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By uw-ssec
Benchmark and optimize Zarr chunking strategies for multi-dimensional scientific datasets on S3/GCS: analyze access patterns from xarray workflows, generate synthetic data, run comprehensive benchmarks across spatial/temporal/spectral patterns, produce ranked recommendations with trade-off reports, and safely rechunk datasets.
npx claudepluginhub uw-ssec/rse-plugins --plugin zarr-chunk-optimizationAnalyze benchmark results and generate a performance report with ranked recommendations and trade-off explanations
Run comprehensive chunking benchmarks on Zarr dataset and generate performance report with recommendations
Generate a synthetic Zarr dataset for controlled chunking benchmarks with configurable dimensions and compression
Apply a specific chunking configuration to a Zarr dataset with validation and progress reporting
Explore chunking trade-offs from existing benchmark results for different access pattern scenarios
Zarr chunking optimization expert that benchmarks multi-dimensional array storage for cloud object stores (S3, GCS) and generates recommendations based on Nguyen et al. (2023) methodology.
Expert in interpreting Zarr chunking benchmark results, analyzing performance trade-offs across access patterns, and generating actionable recommendations with research-backed context from Nguyen et al. (2023).
Identify, formalize, and prioritize data access patterns for multi-dimensional Zarr datasets. Translates user workflow descriptions into benchmark-ready pattern definitions with xarray operation mappings.
Benchmark and optimize Zarr chunking strategies for multi-dimensional scientific datasets. Measures wall-clock time, peak memory, and I/O metrics across spatial, time-series, and spectral access patterns following Nguyen et al. (2023) methodology.
Generate structured benchmark reports with configuration comparisons, performance bias analysis, ranked recommendations, and trade-off explanations from Zarr chunking benchmark results.
Safely apply chunking configurations to Zarr datasets with validation, progress reporting, memory-bounded execution, and rollback safety. Supports local and cloud storage backends.
Generate synthetic Zarr datasets with configurable dimensions, shapes, data types, and compression for controlled chunking benchmarks. Supports local and cloud storage backends (S3, GCS).
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This repository provides specialized agents and skills that understand the unique challenges of scientific software development, including:
To use these agents and skills in Claude Code, add this repository to your plugin marketplace:
/plugin marketplace add uw-ssec/rse-plugins
Once installed, the agents and skills will be available in your Claude Code environment and can be invoked when working on scientific software projects.
The repository provides Claude Code plugins organized by domain. Each plugin contains agents (specialized AI personas) and skills (reusable knowledge modules).
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Agents:
Skills:
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Agents:
Skills:
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Agent:
Commands:
/research - Document and understand existing code, patterns, and architecture/plan - Create detailed, testable implementation plans through interactive research/iterate-plan - Refine existing plans based on feedback or changed requirements/experiment - Try multiple approaches before committing to implementation (optional)/implement - Execute the plan phase by phase with verification checkpoints/validate - Systematically verify implementation against plan criteriaSkill:
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