Claude skills for data science: Automated EDA, SHAP feature engineering, ML pipelines, A/B tests, LLM evaluation, and data quality contracts

This guide describes a pragmatic, production-ready set of Claude skills for data science—an opinionated suite that automates exploratory data analysis (EDA), drives feature engineering with SHAP, scaffolds machine learning pipelines, designs statistical A/B tests, evaluates LLM outputs, and generates enforceable data-quality contracts.
The goal: actionable steps and reusable snippets you can drop into a workflow or extend via the linked repo.

If you want to jump straight to code and templates, check the skill pack on GitHub: Claude skills for data science.
Each anchor below references full examples and YAML/JSON configurations you can adapt.

This article balances technical depth with pragmatic narrative: expect explanation, concise examples, and integration tips for engineers and ML product owners.

Why a Claude skill suite matters for data teams

Large language models (LLMs) like Claude excel at orchestrating routine analysis tasks, documenting rationale, and generating scaffolds—precisely the repetitive parts of the data science lifecycle. Packaging these capabilities into focused skills—EDA, SHAP-based explanation, pipeline scaffolding, A/B design, LLM evaluation, and data-quality contracts—reduces cognitive overhead and speeds reproducibility.

Teams gain consistency: the same prompts and templates produce standardized EDA reports, feature explanations, and pipeline manifests. That consistency makes downstream governance, peer review, and auditing feasible without manual enforcement.

Claude skills also enable rapid onboarding: new analysts can run an automated EDA report and get annotated charts, relevant statistical tests, and suggested features—each with recommended code snippets and limitations flagged by the model.

Automated EDA report: what it should include and how Claude helps

A robust automated EDA contains three core parts: data diagnostics (missingness, types, cardinalities), univariate and bivariate summaries, and anomaly detection / drift signals. The Claude skill should generate a concise executive summary, a prioritized list of issues, and executable code cells (Pandas/Polars/SQL) to reproduce analyses.

Claude can format an EDA report for both humans and machines: human-readable narrative with insights and a machine-readable JSON block containing metrics (null ratios, unique counts, skewness, correlation matrix). This dual output supports automation—dashboards can ingest the JSON while analysts read the commentary.

Implementation tip: design the skill to accept a schema and sample rows, then run lightweight computations server-side (or in a sandbox) and feed aggregated stats back to Claude for interpretation. Store reproducible artifact links in the report. For example, link to a Jupyter notebook or the repo: automated EDA report templates.

Feature engineering with SHAP: explain, propose, and validate features

Feature engineering guided by SHAP shifts the focus from purely statistical heuristics to interpretable impact on model predictions. A Claude skill that integrates SHAP should produce: feature importance ranked by mean |SHAP|, dependence plots recommendations, and candidate feature transformations (e.g., bucketing, interactions) with rationale.

The skill should also propose hypothesis-driven features and provide quick ablation tests. For each proposed feature, the output should include expected directionality (positive/negative effect), a small code snippet to compute it, and a suggested validation (cross-validated improvement or A/B-like holdout evaluation).

For reproducibility, embed SHAP plots and concise textual explanations. Use links back to the pipeline scaffold so approved features automatically propagate into the ML pipeline scaffolding stage: see the ML pipeline scaffold repo examples at feature engineering with SHAP.

ML pipeline scaffold: from prototype to reproducible production

A minimal, maintainable ML pipeline scaffold enforces separation of concerns: data ingestion, validation, transformations, model training, evaluation, and deployment artifacts. The Claude skill should emit a structured manifest (YAML/JSON) and recommended file layout (e.g., data/, src/, experiments/, models/).

The scaffold should include automated tests: unit tests for feature calculators, integration tests that replay sample data, and a lightweight smoke test for inference serving. Claude can generate test templates and CI snippets (GitHub Actions) to run them on each pull request, raising quality and catch regressions early.

Use the scaffold to embed metadata for model cards—training data stats, validation metrics, bias checks, and SHAP-based explanations. This metadata makes downstream monitoring and audit straightforward and ensures the model lifecycle is traceable.

Statistical A/B test design: practical, defensible experiments

Designing A/B tests requires choosing metrics, sample sizes, randomization strategies, and statistical methods (frequentist vs Bayesian). A Claude skill for A/B design should produce: a test plan (hypotheses, primary/secondary metrics), power calculations, sequential testing guidance, and pre-registration text.

Claude can speed iterations by generating recommended sample sizes from baseline conversion rates, minimum detectable effects, and desired power. It should also warn about common pitfalls—peeking, correlated metrics, and non-independence—and propose guardrails like max running time or group-level randomization.

For production usage, have the skill output a test manifest and monitoring hooks to detect metric drift, segmentation effects, and early stopping criteria. Exportable artifacts can be linked to the pipeline scaffold so experiment results automatically write back into the experiment tracking system.

LLM output evaluation: scoring, calibration, and bias checks

Evaluating LLM outputs requires both automated metrics and human-in-the-loop checks. Claude skills can pre-score outputs by applying rubric-based criteria (factuality, relevance, safety) and surface failure modes. For factuality, integrate external retrievers or grounding checks; for safety, apply policy filters and categorize risk levels.

Provide calibration diagnostics: measure confidence vs accuracy, track hallucination rates by category, and recommend calibration methods like temperature adjustments or reranking. Claude can generate test suites for common prompt categories and produce a confusion-style report for error analysis.

For continuous validation, hook the evaluation skill into monitoring pipelines. Summaries should include recommended mitigation steps—retrieval augmentation, prompt engineering changes, or explicit guardrails—and a minimal reproducible prompt/test case to replicate failures.

Data quality contract generation: enforceable, versioned checks

A data quality contract formalizes expectations about schema, feature distributions, cardinality, and lineage. Claude can draft contracts from sample statistics, producing human-readable clauses and machine-checkable rules (e.g., Great Expectations suites or SQL assertions).

Contracts should be versioned alongside code and include severity levels (warn/error), suggested remediation steps, and owners. The skill should export both a policy document and an executable test bundle that integrates into CI/CD to prevent breaking changes from merging.

Integrate contracts into the pipeline scaffold so deployments fail fast on contract violations and alerts include the specific rule, failing sample rows, and a link to the originating contract in the repo.

How to combine these skills in a production workflow

Start small: run the automated EDA skill on the first dataset to generate a report and data-quality contract. If the dataset is model-ready, run the SHAP-guided feature engineering skill to generate candidate features and ablation tests. Next, use the ML pipeline scaffold skill to incorporate approved features, CI tests, and model cards.

While the model is training, prepare an A/B test design with the A/B skill and bake evaluation hooks into the deployment. Once serving, put the LLM evaluation and data-quality contract checks into the monitoring loop so drift and regressions surface immediately.

This orchestration can be automated: use the Claude skills as building blocks called by a task runner or orchestration layer, and persist artifacts to experiment tracking, model registries, and data catalogs. See the repository for orchestration examples and templates: Claude skills for data science repo.

Quick implementation checklist

• Run automated EDA and generate JSON metrics and narrative summary.
• Use SHAP to propose features and produce ablation tests.
• Emit a pipeline scaffold with CI tests and model card metadata.
• Pre-register A/B tests and export power/sample-size calculations.
• Generate data-quality contracts and integrate them into CI.
• Set up LLM evaluation suites for ongoing monitoring.

Semantic core (expanded keyword set)

Selected user questions (FAQ)

Micro-markup recommendation

To enable rich results and better indexing, add JSON-LD FAQ and Article microdata to your page. Example FAQ snippet (add to the page head or just before </body>):

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      "acceptedAnswer":{"@type":"Answer","text":"A Claude skill collects aggregated statistics ... persisted to repo or experiment store."}
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      "@type":"Question",
      "name":"Can SHAP-guided feature suggestions be trusted for production?",
      "acceptedAnswer":{"@type":"Answer","text":"Use SHAP suggestions as hypotheses, run ablation and CV tests, monitor OOS performance."}
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      "@type":"Question",
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References and further reading

The companion repository contains skill templates, examples, and CI snippets: Claude skills for data science GitHub.
Use that repo as the canonical implementation reference and adapt YAML manifests to your orchestration platform.

If you want a quick start, pull the automated EDA and pipeline scaffold folders, run the example notebooks, and iterate your prompts—Claude’s interpretability makes prompt refinement productive.

Good luck. If you need a walk-through for integrating a specific skill into your CI/CD pipeline, ask and I’ll provide an orchestration example targeted to your stack.