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 skills add duonginspace/claude-code-databricks-ml --skill check-results-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/check-results-on-databricks)<a href="https://agentmods.dev/skills/duonginspace/claude-code-databricks-ml/check-results-on-databricks"><img src="https://agentmods.dev/badge/skills/duonginspace/claude-code-databricks-ml/check-results-on-databricks/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/skills/duonginspace/claude-code-databricks-ml/check-results-on-databricks"><img src="https://agentmods.dev/badge/skills/duonginspace/claude-code-databricks-ml/check-results-on-databricks.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 | $0.00026 | $0.00152 |
| Opus 5 | $0.00013 | $0.00076 |
| Sonnet 5 | $0.00005 | $0.00030 |
| Haiku 4.5 | $0.00003 | $0.00015 |
Grade A, and why
check-results-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 8d 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
Check MLflow Results
- Run
uv run python scripts/pull_results_on_databricks.pyto fetch latest results - Read
mlflow_results/latest_run.jsonfor the most recent run - Read
mlflow_results/all_runs.csvfor experiment history - If the run failed or metrics look wrong, also read
mlflow_results/job_logs.txtfor the full Databricks output - Create a comparison table of the top runs by primary metric
- Identify trends across runs and suggest improvements
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.
- 8d ago First seen · 15 lines · 26 tokens per session scan A 81ef1032bab4
check-results-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 26 tokens to every session and 152 once invoked, about $0.0001 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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