for colorblind stakeholders.

The team spends three days rewriting the pipeline, re-running the analysis, and manually rebuilding the report. This isn't a unique failure mode—it's the default when you lack a locked-in workflow. Best practices dictate that you should define clear business questions upfront, ensure legal compliance, and validate every transformation step before moving to inference ^[5]. Without that guardrail, you're guessing. You're also missing out on structured reporting templates that align methodology, statistical assumptions, and results tables into a single publication-ready document. If you're already using a Data Visualization Pack for dashboarding, you've likely seen how disconnected charting tools create version drift. The same fragmentation happens when your analysis pipeline, validation layer, and presentation template live in three different repositories.

What Changes Once the Pipeline Is Locked

Install the skill, and the chaos stops. The orchestrator (skill.md) tells the agent exactly when to run EDA, when to switch to hypothesis testing, and when to generate the final report. You get a production-grade Python pipeline that handles categorical encoding, json_normalize flattening, MultiIndex reshaping, and named aggregation without writing boilerplate. Statsmodels integration runs KPSS stationarity checks, Rainbow linearity tests, F-test joint hypotheses, and power analysis automatically. You stop guessing sample sizes and start using proper power analysis functions for accurate effect size estimation.

Your visualization config (visualization_config.yaml) centralizes seaborn/matplotlib themes, accessible color palettes, and export DPI standards so every chart looks identical across reports. The validator (check_results.py) loads your outputs and refuses to pass if p-values are missing, coefficients are malformed, or NaNs leaked into the final table. You go from manual debugging to automated validation. You go from "does this chart look right?" to "does this meet publication standards?" ^[6]. The whole chain runs through a single shell script, exits non-zero on failure, and leaves you with a structured markdown report that's ready for stakeholders or peer review.

We engineered this to handle the edge cases that break ad-hoc scripts: sparse arrays that crash standard aggregations, string splitting that leaves trailing whitespace, and dictionary unpacking that silently drops keys. The reference guides (pandas-advanced-manipulation.md, statsmodels-hypothesis-testing.md, visualization-standards.md) give you canonical documentation right next to the code, so you never have to context-switch to a browser just to verify a function signature. If you need to extend this to interactive dashboards, you can pair it with a Data Visualization Pack to maintain consistency between static reports and live BI tools. The pipeline doesn't just run—it enforces rigor.

What’s in the Data Analysis Pack

• skill.md — Orchestrator skill that defines the end-to-end data analysis workflow, instructs the agent on when to use each template, reference, script, and validator, and explicitly references all relative paths.
• templates/analysis_pipeline.py — Production-grade Python pipeline for EDA, hypothesis testing, and regression. Uses pandas for categorical handling, json_normalize, MultiIndex reshaping, and named aggregation. Integrates statsmodels for KPSS stationarity, Rainbow linearity test, F-test joint hypotheses, and power analysis.
• templates/visualization_config.yaml — Centralized configuration for reproducible scientific visualization. Defines seaborn/matplotlib themes, accessible color palettes, typography standards, and export specifications (DPI, format) aligned with research and business reporting requirements.
• templates/report_template.md — Structured markdown template for presenting analytical findings. Includes sections for executive summary, methodology, statistical assumptions, results tables, visualizations, and limitations. Ensures consistent, publication-ready output.
• references/pandas-advanced-manipulation.md — Curated reference of advanced pandas operations extracted from canonical docs. Covers categorical data manipulation, json_normalize with record_path/meta, MultiIndex construction/stacking, sparse arrays, named aggregation with dictionary unpacking, and string splitting.
• references/statsmodels-hypothesis-testing.md — Curated reference of statistical testing and regression techniques from canonical docs. Covers KPSS stationarity, Rainbow linearity test, MANOVA, F-test joint hypotheses, mv_test for coefficient effects, quantile regression, and power analysis functions for sample size estimation.
• references/visualization-standards.md — Authoritative guidelines for data visualization in research and business contexts. Covers chart selection matrices, color theory for accessibility, avoiding chart junk, labeling best practices, and statistical graphic integrity standards.
• scripts/run_analysis.sh — Executable shell script that orchestrates the full workflow: creates virtual environment, installs dependencies, runs the analysis pipeline, generates outputs, and triggers the validator. Exits non-zero if any step fails.
• validators/check_results.py — Programmatic validator that loads pipeline outputs, checks for required statistical metrics (p-values, coefficients, R-squared), validates data types, ensures no NaN leakage in final results, and exits with code 1 on validation failure.
• examples/worked_example.yaml — Worked example demonstrating the complete workflow on a Northwind-style sales dataset. Includes hypothesis formulation, expected statsmodels output structure, visualization config overrides, and a filled report template section.

Stop Guessing, Start Shipping Validated Analysis

You don't need another tutorial on how to use pd.merge(). You need a workflow that enforces statistical rigor, catches data leakage before it hits production, and formats outputs to publication standards. Upgrade to Pro to install the Data Analysis Pack. Run the shell script, watch the validator pass, and ship findings that actually hold up. We built this so you can stop stitching together brittle notebooks and start shipping reproducible, auditable analysis on the first pass.