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 vaquarkhan/data-engineering-agent-skills --skill spark-and-distributed-processinggit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skillsWrote 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/vaquarkhan/data-engineering-agent-skills/spark-and-distributed-processing)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/spark-and-distributed-processing"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/spark-and-distributed-processing/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/vaquarkhan/data-engineering-agent-skills/spark-and-distributed-processing"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/spark-and-distributed-processing.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 | $0.00038 | $0.00652 |
| Opus 5 | $0.00019 | $0.00326 |
| Sonnet 5 | $0.00008 | $0.00130 |
| Haiku 4.5 | $0.00004 | $0.00065 |
Grade A, and why
spark-and-distributed-processing 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 9d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark And Distributed Processing
Overview
Use this skill for batch workloads that exceed single-node processing and need distributed execution discipline. It helps agents reason about Spark, managed Spark services such as Glue and EMR, partitioning, joins, storage layout, and failure-safe processing.
When to Use
- building or changing
Sparkjobs - choosing between
Spark,Glue,EMR, or smaller engines - debugging expensive joins, skew, shuffle, or memory issues
- designing batch pipelines over large files or partitioned tables
- implementing transformations against lakehouse tables such as
Iceberg,Delta, orHudi
Do not use this for lightweight local transforms that fit comfortably in a single process.
Workflow
-
Confirm distributed execution is actually required. Check:
- input volume
- latency expectations
- transformation complexity
- file sizes and partition counts
- cost compared with simpler engines
-
Choose the runtime intentionally.
Spark: direct control and broad ecosystem supportGlue: managed AWS-native Spark executionEMR: broader cluster control for Spark and related engines- short-lived or serverless Spark (
Lambda, serverlessGlue, hard timeout ceilings): loadspark-serverless-reliability-and-state-management
-
Design the physical plan, not just the logical one. Account for:
- partitioning and file layout
- shuffle-heavy joins
- skewed keys
- checkpoint or intermediate persistence
- write mode and idempotency behavior
-
Keep data contracts visible at the edges. Validate input assumptions before expensive execution and verify output contracts before publish.
-
Make backfills and reruns safe. Historical batch recomputation should define overwrite, merge, or append semantics explicitly.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "Spark will handle optimization for us." | Engine optimizations help, but poor partitioning, skew, and write strategy still create failures or huge cost. |
| "We can just scale the cluster." | Scaling often masks bad physical design and can still fail on skew or bad shuffles. |
| "Managed Spark means we do not need runtime design." | Glue and EMR still require deliberate partitioning, retries, and storage strategy. |
What ships with it
2 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.
- 9d ago First seen · 74 lines · 38 tokens per session scan A 459223114e01
spark-and-distributed-processing is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 652 once invoked, about $0.0002 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 2026-09-03.
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