Manage files in GCS, CoreWeave S3, R2, and local storage through Marin's fsutil command-line tool. List, inspect, copy, move, or synchronize data using bucket connections defined in the checkout's configuration.
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Use Marin's fsutil CLI for local object-storage operations across GCS, CoreWeave S3, R2, and local paths. Use whenever a task needs to list, inspect, read, size, find, copy, move, synchronize, hash, browse, or remove data at gs:// or s3:// URLs from a checkout. Do not launch an Iris job solely to access object storage.
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Use uv run fsutil from the Marin checkout for operations on gs:// and
s3:// paths. It routes each declared bucket to the backend and endpoint in
config/*.yaml.
Read docs/references/fsutil.md for commands, behavior, and safety constraints.
Let the task determine which command and verification are appropriate.
Use this interface before provider-specific CLIs, hand-built S3 endpoints,
custom transfer scripts, or Iris jobs. Do not launch Iris solely to access
object storage. For Python code that needs the same bucket routing, use
rigging.filesystem.buckets.filesystem_for(url).
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Compare Zephyr changes using the same prepared dataset and matching execution settings. Analyze Finelog metrics for each processing stage to distinguish CPU efficiency, elapsed time, and peak memory changes, then report improvements, regressions, or tradeoffs.
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Register Hugging Face datasets for Marin by inspecting their structure without downloading the full dataset. Identify text fields, configurations, and data splits, then add a dataset module and validate the mapping with a sample.
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Turn a specified GCP access request into a reviewable Pulumi change and pull request. Select the narrowest suitable role and encrypt personal email identities, covering resource permissions and Cloud Run viewer access through IAP, an access gateway.
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Add or update Pallas/Mosaic kernels, specialized computation routines for JAX. The skill covers reference implementations, output and gradient checks, public wrappers, fallback behavior, performance benchmarks, and hardware-specific tuning when requested.
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Preserve old experiment scripts with a dated Git tag, a named snapshot of the code, and add comments linking related GitHub issues to the archived files and cleanup pull request.
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Launch and continuously monitor a specified Zephyr pipeline on Iris when asked. It checks coordinator and worker health, tracks stage progress, identifies stalled or repeated failures, and requires explicit approval before stopping or restarting jobs.
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