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Everything your AI needs, in one place.
Ready-made agents, skills, personas, prompts, templates and tools. Each one is checked before it goes live, works with any model, and installs in a click. Rate what you use so the best rises to the top.
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A service that does a whole job for you - research, coding, support - and reports back.
Step-by-step instructions an AI follows for one kind of task. Install once, reuse everywhere.
A voice and set of rules layered onto any model: tone, audience, do's and don'ts.
A ready-to-use prompt with fill-in-the-blank variables and notes on when it works best.
A complete routing setup - models, rules and settings - in one file you can apply in a minute.
A single function an AI can call: a calculator, a search, a database lookup.
A language model endpoint with its price, speed and quality declared so the router can compare it.
pandera
Expert guidance for Pandera, the Python library for validating pandas and Polars DataFrames with expressive schemas. Helps developers define data contracts, validate data pipelines, and catch data qua
polars
Polars is a blazingly fast DataFrame library written in Rust with a Python API. Learn eager and lazy evaluation, expressions, groupby, joins, and how Polars outperforms pandas for large datasets throu
Time Series Imputation Feasibility Analysis
Analyze the feasibility of imputing missing data for short time series by checking date alignment with similar series based on shared key columns using Polars.
statsforecast_polars_ensemble_pipeline
Execute a univariate time series forecasting pipeline using StatsForecast and Polars. Includes ID concatenation, cross-validation, ensemble generation (AutoARIMA, AutoETS, DynamicOptimizedTheta), non-
polars
Write fast, idiomatic Polars in Python using the lazy API, and answer natural-language questions about data in one shot. Use this skill whenever a task involves loading, querying, transforming, aggreg
polars
Polars workflow skill. Use this skill when the user needs Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, par
polars-v2
Polars workflow skill. Use this skill when the user needs Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, par
Find
Search or browse by kind. Every card shows who made it, how many people installed it and what they think.
Install
One click. You get a manifest the router understands, plus copy-paste snippets for the CLI, Python and YAML.
Rate and publish
Leave a star rating after you have used it. Made something useful? Publish it - free listings go live immediately.
Prefer the terminal? osr stack apply registry://starter installs the starter template.