code-data-analysis-scaffolds

What this skill does

# Code Data Analysis Scaffolds

## Table of Contents
- [Overview](#overview)
- [Workflow](#workflow)
- [Scaffold Types](#scaffold-types)
- [Guardrails](#guardrails)
- [Quick Reference](#quick-reference)

## Overview

This skill provides structured scaffolds for common technical patterns:

1. **TDD Scaffold**: Given requirements, generate test structure before implementing code
2. **EDA Scaffold**: Given dataset, create systematic exploration plan
3. **Statistical Analysis Scaffold**: Given question, design appropriate statistical test/model
4. **Validation Scaffold**: Given code/model/data, create comprehensive validation checklist

**Skip this skill** when the user wants immediate execution without scaffolding, already has a clear plan, or the task is trivial.

**Quick example:**

> **Task**: "Write authentication function"
>
> **TDD Scaffold**:
> ```python
> # Test structure (write these FIRST)
> def test_valid_credentials():
>     assert authenticate("[email protected]", "correct_pass") == True
>
> def test_invalid_password():
>     assert authenticate("[email protected]", "wrong_pass") == False
>
> def test_nonexistent_user():
>     assert authenticate("[email protected]", "any_pass") == False
>
> def test_empty_credentials():
>     with pytest.raises(ValueError):
>         authenticate("", "")
>
> # Now implement authenticate() to make tests pass
> ```

## Workflow

Copy this checklist and track your progress:

```
Code Data Analysis Scaffolds Progress:
- [ ] Step 1: Clarify task and objectives
- [ ] Step 2: Choose appropriate scaffold type
- [ ] Step 3: Generate scaffold structure
- [ ] Step 4: Validate scaffold completeness
- [ ] Step 5: Deliver scaffold and guide execution
```

**Step 1: Clarify task and objectives**

Ask user for the task, dataset/codebase context, constraints, and expected outcome. Determine if this is TDD (write tests first), EDA (explore data), statistical analysis (test hypothesis), or validation (check quality). See [resources/template.md](resources/template.md) for context questions.

**Step 2: Choose appropriate scaffold type**

Based on task, select scaffold: TDD (testing code), EDA (exploring data), Statistical Analysis (hypothesis testing, A/B tests), Causal Inference (estimating treatment effects), Predictive Modeling (building ML models), or Validation (checking quality). See [Scaffold Types](#scaffold-types) for guidance on choosing.

**Step 3: Generate scaffold structure**

Create systematic framework with clear steps, validation checkpoints, and expected outputs at each stage. For standard cases use [resources/template.md](resources/template.md); for advanced techniques see [resources/methodology.md](resources/methodology.md).

**Step 4: Validate scaffold completeness**

Check scaffold covers all requirements, includes validation steps, makes assumptions explicit, and provides clear success criteria. Self-assess using [resources/evaluators/rubric_code_data_analysis_scaffolds.json](resources/evaluators/rubric_code_data_analysis_scaffolds.json) - minimum score ≥3.5.

**Step 5: Deliver scaffold and guide execution**

Present scaffold with clear next steps. If user wants execution help, follow the scaffold systematically. If scaffold reveals gaps (missing data, unclear requirements), surface these before proceeding.

## Scaffold Types

### TDD (Test-Driven Development)
**When**: Writing new code, refactoring existing code, fixing bugs
**Output**: Test structure (test cases → implementation → refactor)
**Key Elements**: Test cases covering happy path, edge cases, error conditions, test data setup

### EDA (Exploratory Data Analysis)
**When**: New dataset, data quality questions, feature engineering
**Output**: Exploration plan (data overview → quality checks → univariate → bivariate → insights)
**Key Elements**: Data shape/types, missing values, distributions, outliers, correlations

### Statistical Analysis
**When**: Hypothesis testing, A/B testing, comparing groups
**Output**: Analysis design (question → hypothesis → test selection → assumptions → interpretation)
**Key Elements**: Null/alternative hypotheses, significance level, power analysis, assumption checks

### Causal Inference
**When**: Estimating treatment effects, understanding causation not just correlation
**Output**: Causal design (DAG → identification strategy → estimation → sensitivity analysis)
**Key Elements**: Confounders, treatment/control groups, identification assumptions, effect estimation

### Predictive Modeling
**When**: Building ML models, forecasting, classification/regression tasks
**Output**: Modeling pipeline (data prep → feature engineering → model selection → validation → evaluation)
**Key Elements**: Train/val/test split, baseline model, metrics selection, cross-validation, error analysis

### Validation
**When**: Checking data quality, code quality, model quality before deployment
**Output**: Validation checklist (assertions → edge cases → integration tests → monitoring)
**Key Elements**: Acceptance criteria, test coverage, error handling, boundary conditions

## Guardrails

- **Clarify before scaffolding** - Don't guess what user needs; ask clarifying questions first
- **Distinguish causal vs predictive** - Causal inference needs different methods than prediction (RCT/IV vs ML)
- **Make assumptions explicit** - Every scaffold has assumptions (data distribution, user behavior, system constraints)
- **Include validation steps** - Scaffold should include checkpoints to validate work at each stage
- **Provide examples** - Show what good looks like (sample test, sample EDA visualization, sample model evaluation)
- **Surface gaps early** - If scaffold reveals missing data/requirements, flag immediately
- **Avoid premature optimization** - Start with simple scaffold, add complexity only if needed
- **Follow best practices** - TDD: test first, EDA: start with data quality, Modeling: baseline before complex models

## Quick Reference

| Task Type | When to Use | Scaffold Resource |
|-----------|-------------|-------------------|
| **TDD** | Writing/refactoring code | [resources/template.md](resources/template.md) #tdd-scaffold |
| **EDA** | Exploring new dataset | [resources/template.md](resources/template.md) #eda-scaffold |
| **Statistical Analysis** | Hypothesis testing, A/B tests | [resources/template.md](resources/template.md) #statistical-analysis-scaffold |
| **Causal Inference** | Treatment effect estimation | [resources/methodology.md](resources/methodology.md) #causal-inference-methods |
| **Predictive Modeling** | ML model building | [resources/methodology.md](resources/methodology.md) #predictive-modeling-pipeline |
| **Validation** | Quality checks before shipping | [resources/template.md](resources/template.md) #validation-scaffold |
| **Examples** | See what good looks like | [resources/examples/](resources/examples/) |
| **Rubric** | Validate scaffold quality | [resources/evaluators/rubric_code_data_analysis_scaffolds.json](resources/evaluators/rubric_code_data_analysis_scaffolds.json) |