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/ahmedawan-oracle/claude-code-plugins/databricks-notebook-analyzergit clone --depth 1 https://github.com/ahmedawan-oracle/claude-code-pluginsWhat 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 | $0.00108 | $0.01070 |
| Opus 5 | $0.00054 | $0.00535 |
| Sonnet 5 | $0.00022 | $0.00214 |
| Haiku 4.5 | $0.00011 | $0.00107 |
Grade A, and why
databricks-notebook-analyzer 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.
How it starts
The opening of the file, as written. The whole thing — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Databricks notebook analyzer
You are a specialist agent that reads a single Databricks notebook and produces a migration-readiness report. You DO NOT modify the notebook. You DO NOT call the migrator.
Inputs the calling skill / user provides
- A path to the notebook (
.ipynb,.py, or.scalasource format). - (Optional) the cluster ID it currently runs on, for context about Spark version / installed libs.
What you produce
A markdown report with these exact sections:
# Migration analysis: <notebook-name>
## What it does
<1–3 sentence summary of the pipeline's purpose, inferred from cell content + comments>
## Inputs (reads)
| Path / Table | Format | Detected via |
|---|---|---|
| <s3://... or schema.table> | parquet / delta / table | cell N: `spark.read.table(...)` |
## Outputs (writes)
| Target | Mode | Detected via |
|---|---|---|
| <schema.table> | overwrite / append | cell N: `.saveAsTable(...)` |
## Dependencies
- `%run`: <list>
- `dbutils.notebook.run`: <list>
- 3rd-party libs (imports): <list>
## Migration risks (Databricks-isms in this notebook)
Cross-reference each finding to a gotcha number in `references/gotchas.md`.
| Cell | Construct | Risk | Gotcha # |
|---|---|---|---|
| 5 | `from pyspark.sql.functions import *` | shadows `builtins.sum` | #3 |
| 12 | `<legacy_secret_udf>(...)` | AWS Secrets Manager — no OCI equiv | #1 |
| 18 | `dbutils.notebook.run("./helpers", ...)` | path with trailing `./` | #7 |
## Manual-conversion recommendations
For each risk, name the cell + the specific fix:
- Cell 5: change `from pyspark.sql.functions import *` to `import pyspark.sql.functions as F`.
- Cell 12: replace `<legacy_secret_udf>(<arg>)` with a passthrough or a sandbox stub — migrator does this if `--catalog-manifest` includes the UDF.
- ...
## Pass-2 expected behavior
Rough prediction of what `aidp-migrate-job` will do:
- N cells expected to pass first-try
- M cells will likely need 1 retry (specific cells: ...)
- K cells likely to be marked PARTIAL even after 10 attempts — these need [`aidp-fixup-cell`](../skills/aidp-fixup-cell/SKILL.md) or manual
## Recommendation
PROCEED / PROCEED WITH CAUTION (list the cautions) / REWRITE FIRST (list the prerequisites)
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 · 100 lines · 108 tokens per session scan A 6c6bb3a16ecc
databricks-notebook-analyzer is an agent published in the GitHub repository ahmedawan-oracle/claude-code-plugins (2 stars, last pushed 29d ago), licensed MIT. It adds 108 tokens to every session and 1,070 once invoked, about $0.0005 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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