policyengine-claude

Recharts chart patterns, formatting, and styling for PolicyEngine apps

PolicyEngine code writing style guide - formula optimization, direct returns, eliminating unnecessary variables

PolicyEngine parameter patterns - YAML structure, naming conventions, metadata requirements, federal/state separation

PolicyEngine code review patterns - validation checklist, common issues, review standards

PolicyEngine testing patterns - YAML test structure, naming conventions, period handling, and quality standards

PolicyEngine variable patterns - variable creation, no hard-coding principle, federal/state separation, metadata standards

Reference for the /create-dashboard and /deploy-dashboard orchestrated AI workflow

Mandatory frontend technology requirements for PolicyEngine dashboards and interactive tools — Tailwind CSS v4, Next.js (App Router), @policyengine/ui-kit theme, Vercel deployment

Deploying PolicyEngine backend APIs to Modal — workspace setup, authentication, deployment commands, environments, and troubleshooting

PolicyEngine vectorization patterns - NumPy operations, where/select usage, avoiding scalar logic with arrays

Code organization patterns for PolicyEngine - variable naming conventions, folder structure, file organization

PolicyEngine aggregation patterns - using adds attribute and add() function for summing variables across entities

PolicyEngine period handling - converting between YEAR, MONTH definition periods and testing patterns

PolicyEngine reform patterns - factory functions, contrib parameters, in_effect toggles, registration, and test keys for contributed policy reforms

This skill should be used when writing unit tests, integration tests, or test fixtures for PolicyEngine frontend apps, APIs, SDKs, and standalone tools. NOT for country model packages (policyengine-us, policyengine-uk, etc.) — those use YAML-based tests with their own conventions. Covers the Given-When-Then naming convention, fixture extraction, edge case coverage, and the rule that only modified test files should be run. Triggers: "write tests", "add tests", "unit test", "test file", "test coverage", "write a test for", "test this function", "test this component", "given when then", "test fixtures", "mock setup", "edge cases", "test naming", "test convention"

Developing policyengine-app-v2 — the main React frontend for policyengine.org

ALWAYS LOAD THIS SKILL before writing any policyengine.py microsimulation code. Contains correct import paths, environment setup, dataset loading, and analysis patterns. Triggers: "write a script", "policyengine.py", "microsimulation script", "run a simulation", "load the dataset", "FRS", "EFRS", "enhanced FRS", "CPS", "enhanced CPS", "by income decile", "by tenure", "by region", "energy spending", "domestic energy", "household net income", "output_dataset", "ensure_datasets", "uk_datasets", "us_datasets", "import datasets", "from policyengine", "Simulation(dataset=", "uk_latest", "us_latest", "plotly", "analysis script", "decile breakdown", "percentile", "groupby", "weighted", "mean", "median", "p25", "p75", "tenure type", "income band", "policy reform script".

PolicyEngine Core simulation engine - the foundation powering all PolicyEngine calculations

Common analysis patterns for PolicyEngine research repositories (CRFB, newsletters, dashboards, impact studies). For population-level estimates (cost, poverty, distributional impacts), use the policyengine-microsimulation skill instead.

Generate marketing content from PolicyEngine blog posts - social media images, social post copy, and branded assets

SEO first principles for PolicyEngine web applications - meta tags, crawlability, performance, and dual-mode (standalone + iframe) considerations

L0 regularization for neural network sparsification and intelligent sampling - used in survey calibration. Triggers: "L0", "sparsification", "sample selection", "hard concrete", "sparse weights", "household selection", "gate", "survey sparsity", "l0-python"

Survey weight calibration to match population targets - used in policyengine-us-data for enhanced microdata. Triggers: "calibrate", "calibration", "survey weights", "reweighting", "population targets", "benchmarks", "microcalibrate", "weight adjustment", "target matching"

Weighted pandas DataFrames for survey microdata analysis - inequality, poverty, and distributional calculations. Triggers: "weighted mean", "Gini", "poverty rate", "inequality", "MicroDataFrame", "MicroSeries", "weighted statistics", "decile", "quintile", "income distribution", "microdf"

ML-based variable imputation for survey data - used in policyengine-us-data to fill missing values. Triggers: "impute", "imputation", "missing values", "donor", "recipient", "quantile forest", "statistical matching", "PUF", "microimpute", "fill missing"

UK survey data enhancement - FRS with WAS imputation patterns and cross-repo variable workflows. Triggers: "FRS", "Family Resources Survey", "WAS", "Wealth and Assets Survey", "UK data", "UK microdata", "wealth imputation", "policyengine-uk-data"

US survey data enhancement - CPS with PUF imputation patterns and cross-repo variable workflows. Triggers: "CPS", "Current Population Survey", "PUF", "Public Use File", "US data", "US microdata", "enhanced CPS", "policyengine-us-data", "cross-repo", "FINANCIAL_SUBSET"

Find and reference PolicyEngine blog posts, research articles, and published analyses for evidence and proof points. Triggers: "find blog post", "PolicyEngine research", "published analysis", "proof point", "has PolicyEngine written about", "blog post about"

Using PolicyEngine web apps to analyze tax and benefit policy impacts - for users of policyengine.org. Triggers: "how to use PolicyEngine", "household calculator", "policy reform on website", "policyengine.org walkthrough", "web app guide"

PolicyEngine writing style for blog posts, documentation, PR descriptions, and research reports - emphasizing active voice, quantitative precision, and neutral tone

ALWAYS LOAD THIS SKILL FIRST before writing any PolicyEngine-UK code. Contains the correct API patterns for household calculations and population simulations using the new policyengine package (not policyengine_uk directly). Triggers: "what would", "how much would a", "benefit be", "eligible for", "qualify for", "single parent", "married couple", "family of", "household of", "if they earn", "with income of", "earning £", "making £", "calculate benefits", "calculate taxes", "benefit for a", "tax for a", "what would I get", "what would they get", "what is the rate", "what is the threshold", "personal allowance", "maximum benefit", "income limit", "benefit amount", "how much is", "Universal Credit", "child benefit", "pension credit", "housing benefit", "council tax", "income tax", "national insurance", "JSA", "ESA", "PIP", "disability living allowance", "working tax credit", "child tax credit", "Scotland", "Wales", "UK", "microsimulation", "population", "reform", "policy impact", "budgetary", "decile".

ALWAYS LOAD THIS SKILL FIRST before writing any PolicyEngine-US code. Contains the correct API patterns for household calculations and population simulations using the new policyengine package. Covers US federal and state taxes/benefits. Triggers: "what would", "how much would a", "benefit be", "eligible for", "qualify for", "single parent", "married couple", "family of", "household of", "if they earn", "earning $", "making $", "calculate benefits", "calculate taxes", "benefit for a", "what would I get", "what is the maximum", "what is the rate", "poverty line", "income limit", "benefit amount", "maximum benefit", "compare states", "TANF", "SNAP", "EITC", "CTC", "SSI", "WIC", "Section 8", "Medicaid", "ACA", "child tax credit", "earned income", "supplemental security", "housing voucher", "microsimulation", "population", "reform", "policy impact", "budgetary", "decile".

Testing patterns for PolicyEngine data generation pipelines (policyengine-us-data, policyengine-uk-data)

PolicyEngine API - Flask REST service powering policyengine.org and programmatic access

Guidance for working with the PolicyEngine GitHub agent bot

Analyze policy impacts for congressional districts and representatives' constituents. Use when the user mentions a specific district (NY-17, CA-52), a representative's name, or asks about geographic policy impacts at district level. Provides HuggingFace district datasets.