Imported from majiayu000/claude-skill-registry (skills/development/policyengine-standards-policyengine-policyengine-claude/SKILL.md). Install upstream with npx skills add majiayu000/claude-skill-registry --skill policyengine-standards-policyengine-policyengine-claude. Copyright stays with the author.

PolicyEngine Standards Skill

Use this skill to ensure code meets PolicyEngine's development standards and passes CI checks.

When to Use This Skill

• Before committing code to any PolicyEngine repository
• When CI checks fail with linting/formatting errors
• Setting up a new PolicyEngine repository
• Reviewing PRs for standard compliance
• When AI tools generate code that needs standardization

Critical Requirements

Python Version

⚠️ MUST USE Python 3.13 - Do NOT downgrade to older versions

• Check version: python --version
• Use pyproject.toml to specify version requirements

Command Execution

⚠️ ALWAYS use uv run for Python commands - Never use bare python or pytest

• ✅ Correct: uv run python script.py, uv run pytest tests/
• ❌ Wrong: python script.py, pytest tests/
• This ensures correct virtual environment and dependencies

Documentation (Python Projects)

⚠️ MUST USE Jupyter Book 2.0 (MyST-NB) - NOT Jupyter Book 1.x

• Build docs: myst build docs (NOT jb build)
• Use MyST markdown syntax

Before Committing - Checklist

1. Write tests first (TDD - see below)
2. Format code: make format or language-specific formatter
3. Run tests: make test to ensure all tests pass
4. Check linting: Ensure no linting errors
5. Use config files: Prefer config files over environment variables
6. Reference issues: Include "Fixes #123" in commit message

Creating Pull Requests

The CI Waiting Problem

Common failure pattern:

User: "Create a PR and mark it ready when CI passes"
Claude: "I've created the PR as draft. CI will take a while, I'll check back later..."
[Chat ends - Claude never checks back]
Result: PR stays in draft, user has to manually check CI and mark ready

Solution: Use /create-pr Command

When creating PRs, use the /create-pr command:

/create-pr

This command:

• ✅ Creates PR as draft
• ✅ Actually waits for CI (polls every 15 seconds)
• ✅ Marks ready when CI passes
• ✅ Reports failures with details
• ✅ Handles timeouts gracefully

Why this works: The command contains explicit polling logic that Claude executes, so it actually waits instead of giving up.

If /create-pr is Not Available

If the command isn't installed, implement the pattern directly:

# 1. Create PR as draft
# CRITICAL: Use --repo flag to create PR in upstream repo from fork
gh pr create --repo PolicyEngine/policyengine-us --draft --title "Title" --body "Body"
PR_NUMBER=$(gh pr view --json number --jq '.number')

# 2. Wait for CI (ACTUALLY WAIT - don't give up!)
POLL_INTERVAL=15
ELAPSED=0

while true; do  # No timeout - wait as long as needed
  CHECKS=$(gh pr checks $PR_NUMBER --json status,conclusion)
  TOTAL=$(echo "$CHECKS" | jq '. | length')
  COMPLETED=$(echo "$CHECKS" | jq '[.[] | select(.status == "COMPLETED")] | length')

  echo "[$ELAPSED s] CI: $COMPLETED/$TOTAL completed"

  if [ "$COMPLETED" -eq "$TOTAL" ] && [ "$TOTAL" -gt 0 ]; then
    FAILED=$(echo "$CHECKS" | jq '[.[] | select(.conclusion == "FAILURE")] | length')
    if [ "$FAILED" -eq 0 ]; then
      echo "✅ All CI passed! Marking ready..."
      gh pr ready $PR_NUMBER
      break
    else
      echo "❌ CI failed. PR remains draft."
      gh pr checks $PR_NUMBER
      break
    fi
  fi

  sleep $POLL_INTERVAL
  ELAPSED=$((ELAPSED + POLL_INTERVAL))
done

# Important: No timeout! Population simulations can take 30+ minutes.

DO NOT Say "I'll Check Back Later"

❌ WRONG:

"I've created the PR as draft. CI checks will take a few minutes.
I'll check back later once they complete."

Why wrong: You cannot check back later. The chat session ends.

✅ CORRECT:

"I've created the PR as draft. Now polling CI status every 15 seconds..."
[Actually polls using while loop]
"CI checks completed. All passed! Marking PR as ready for review."

When to Create Draft vs Ready

Always create as draft when:

• CI checks are configured
• User asks to wait for CI
• Making automated changes
• Unsure if CI will pass

Create as ready only when:

• User explicitly requests ready PR
• No CI configured
• CI already verified locally

PR Workflow Standards

Standard flow:

# 1. Ensure branch is pushed
git push -u origin feature-branch

# 2. Create PR as draft (use --repo for cross-fork PRs)
gh pr create --repo PolicyEngine/policyengine-us --draft --title "..." --body "..."

# 3. Wait for CI (use polling loop - see pattern above)

# 4. If CI passes:
gh pr ready $PR_NUMBER

# 5. If CI fails:
echo "CI failed. PR remains draft. Fix issues and push again."

Test-Driven Development (TDD)

PolicyEngine follows Test-Driven Development practices across all repositories.

TDD Workflow

1. Write test first (RED):

# tests/test_new_feature.py
def test_california_eitc_calculation():
    """Test California EITC for family with 2 children earning $30,000."""
    situation = create_family(income=30000, num_children=2, state="CA")
    sim = Simulation(situation=situation)
    ca_eitc = sim.calculate("ca_eitc", 2024)[0]

    # Test fails initially (feature not implemented yet)
    assert ca_eitc == 3000, "CA EITC should be $3,000 for this household"

2. Implement feature (GREEN):

# policyengine_us/variables/gov/states/ca/tax/income/credits/ca_eitc.py
class ca_eitc(Variable):
    value_type = float
    entity = TaxUnit
    definition_period = YEAR

    def formula(tax_unit, period, parameters):
        # Implementation to make test pass
        federal_eitc = tax_unit("eitc", period)
        return federal_eitc * parameters(period).gov.states.ca.tax.eitc.match

3. Refactor (REFACTOR):

# Clean up, optimize, add documentation
# All while tests continue to pass

TDD Benefits

Why PolicyEngine uses TDD:

• ✅ Accuracy - Tests verify implementation matches regulations
• ✅ Documentation - Tests show expected behavior
• ✅ Regression prevention - Changes don't break existing features
• ✅ Confidence - Safe to refactor
• ✅ Isolation - Multi-agent workflow (test-creator and rules-engineer work separately)

TDD in Multi-Agent Workflow

Country model development:

1. @document-collector gathers regulations
2. @test-creator writes tests from regulations (isolated, no implementation access)
3. @rules-engineer implements from regulations (isolated, no test access)
4. Both work from same source → tests verify implementation accuracy

See policyengine-core-skill and country-models agents for details.

Test Examples

Python (pytest):

def test_ctc_for_two_children():
    """Test CTC calculation for married couple with 2 children."""
    situation = create_married_couple(
        income_1=75000,
        income_2=50000,
        num_children=2,
        child_ages=[5, 8]
    )

    sim = Simulation(situation=situation)
    ctc = sim.calculate("ctc", 2024)[0]

    assert ctc == 4000, "CTC should be $2,000 per child"

React (Jest + RTL):

import { render, screen } from '@testing-library/react';
import TaxCalculator from './TaxCalculator';

test('displays calculated tax', () => {
  render(<TaxCalculator income={50000} />);

  // Test what user sees, not implementation
  expect(screen.getByText(/\$5,000/)).toBeInTheDocument();
});

Test Organization

Python:

tests/
├── test_variables/
│   ├── test_income.py
│   ├── test_deductions.py
│   └── test_credits.py
├── test_parameters/
└── test_simulations/

React:

src/
├── components/
│   └── TaxCalculator/
│       ├── TaxCalculator.jsx
│       └── TaxCalculator.test.jsx

Running Tests

Python:

# All tests
make test

# With uv
uv run pytest tests/ -v

# Specific test
uv run pytest tests/test_credits.py::test_ctc_for_two_children -v

# With coverage
uv run pytest tests/ --cov=policyengine_us --cov-report=html

React:

# All tests
make test

# Watch mode
npm test -- --watch

# Specific test
npm test -- TaxCalculator.test.jsx

# Coverage
npm test -- --coverage

Test Quality Standards

Good tests:

• ✅ Test behavior, not implementation
• ✅ Clear, descriptive names
• ✅ Single assertion per test (when possible)
• ✅ Include documentation (docstrings)
• ✅ Based on official regulations with citations

Bad tests:

• ❌ Testing private methods
• ❌ Mocking everything
• ❌ No assertion messages
• ❌ Magic numbers without explanation

Example: TDD for New Feature

# Step 1: Write test (RED)
def test_new_york_empire_state_child_credit():
    """Test NY Empire State Child Credit for family with 1 child.

    Based on NY Tax Law Section 606(c-1).
    Family earning $50,000 with 1 child under 4 should receive $330.
    """
    situation = create_family(
        income=50000,
        num_children=1,
        child_ages=[2],
        state="NY"
    )

    sim = Simulation(situation=situation)
    credit = sim.calculate("ny_empire_state_child_credit", 2024)[0]

    assert credit == 330, "Should receive $330 for child under 4"

# Test fails - feature doesn't exist yet

# Step 2: Implement (GREEN)
# Create variable in policyengine_us/variables/gov/states/ny/...
# Test passes

# Step 3: Refactor
# Optimize, add documentation, maintain passing tests

Python Standards

Formatting

• Formatter: Black with 79-character line length
• Command: make format or black . -l 79
• Check without changes: black . -l 79 --check

# Format all Python files
make format

# Check if formatting is needed (CI-style)
black . -l 79 --check

Code Style

# Imports: Grouped and alphabetized
import os
import sys
from pathlib import Path  # stdlib

import numpy as np
import pandas as pd  # third-party

from policyengine_us import Simulation  # local

# Naming conventions
class TaxCalculator:  # CamelCase for classes
    pass

def calculate_income_tax(income):  # snake_case for functions
    annual_income = income * 12  # snake_case for variables
    return annual_income

# Type hints (recommended)
def calculate_tax(income: float, state: str) -> float:
    """Calculate state income tax.

    Args:
        income: Annual income in dollars
        state: Two-letter state code

    Returns:
        Tax liability in dollars
    """
    pass

# Error handling - catch specific exceptions
try:
    result = simulation.calculate("income_tax", 2024)
except KeyError as e:
    raise ValueError(f"Invalid variable name: {e}")

Testing

import pytest

def test_ctc_calculation():
    """Test Child Tax Credit calculation for family with 2 children."""
    situation = create_family(income=50000, num_children=2)
    sim = Simulation(situation=situation)
    ctc = sim.calculate("ctc", 2024)[0]

    assert ctc == 4000, "CTC should be $2000 per child"

Run tests:

# All tests
make test

# Or with uv
uv run pytest tests/ -v

# Specific test
uv run pytest tests/test_tax.py::test_ctc_calculation -v

# With coverage
uv run pytest tests/ --cov=policyengine_us --cov-report=html

JavaScript/React Standards

Formatting

• Formatters: Prettier + ESLint
• Command: npm run lint -- --fix && npx prettier --write .
• CI Check: npm run lint -- --max-warnings=0

# Format all files
make format

# Or manually
npm run lint -- --fix
npx prettier --write .

# Check if formatting is needed (CI-style)
npm run lint -- --max-warnings=0

Code Style

// Use functional components only (no class components)
import { useState, useEffect } from "react";

function TaxCalculator({ income, state }) {
  const [tax, setTax] = useState(0);

  useEffect(() => {
    // Calculate tax when inputs change
    calculateTax(income, state).then(setTax);
  }, [income, state]);

  return (
    <div>
      <p>Tax: ${tax.toLocaleString()}</p>
    </div>
  );
}

// File naming
// - Components: PascalCase.jsx (TaxCalculator.jsx)
// - Utilities: camelCase.js (formatCurrency.js)

// Environment config - use config file pattern
// src/config/environment.js
const config = {
  API_URL: process.env.NODE_ENV === 'production'
    ? 'https://api.policyengine.org'
    : 'http://localhost:5000'
};
export default config;

React Component Size

• Keep components under 150 lines after formatting
• Extract complex logic into custom hooks
• Split large components into smaller ones

Version Control Standards

Changelog Management

CRITICAL: For PRs, ONLY modify changelog_entry.yaml. NEVER manually update CHANGELOG.md or changelog.yaml.

Terminology Note: When someone says "add a changelog entry" or "needs a changelog entry" in PolicyEngine context, they mean:

• ✅ Create/update changelog_entry.yaml (the PR-level entry file)
• ❌ NOT editing CHANGELOG.md (the main changelog file)
• ❌ NOT editing changelog.yaml (the compiled changelog)

Correct Workflow:

1. Create changelog_entry.yaml at repository root:

   - bump: patch  # or minor, major
     changes:
       added:
       - Description of new feature
       fixed:
       - Description of bug fix
       changed:
       - Description of change

2. Commit ONLY changelog_entry.yaml with your code changes

3. GitHub Actions automatically updates CHANGELOG.md and changelog.yaml on merge

DO NOT:

• ❌ Run make changelog manually during PR creation
• ❌ Commit CHANGELOG.md or changelog.yaml in your PR
• ❌ Modify main changelog files directly

Git Workflow

1. Create branches on PolicyEngine repos, NOT forks

   • Forks cause CI failures due to missing secrets
   • Request write access if needed

2. Branch naming: feature-name or fix-issue-123

3. Commit messages:

   Add CTC reform analysis for CRFB report
   
   - Implement household-level calculations
   - Add state-by-state comparison
   - Create visualizations
   
   Fixes #123
   

4. PR description: Include "Fixes #123" to auto-close issues

Common Git Pitfalls

Never do these:

• ❌ Force push to main/master
• ❌ Commit secrets or .env files
• ❌ Skip hooks with --no-verify
• ❌ Create versioned files (app_v2.py, component_new.jsx)

Always do:

• ✅ Fix original files in place
• ✅ Run formatters before pushing
• ✅ Reference issue numbers in commits
• ✅ Watch CI after filing PR

Common AI Pitfalls

Since many PRs are AI-generated, watch for these common mistakes:

1. File Versioning

❌ Wrong:

# Creating new versions instead of fixing originals
app_new.py
app_v2.py
component_refactored.jsx

✅ Correct:

# Always modify the original file
app.py  # Fixed in place

2. Formatter Not Run

❌ Wrong: Committing without formatting (main cause of CI failures)

✅ Correct:

# Python
make format
black . -l 79

# React
npm run lint -- --fix
npx prettier --write .

3. Environment Variables

❌ Wrong:

// React env vars without REACT_APP_ prefix
const API_URL = process.env.API_URL;  // Won't work!

✅ Correct:

// Use config file pattern instead
import config from './config/environment';
const API_URL = config.API_URL;

4. Using Wrong Python Version

❌ Wrong: Downgrading to Python 3.10 or older

✅ Correct: Use Python 3.13 as specified in project requirements

5. Manual Changelog Updates

❌ Wrong: Running make changelog and committing CHANGELOG.md

✅ Correct: Only create changelog_entry.yaml in PR

Repository Setup Patterns

Python Package Structure

policyengine-package/
├── policyengine_package/
│   ├── __init__.py
│   ├── core/
│   ├── calculations/
│   └── utils/
├── tests/
│   ├── test_calculations.py
│   └── test_core.py
├── pyproject.toml
├── Makefile
├── CLAUDE.md
├── CHANGELOG.md
└── README.md

React App Structure

policyengine-app/
├── src/
│   ├── components/
│   ├── pages/
│   ├── config/
│   │   └── environment.js
│   └── App.jsx
├── public/
├── package.json
├── .eslintrc.json
├── .prettierrc
└── README.md

Makefile Commands

Standard commands across PolicyEngine repos:

make install    # Install dependencies
make test       # Run tests
make format     # Format code
make changelog  # Update changelog (automation only, not manual)
make debug      # Start dev server (apps)
make build      # Production build (apps)

CI Stability

Common CI Issues

1. Fork PRs Fail

• Problem: PRs from forks don't have access to repository secrets
• Solution: Create branches directly on PolicyEngine repos

2. GitHub API Rate Limits

• Problem: Smoke tests fail with 403 errors
• Solution: Re-run failed jobs (different runners have different limits)

3. Linting Failures

• Problem: Code not formatted before commit
• Solution: Always run make format before committing

4. Test Failures in CI but Pass Locally

• Problem: Missing uv run prefix
• Solution: Use uv run pytest instead of pytest

Best Practices Checklist

Code Quality

• Code formatted with Black (Python) or Prettier (JS)
• No linting errors
• All tests pass
• Type hints added (Python, where applicable)
• Docstrings for public functions/classes
• Error handling with specific exceptions

Version Control

• Only changelog_entry.yaml created (not CHANGELOG.md)
• Commit message references issue number
• Branch created on PolicyEngine repo (not fork)
• No secrets or .env files committed
• Original files modified (no _v2 or _new files)

Testing

• Tests written for new functionality
• Tests pass locally with make test
• Coverage maintained or improved
• Edge cases handled

Documentation

• README updated if needed
• Code comments for complex logic
• API documentation updated if needed
• Examples provided for new features

Quick Reference

Format Commands by Language

Python:

make format                # Format code
black . -l 79 --check      # Check formatting
uv run pytest tests/ -v    # Run tests

React:

make format                              # Format code
npm run lint -- --max-warnings=0         # Check linting
npm test                                 # Run tests

Pre-Commit Checklist

# 1. Format
make format

# 2. Test
make test

# 3. Check linting
# Python: black . -l 79 --check
# React: npm run lint -- --max-warnings=0

# 4. Stage and commit
git add .
git commit -m "Description

Fixes #123"

# 5. Push and watch CI
git push

Resources

• Main CLAUDE.md: /PolicyEngine/CLAUDE.md
• Python Style: PEP 8, Black documentation
• React Style: Airbnb React/JSX Style Guide
• Testing: pytest documentation, Jest/RTL documentation
• Writing Style: See policyengine-writing-skill for blog posts, PR descriptions, and documentation

Examples

See PolicyEngine repositories for examples of standard-compliant code:

• policyengine-us: Python package standards
• policyengine-app: React app standards
• givecalc: Streamlit app standards
• crfb-tob-impacts: Analysis repository standards