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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 impa...
PolicyEngine React web application - the user interface at policyengine.org
PolicyEngine period handling - converting between YEAR, MONTH definition periods and testing patterns
PolicyEngine aggregation patterns - using adds attribute and add() function for summing variables across entities
PolicyEngine writing style for blog posts, documentation, PR descriptions, and research reports - emphasizing active voice, quantitative precision, and neutral tone
UK survey data enhancement - FRS with WAS imputation patterns
PolicyEngine API - Flask REST service powering policyengine.org and programmatic access
PolicyEngine variable patterns - variable creation, no hard-coding principle, federal/state separation, metadata standards
PolicyEngine-US federal and state tax/benefit rules - variables, parameters, household situations. For national/state microsimulation see policyengine-microsimulation; for district-level see policyengine-distri...
Using PolicyEngine web apps to analyze tax and benefit policy impacts - for users of policyengine.org
Weighted pandas DataFrames for survey microdata analysis - inequality, poverty, and distributional calculations
PolicyEngine code writing style guide - formula optimization, direct returns, eliminating unnecessary variables
PolicyEngine-UK tax and benefit microsimulation patterns, situation creation, and common workflows
ALWAYS USE THIS SKILL for PolicyEngine microsimulation, population-level analysis, winners/losers calculations. Triggers: "microsimulation", "share who would lose/gain", "policy impact", "national average", wei...
ML-based variable imputation for survey data - used in policyengine-us-data to fill missing values
PolicyEngine Core simulation engine - the foundation powering all PolicyEngine calculations
PolicyEngine coding standards, formatters, CI requirements, and development best practices
Code organization patterns for PolicyEngine - variable naming conventions, folder structure, file organization
Testing patterns for PolicyEngine data generation pipelines (policyengine-us-data, policyengine-uk-data)
L0 regularization for neural network sparsification and intelligent sampling - used in survey calibration
Common analysis patterns for PolicyEngine research repositories (CRFB, newsletters, dashboards, impact studies)
PolicyEngine parameter patterns - YAML structure, naming conventions, metadata requirements, federal/state separation
ML 모델 벤치마크 및 평가 실행. "벤치마크", "모델 평가", "성능 테스트", "inference 속도" 요청 시 활성화됩니다.
사용자의 개발 프로필 정보를 관리합니다. 사용자의 기술 스택, 선호도, 경험 수준에 대한 질문이 필요할 때 자동으로 활성화되며, ~/.agents/WHOAMI.md 파일에서 정보를 읽습니다. 파일이 없으면 대화형으로 프로필을 생성합...
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