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PolicyEngine AI Skills

GitHub ↗

Explorez les skills IA publiés de PolicyEngine et consultez leurs instructions source.

Statistiques de l’auteur

Skills indexés

29

Étoiles des dépôts des skills indexés

145

Forks du dépôt

29

Moyenne des étoiles par skill indexé

5

Les étoiles d’un dépôt peuvent se répéter entre ses skills.

29 skills

Explorer

GitHub

microcalibrate

PolicyEngine

Survey weight calibration to match population targets - used in policyengine-us-data for enhanced microdata

GitHub

policyengine-design

PolicyEngine

PolicyEngine visual identity - colors, fonts, logos, and branding for web apps, calculators, charts, and research

GitHub

policyengine-district-analysis

PolicyEngine

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...

GitHub

policyengine-writing

PolicyEngine

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

GitHub

policyengine-us

PolicyEngine

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...

GitHub

microdf

PolicyEngine

Weighted pandas DataFrames for survey microdata analysis - inequality, poverty, and distributional calculations

GitHub

policyengine-uk

PolicyEngine

PolicyEngine-UK tax and benefit microsimulation patterns, situation creation, and common workflows

GitHub

policyengine-microsimulation

PolicyEngine

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...

GitHub

microimpute

PolicyEngine

ML-based variable imputation for survey data - used in policyengine-us-data to fill missing values

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.