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.
git 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/agents/duonginspace/claude-code-databricks-ml/experiment-runner)<a href="https://agentmods.dev/agents/duonginspace/claude-code-databricks-ml/experiment-runner"><img src="https://agentmods.dev/badge/agents/duonginspace/claude-code-databricks-ml/experiment-runner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/duonginspace/claude-code-databricks-ml/experiment-runner"><img src="https://agentmods.dev/badge/agents/duonginspace/claude-code-databricks-ml/experiment-runner.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00032 | $0.00363 |
| Opus 5.5 | $0.00013 | $0.00145 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 6mo ago), licensed MIT. It adds 32 tokens to every session and 363 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
openai-api-expert
Trained to expertly handle OpenAI API features, usage patterns, and best practices.
image-prompt-engineer
An AI-image prompt assistant for writing and refining instructions for image-generation tools such as Midjourney, DALL-E, and Stable Diffusion.