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 G1Joshi/Agent-Skills --skill sparkgit clone --depth 1 https://github.com/G1Joshi/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/g1joshi/agent-skills/spark)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/spark"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/spark.svg" alt="Measured on agentmods" 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 | $0.00013 | $0.00289 |
| Opus 5 | $0.00006 | $0.00144 |
| Sonnet 5 | $0.00003 | $0.00058 |
| Haiku 4.5 | $0.00001 | $0.00029 |
Grade A, and why
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 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.
What it actually says
Apache Spark
Spark is the king of Big Data. v4.0 (2024/2025) makes Spark Connect the default, allowing thin clients (like VS Code) to connect to massive clusters easily.
When to Use
- Data Engineering: ETL at Petabyte scale.
- Streaming: Structured Streaming for real-time analytics.
- Legacy ML:
spark.ml(though mostly replaced by XGBoost/Torch).
Core Concepts
Spark Connect
Decouples client (your laptop) from server (the cluster). Allows using Spark from Go/Rust/TypeScript.
Catalyst Optimizer
Optimizes your SQL/DataFrame queries before execution.
RDD
The low-level API. Almost never used directly in modern Spark.
Best Practices (2025)
Do:
- Use PySpark: It is now a first-class citizen with Python UDF profiling.
- Use Delta Lake / Iceberg: Spark works best with modern table formats.
- Use
pandas_udf: For vectorized Python UDFs.
Don't:
- Don't use
rdd.map: It is slow (Python serialization). Use DataFrames.
References
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 · 45 lines · 13 tokens per session scan A c9bab28e02e8
spark is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 13 tokens to every session and 289 once invoked, about $0.0001 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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