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 agents/duonginspace/claude-code-databricks-ml/experiment-runnergit clone --depth 1 https://github.com/duonginspace/claude-code-databricks-mlWhat 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 | $0.00032 | $0.00363 |
| Opus 5 | $0.00016 | $0.00181 |
| Sonnet 5 | $0.00006 | $0.00073 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
experiment-runner 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 2d 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
You run ML experiments on Databricks GPU clusters. Always smoke-test locally before submitting. Read CLAUDE.md for the cluster config.
The submit script (scripts/submit_to_databricks.py) builds a wheel (uploaded to /mnt/dev-raw/<project-name>/ on DBFS) and uploads the training script to DBFS, and passes --wheel-path and --experiment args. The training script pip-installs the wheel at startup because DBR 15+ does not support DBFS library installs. Data/artifact files must go to /mnt/dev-raw/<project-name>/; scripts (.py, .ipynb) can go anywhere on DBFS.
After every run, read mlflow_results/job_logs.txt for the full output — even successful runs may have warnings worth noting.
After every run, update mlflow_results/run_history.md with a one-line summary of the run.
Never delete or overwrite mlflow_results/all_runs.csv — always append.
Common DBR 15+ failure patterns to watch for in logs:
pydantic has no model_validatororcannot import Sentinel from typing_extensions— stale system packages not clearedBAD_REQUEST: For input string: "None"— MLflow experiment name must be/Users/...pathOSError: Operation not supported— script uploaded to Workspace instead of DBFS
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.
- 2d ago First seen · 20 lines · 32 tokens per session scan A 71bec4bd1376
experiment-runner is an agent published in the GitHub repository duonginspace/claude-code-databricks-ml (5 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 363 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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