Code Data Analysis Scaffolds

Table of Contents

• Purpose
• When to Use This Skill
• When NOT to Use This Skill
• What Is It?
• Workflow
• Scaffold Types
• Guardrails
• Quick Reference

Purpose

This skill provides structured scaffolds (frameworks, checklists, templates) for technical work in software engineering and data science. It helps you approach complex tasks systematically by defining what to do, in what order, and what to validate before proceeding.

When to Use This Skill

Use this skill when you need to:

• Write tests systematically - TDD scaffolding, test suite design, test data generation
• Explore data rigorously - EDA plans, data quality checks, feature analysis strategies
• Design statistical analyses - A/B tests, causal inference, hypothesis testing frameworks
• Build predictive models - Model selection, validation strategy, evaluation metrics
• Refactor with confidence - Test coverage strategy, refactoring checklist, regression prevention
• Validate technical work - Data validation, model evaluation, code quality checks
• Clarify technical approach - Distinguish causal vs predictive goals, choose appropriate methods

Trigger phrases:

• "Write tests for [code/feature]"
• "Explore this dataset"
• "Analyze [data/problem]"
• "Build a model to predict..."
• "How should I validate..."
• "Design an A/B test for..."
• "What's the right approach to..."

When NOT to Use This Skill

Skip this skill when:

• Just execute, don't plan - User wants immediate code/analysis without scaffolding
• Scaffold already exists - User has clear plan and just needs execution help
• Non-technical tasks - Use appropriate skill for writing, planning, decision-making
• Simple one-liners - No scaffold needed for trivial tasks
• Exploratory conversation - User is brainstorming, not ready for structured approach yet

What Is It?

Code Data Analysis Scaffolds provides structured frameworks 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

Quick example:

Task: "Write authentication function"

TDD Scaffold:

# Test structure (write these FIRST)
def test_valid_credentials():
    assert authenticate("user@example.com", "correct_pass") == True

def test_invalid_password():
    assert authenticate("user@example.com", "wrong_pass") == False

def test_nonexistent_user():
    assert authenticate("nobody@example.com", "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 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 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; for advanced techniques see 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 - 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, unclea

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