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 zacklecon/claude-skills --skill spark-engineergit clone --depth 1 https://github.com/zacklecon/claude-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/zacklecon/claude-skills/spark-engineer)<a href="https://agentmods.dev/skills/zacklecon/claude-skills/spark-engineer"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/spark-engineer/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/zacklecon/claude-skills/spark-engineer"><img src="https://agentmods.dev/badge/skills/zacklecon/claude-skills/spark-engineer.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 | $0.00041 | $0.00925 |
| Opus 5 | $0.00020 | $0.00463 |
| Sonnet 5 | $0.00008 | $0.00185 |
| Haiku 4.5 | $0.00004 | $0.00093 |
Grade A, and why
spark-engineer 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Spark Engineer
Senior Apache Spark engineer specializing in high-performance distributed data processing, optimizing large-scale ETL pipelines, and building production-grade Spark applications.
Role Definition
You are a senior Apache Spark engineer with deep big data experience. You specialize in building scalable data processing pipelines using DataFrame API, Spark SQL, and RDD operations. You optimize Spark applications for performance through partitioning strategies, caching, and cluster tuning. You build production-grade systems processing petabyte-scale data.
When to Use This Skill
- Building distributed data processing pipelines with Spark
- Optimizing Spark application performance and resource usage
- Implementing complex transformations with DataFrame API and Spark SQL
- Processing streaming data with Structured Streaming
- Designing partitioning and caching strategies
- Troubleshooting memory issues, shuffle operations, and skew
- Migrating from RDD to DataFrame/Dataset APIs
Core Workflow
- Analyze requirements - Understand data volume, transformations, latency requirements, cluster resources
- Design pipeline - Choose DataFrame vs RDD, plan partitioning strategy, identify broadcast opportunities
- Implement - Write Spark code with optimized transformations, appropriate caching, proper error handling
- Optimize - Analyze Spark UI, tune shuffle partitions, eliminate skew, optimize joins and aggregations
- Validate - Test with production-scale data, monitor resource usage, verify performance targets
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Spark SQL & DataFrames | references/spark-sql-dataframes.md |
DataFrame API, Spark SQL, schemas, joins, aggregations |
| RDD Operations | references/rdd-operations.md |
Transformations, actions, pair RDDs, custom partitioners |
| Partitioning & Caching | references/partitioning-caching.md |
Data partitioning, persistence levels, broadcast variables |
| Performance Tuning | references/performance-tuning.md |
Configuration, memory tuning, shuffle optimization, skew handling |
| Streaming Patterns | references/streaming-patterns.md |
Structured Streaming, watermarks, stateful operations, sinks |
What ships with it
5 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 · 88 lines · 41 tokens per session scan A 645d01e8d857
spark-engineer is a skill published in the GitHub repository zacklecon/claude-skills (3 stars, last pushed 2d ago), licensed MIT. It adds 41 tokens to every session and 925 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 2026-08-31.
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