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 Galius5136/databricks-spark-3.5-cert-prep --skill spark-3.5-updatesgit clone --depth 1 https://github.com/Galius5136/databricks-spark-3.5-cert-prepWrote 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/galius5136/databricks-spark-3.5-cert-prep/spark-3.5-updates)<a href="https://agentmods.dev/skills/galius5136/databricks-spark-3.5-cert-prep/spark-3.5-updates"><img src="https://agentmods.dev/badge/skills/galius5136/databricks-spark-3.5-cert-prep/spark-3.5-updates/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/galius5136/databricks-spark-3.5-cert-prep/spark-3.5-updates"><img src="https://agentmods.dev/badge/skills/galius5136/databricks-spark-3.5-cert-prep/spark-3.5-updates.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 | $0.00160 | $0.02915 |
| Opus 5.5 | $0.00064 | $0.01166 |
| Sonnet 5.5 | $0.00032 | $0.00583 |
| Haiku 4.5 | $0.00016 | $0.00292 |
Grade A, and why
spark-3.5-updates 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.
How it starts
The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark 3.1 → 3.5 Updates — Exam-Prep Knowledge Base
Anchor: Spark 3.5 | Chapters: 3 | Generated: 2026-05-24
Scope: only what changed BETWEEN the Damji book (Spark 3.0 baseline) AND Spark 3.5. Anything Spark 4.x is flagged ⚠️.
Topics covered:
- A: Pandas UDF type hints + Pandas Function APIs + Arrow Python UDF — Sec 7 obj 2
- B: New built-in functions added in Spark 3.3-3.5 — Sec 3 supplement
- C: AQE evolution vs Spark 3.0 baseline — Sec 4 supplement
Cross-references (don't expand here):
- Pandas API on Spark (
pyspark.pandas) → skillpandas-on-spark- Spark Connect → skill
spark-connect- AQE 3.0 baseline + Spark UI →
apache-spark/chapters/ch07-tuning.md- Spark 3.0 epilogue / Catalyst →
apache-spark/chapters/ch12-epilogue-spark-3.md
How to Use This Skill
- Without arguments — load the Core Frameworks below.
- By topic letter — A (Pandas UDF), B (functions), C (AQE).
- By chapter —
ch01,ch02,ch03.
Core Frameworks & Mental Models
Topic A — Pandas UDF (Sec 7 obj 2)
Pandas UDF = vectorized Python UDF using Arrow as transport between JVM and Python. Spark 3.0+ uses Python type hints; legacy PandasUDFType (book Damji ch.5) is deprecated.
The 4 type-hint shapes
| # | Type hint | Use |
|---|---|---|
| 1 | pd.Series, ... -> pd.Series |
Series-to-Series scalar |
| 2 | Iterator[pd.Series] -> Iterator[pd.Series] |
Iterator scalar (per-worker state) |
| 3 | Iterator[Tuple[pd.Series, ...]] -> Iterator[pd.Series] |
Iterator multi-Series |
| 4 | pd.Series, ... -> Any |
Grouped aggregate / window |
Pandas Function APIs (whole DataFrame, not column)
df.mapInPandas(func, schema)— iterator pd.DataFrame → iterator pd.DataFramedf.groupby(k).applyInPandas(func, schema)— one pd.DataFrame per groupdf.groupby(k).cogroup(other.groupby(k)).applyInPandas(func, schema)— two pd.DataFrames per cogroup
Arrow Python UDF (Spark 3.5+)
@udf(returnType="int", useArrow=True) # row-by-row, Arrow transport
def f(s): return len(s)
useArrow=True/False/None.None→ fall back tospark.sql.execution.pythonUDF.arrow.enabled- Better type coercion than default pickled UDF; still row-by-row (not vectorized)
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 190 lines · 160 tokens per session scan A 97b5a7290cde
spark-3.5-updates is a skill published in the GitHub repository Galius5136/databricks-spark-3.5-cert-prep (13 stars, last pushed 5mo ago), licensed MIT. It adds 160 tokens to every session and 2,915 once invoked, about $0.0006 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 2026-08-30.
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