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SKILL verified MIT Self-run

Train Local

skill-duonginspace-claude-code-databricks-ml-train-local · by duonginspace

Run a quick training experiment locally (CPU or MPS). Use when the user wants to test a model change, debug training code, run a smoke test, or validate a new architecture before submitting to Databricks. Fast iteration, small data, short epochs.

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Install

$ agentstack add skill-duonginspace-claude-code-databricks-ml-train-local

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

✓ Security review passed
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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

Local training task

$ARGUMENTS

Steps

  1. Read CLAUDE.md for the project structure and current experiment config.
  2. If not specified, default to: 2 epochs, 10% of training data, CPU/MPS device, batch size 16.
  3. Add --dry-run or --fast-dev-run flag if the training script supports it.
  4. Run: uv run python scripts/train.py 2>&1 | tee mlflow_results/local_run.log
  5. Watch for: import errors, shape mismatches, CUDA/MPS device errors, NaN losses, OOM errors.
  6. If the run succeeds, report: final train loss, validation metric, time per epoch.
  7. If it fails, diagnose the error and propose a fix before suggesting a Databricks run.

Output

Tell the user whether the code is ready to submit to Databricks, or what to fix first. Always suggest using /run-on-databricks after a successful local smoke test.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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