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 scala-data-engineering-on-jvm-runtimesgit 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/scala-data-engineering-on-jvm-runtimes)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/scala-data-engineering-on-jvm-runtimes"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/scala-data-engineering-on-jvm-runtimes/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/scala-data-engineering-on-jvm-runtimes"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/scala-data-engineering-on-jvm-runtimes.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 | $0.00053 | $0.00680 |
| Opus 5 | $0.00026 | $0.00340 |
| Sonnet 5 | $0.00011 | $0.00136 |
| Haiku 4.5 | $0.00005 | $0.00068 |
Grade A, and why
scala-data-engineering-on-jvm-runtimes 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 8d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scala Data Engineering On JVM Runtimes
Overview
Use this skill when Scala is the implementation language for distributed or streaming data systems. It helps agents manage build compatibility, packaging, JVM dependency issues, typed data models, serialization behavior, and runtime assumptions that often cause production failures long after code compiles.
When to Use
- building or modifying
ScalaSparkjobs - implementing
Flink,Kafka Streams, or JVM-native data processors inScala - managing
sbtbuilds, shaded JARs, or runtime compatibility - choosing typed datasets, encoders, or JVM serialization strategies
- debugging classpath, version, or packaging problems in distributed runtimes
Do not assume compile success means distributed runtime safety.
Workflow
-
Define the runtime and compatibility surface. Include:
- engine and version
Scalaversion- JVM level
- cluster or container runtime
- connector and library compatibility
-
Shape the data model intentionally. Choose:
- typed case classes or schemas
- serialization strategy
- encoder behavior
- where UDFs are truly needed
- how nulls and optional fields are represented
-
Package for the real deployment target. Decide:
sbtor other build surface- fat-jar or shaded-jar approach
- dependency conflict handling
- resource and config loading behavior
- how the job is launched and parameterized
-
Design for distributed execution, not driver-local convenience. Check:
- partition behavior
- skew
- state growth
- checkpoint or savepoint needs
- accidental driver-side collection or closure capture
-
Validate with runtime realism. Require:
- representative local or test-cluster execution
- packaging verification
- schema and compatibility checks
- observability and failure handling expectations
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "It compiles, so the job is fine." | Distributed classpath, serialization, and dependency issues often appear only at runtime. |
| "We can fix the JAR if deployment fails." | Packaging problems discovered at deploy time slow delivery and often hide deeper compatibility issues. |
| "A quick UDF is simpler." | Overusing UDFs can hide schema, optimizer, and performance problems in JVM data engines. |
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.
- 8d ago First seen · 85 lines · 53 tokens per session scan A aee03c801747
scala-data-engineering-on-jvm-runtimes is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 53 tokens to every session and 680 once invoked, about $0.0003 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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