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Found 26 Skills
Extract and analyze Agentforce session tracing data from Salesforce Data 360. Supports high-volume extraction (1-10M records/day), Polars-based analysis, and debugging workflows for agent sessions.
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
Use when writing or modifying Python code that imports `xolars` — pairing an xarray Dataset with per-dimension Polars frames, constructing the `Xolars` container, selecting with isel/sel, or doing zarr+parquet round-trips via write/open. Skip for plain xarray or polars work that doesn't touch the `Xolars` class.
Interactive onboarding wizard to set up Polar payment integration from scratch. Use this skill when: (1) User wants to "set up Polar" or "integrate Polar" in their project; (2) User is starting fresh with Polar and needs guided setup; (3) User asks "how do I get started with Polar"; (4) User wants to add payments/subscriptions/checkout to their app using Polar; (5) User needs help creating their first Polar product and checkout. This skill walks through MCP installation, authentication, product creation, and generates framework-specific integration code.
Local development environment management for Polar using Docker
Guide for migrating to Polar from other payment platforms (Stripe Billing, Paddle, Lemon Squeezy, Gumroad). Use this skill when: (1) Planning a migration from another payment provider to Polar; (2) Migrating customer data and subscriptions; (3) Running parallel systems during transition; (4) Mapping products and pricing between platforms; (5) Handling payment method migration; (6) Communicating migration to customers; (7) Testing migration in sandbox before production.
Guide for testing Polar payment integrations using the sandbox environment. Use this skill when: (1) Setting up the Polar sandbox for development; (2) Testing checkout flows without real payments; (3) Using Stripe test cards with Polar; (4) Writing integration tests for payment flows; (5) Testing webhooks locally with ngrok; (6) Mocking Polar in unit tests; (7) Setting up CI/CD pipelines with Polar sandbox; (8) Debugging payment issues in sandbox.
Add Polar billing to a TypeScript/JavaScript app using the @polar-sh/sdk package. Use this skill whenever the user wants to add a Checkout endpoint, a Customer Portal endpoint, or a Webhooks endpoint for Polar to any framework — Next.js, Express, Hono, Astro, SvelteKit, Remix, TanStack Start, Nuxt, Fastify, Elysia, Deno, Supabase Edge Functions, Cloudflare Workers, Bun, etc.
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Executive-grade data analysis with pandas/polars and McKinsey-quality visualizations. Use when analyzing data, building dashboards, creating investor presentations, or calculating SaaS metrics.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.
Best practices for developing tools, dashboards and interactive data apps with HoloViz Panel. Create reactive, component-based UIs with widgets, layouts, templates, and real-time updates. Use when developing interactive data exploration tools, dashboards, data apps, or any interactive Python web application. Supports file uploads, streaming data, multi-page apps, and integration with HoloViews, hvPlot, Pandas, Polars, DuckDB and the rest of the HoloViz and PyData ecosystems.