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 apache-sparkgit 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/apache-spark)<a href="https://agentmods.dev/skills/galius5136/databricks-spark-3.5-cert-prep/apache-spark"><img src="https://agentmods.dev/badge/skills/galius5136/databricks-spark-3.5-cert-prep/apache-spark/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/apache-spark"><img src="https://agentmods.dev/badge/skills/galius5136/databricks-spark-3.5-cert-prep/apache-spark.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 | $0.00077 | $0.03433 |
| Opus 5.5 | $0.00031 | $0.01373 |
| Sonnet 5.5 | $0.00015 | $0.00687 |
| Haiku 4.5 | $0.00008 | $0.00343 |
Grade A, and why
apache-spark 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Spark, 2nd Edition — Knowledge Base
Authors: Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee | Chapters: 12 | Generated: 2026-05-24
This skill is tuned for Databricks Certified Associate Developer for Apache Spark exam prep — PySpark first, with emphasis on architecture, DataFrame API, Spark SQL, tuning, and Structured Streaming.
How to Use This Skill
- Without arguments — load the Core Frameworks below as a Spark mental model.
- By topic — ask about
shuffle partitions,broadcast join,watermark,output modes, etc. → I find and read the relevant chapter. - By chapter number — ask for
ch07to load that specific chapter file. - Browse — ask "what chapters do you have?" to see the full index.
When you ask about a topic that's only briefly mentioned in Core Frameworks, I'll read the relevant chapter file before answering.
Core Frameworks & Mental Models
Spark in one paragraph
A unified engine for large-scale data processing. The driver orchestrates executors on a cluster; computation is expressed as a DAG of transformations (lazy) and actions (eager). Structured APIs (DataFrame, Dataset) are optimized by the Catalyst optimizer and compiled by Tungsten into compact JVM bytecode. The same engine powers batch (Spark SQL), streaming (Structured Streaming), ML (MLlib), and graph (GraphX) workloads.
Execution hierarchy (memorize for the exam)
Application → Job (per action) → Stage (split at every shuffle/exchange) → Task (one per partition, runs on one executor core).
Lazy evaluation + lineage
Transformations record a lineage DAG; actions trigger execution. Lineage = fault tolerance — Spark can rebuild lost partitions by replaying transformations on the source data.
Narrow vs Wide transformations
Narrow (filter, select, map, union): 1 input partition → 1 output partition, no shuffle. Wide (groupBy, orderBy, join, distinct, repartition): cross-partition data exchange → stage boundary.
What ships with it
15 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.
- chapters/ch01-introduction.md 4.6 KB
- chapters/ch02-getting-started.md 5.3 KB
- chapters/ch03-structured-apis.md 5.7 KB
- chapters/ch04-sql-builtin-sources.md 6.6 KB
- chapters/ch05-sql-external-sources.md 10 KB
- chapters/ch06-datasets.md 4.8 KB
- chapters/ch07-tuning.md 14 KB
- chapters/ch08-structured-streaming.md 8.6 KB
- chapters/ch09-reliable-data-lakes.md 4.6 KB
- chapters/ch10-mllib.md 5.2 KB
- chapters/ch11-ml-pipelines-deployment.md 4.9 KB
- chapters/ch12-epilogue-spark-3.md 5.7 KB
- cheatsheet.md 7.4 KB
- glossary.md 11 KB
- patterns.md 8.0 KB
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 · 255 lines · 77 tokens per session scan A 64b53306d5d7
apache-spark 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 77 tokens to every session and 3,433 once invoked, about $0.0003 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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