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 pandas-on-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/pandas-on-spark)<a href="https://agentmods.dev/skills/galius5136/databricks-spark-3.5-cert-prep/pandas-on-spark"><img src="https://agentmods.dev/badge/skills/galius5136/databricks-spark-3.5-cert-prep/pandas-on-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/pandas-on-spark"><img src="https://agentmods.dev/badge/skills/galius5136/databricks-spark-3.5-cert-prep/pandas-on-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 | $0.00119 | $0.02367 |
| Opus 5 | $0.00060 | $0.01184 |
| Sonnet 5 | $0.00024 | $0.00473 |
| Haiku 4.5 | $0.00012 | $0.00237 |
Grade A, and why
pandas-on-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 11d 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pandas API on Spark — Exam-Prep Knowledge Base
Source Spark version: 3.5.7 + 2021 Databricks Koalas-merger context | Chapters: 1 (single deep-dive) | Generated: 2026-05-24
Scope rule: All content is in scope for Sec 7 of the Databricks Certified Associate Developer for Apache Spark exam, anchored to Spark 3.5. Anything Spark 4.x is flagged ⚠️.
Out of scope for this skill (cross-links only, no expansion):
- Pandas UDF (
@pandas_udf) — same exam section but a different API; covered by a separate skill.- Spark Connect — see skill
spark-connect.
How to Use This Skill
- Without arguments — loads the Core Frameworks below.
- By topic — ask about
default index type,to_pandas,ops_on_diff_frames,checkpoint,Koalas, etc. - By chapter — only
ch01; the topic is single-chapter sized.
Core Frameworks & Mental Models
Pandas API on Spark in one paragraph
pyspark.pandas is a pandas-compatible DataFrame API that executes distributed on Spark. It started as the standalone Koalas project (Databricks) and was merged into PySpark with Spark 3.2 (Sept-Oct 2021, via the SPIP under Project Zen). Goal: let pandas users scale from single-machine to multi-TB cluster workloads with a single import change (import pandas as pd → import pyspark.pandas as pd), while keeping the entire downstream code identical.
Why use it (advantages — exam objective 1)
- Familiar pandas syntax at Spark scale — minimal code change to scale.
- No extra install since Spark 3.2 — ships with PySpark.
- Single-machine speedup via Catalyst optimizer + whole-stage codegen (Databricks 2021 benchmark: ~4× faster join on 130 GB CSV vs native pandas; survives chain operations where pandas OOMs).
- Linear scalability — same job runs on 60 GB single-machine or 15 TB on 256-node cluster (Databricks benchmark: ~10s std-dev compute in both cases).
- Unified analytics — same DataFrames feed
ps.sql(...), Spark Structured Streaming, and MLlib. - Lazy execution — Catalyst plans and optimizes; jobs trigger only when needed.
- All Spark features work — web UI, history server, AQE, dynamic allocation, deployment modes.
What ships with it
4 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.
- 11d ago First seen · 179 lines · 119 tokens per session scan A 02401f211147
pandas-on-spark is a skill published in the GitHub repository Galius5136/databricks-spark-3.5-cert-prep (12 stars, last pushed 3mo ago), licensed MIT. It adds 119 tokens to every session and 2,367 once invoked, about $0.0006 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 2026-08-30.
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