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Load Testing
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Juicebox CI/CD Data Analysis Testing
juicebox-ci-integration
jeremylongshore/claude-code-plugins-plus-skills
238
Configure automated Continuous Integration and Continuous Deployment (CI/CD) pipelines for Juicebox AI data analysis integrations. This setup utilizes GitHub Actions to run comprehensive unit tests (with mocked datasets) and integration tests (connecting to live APIs) on every PR and merge to main. It ensures that data upload logic, analysis execution, and result parsing workflows are robust and reliable before deployment.
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Exporting Clari Forecasts and Pipeline Snapshots
clari-hello-world
jeremylongshore/claude-code-plugins-plus-skills
58
This guide provides a comprehensive walkthrough for interacting with the Clari API to export critical business data. It covers listing available forecasts, initiating the export of forecast and quota snapshots, monitoring the job status, and ultimately downloading and parsing the structured data. This skill is ideal for testing API connectivity and integrating Clari's revenue intelligence into external data pipelines.
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Deploy GPU Workloads on CoreWeave
coreweave-hello-world
jeremylongshore/claude-code-plugins-plus-skills
436
This guide demonstrates how to deploy GPU workloads on the CoreWeave platform using Kubernetes (kubectl). It covers deploying vLLM inference services for large language model (LLM) testing and running batch CUDA jobs for benchmarking. This is ideal for users running their first GPU workloads, testing inference pipelines, or verifying cluster access on bare-metal GPU nodes (A100, H100).
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Snowflake Load Testing and Scaling Strategies
snowflake-load-scale
jeremylongshore/claude-code-plugins-plus-skills
121
This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.
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Execute Heavy Trading Simulations on Cloud
trader-cloud-backtest
ruvnet/ruflo
417
Dispatches computationally intensive quantitative financial jobs (such as multi-year walk-forward backtesting, large Monte-Carlo simulations, parameter sweeps, and deep model training using neural-trader) to a managed cloud agent. This is necessary for workloads that exceed local computational limits, providing robust, scalable infrastructure for professional quantitative research.
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