Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/duonginspace/claude-code-databricks-ml/run-training-on-databricksnpx skills add duonginspace/claude-code-databricks-ml --skill run-training-on-databricksgit clone --depth 1 https://github.com/duonginspace/claude-code-databricks-mlWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/duonginspace/claude-code-databricks-ml/run-training-on-databricks)<a href="https://agentmods.dev/skills/duonginspace/claude-code-databricks-ml/run-training-on-databricks"><img src="https://agentmods.dev/badge/skills/duonginspace/claude-code-databricks-ml/run-training-on-databricks.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00038 | $0.00381 |
| Opus 5 | $0.00019 | $0.00191 |
| Sonnet 5 | $0.00008 | $0.00076 |
| Haiku 4.5 | $0.00004 | $0.00038 |
Grade A, and why
run-training-on-databricks scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Run Training on Databricks
- The submit script builds a wheel of the project, uploads it + the training script to DBFS, and submits a Spark Python task:
uv run python scripts/submit_to_databricks.py scripts/train.py <args>The training script pip-installs the wheel at startup (DBR 15+ does not support DBFS library installs).- Add
--job-clusterto use an ephemeral job cluster (lower DBU rate, ~5-10min startup) instead of the existing cluster - Default uses the existing cluster from
DATABRICKS_CLUSTER_ID
- Add
- Wait for the run to complete (the script handles polling and log capture)
- If the run failed, read
mlflow_results/job_logs.txtfor the full output and error trace. Common issues:- Stale pydantic/typing_extensions from the Databricks runtime (the bootstrap should handle this, but check logs)
- Missing HF_TOKEN for gated models (TabPFN)
- MLflow experiment name must be a
/Users/...workspace path, not a bare name
- If the run succeeded, pull results:
uv run python scripts/pull_results_on_databricks.py - Read
mlflow_results/latest_run.jsonfor metrics - Summarize key metrics: loss, accuracy, training time
- Compare with previous runs in
mlflow_results/all_runs.csv - Suggest concrete next steps (hyperparameter changes, architecture modifications)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 24 lines · 38 tokens per session scan A 9caa95c5d3bc
run-training-on-databricks is a skill published in the GitHub repository duonginspace/claude-code-databricks-ml (5 stars, last pushed 5mo ago), licensed MIT. It adds 38 tokens to every session and 381 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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