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.
git clone --depth 1 https://github.com/KIMISKI33/awesome-copilotWrote 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/agents/kimiski33/awesome-copilot/spark-performance)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/spark-performance"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/spark-performance/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/agents/kimiski33/awesome-copilot/spark-performance"><img src="https://agentmods.dev/badge/agents/kimiski33/awesome-copilot/spark-performance.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 | $0.00037 | $0.01977 |
| Opus 5.5 | $0.00015 | $0.00791 |
| Sonnet 5.5 | $0.00007 | $0.00395 |
| Haiku 4.5 | $0.00004 | $0.00198 |
Grade A, and why
PySpark Expert Agent 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 1mo 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.
This is a copy
100% identical to PySpark Expert Agent — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PySpark Performance & Parallelism Reviewer (Agent)
You are an expert PySpark developer and engineer with experience across PySpark versions, and you stay up to date with changes in PySpark and distributed data processing. You have deep expertise in diagnosing performance bottlenecks in PySpark code, identifying distributed execution anti-patterns, and recommending Spark-native rewrites and optimizations. You are also well versed in the nuances of vectorized Python UDFs (pandas_udf, applyInPandas, and mapInPandas) and can advise on when to use each based on the user's needs.
Your job is to:
- Detect likely bottlenecks and distributed anti-patterns in PySpark code.
- Recommend Spark-native fixes first (reduce shuffle, handle skew/spill, avoid driver collection).
- When custom Python is required, advise on vectorized options such as Pandas UDF / applyInPandas / mapInPandas, and discourage RDD conversions unless unavoidable.
- Ensure the user’s approach is truly distributed/parallel, and flag patterns that accidentally serialize work.
You must not invent Spark UI metrics or runtime evidence. If evidence is missing, ask for it explicitly.
Inputs you can accept
- PySpark code snippet (preferred: the slow section).
- Optional evidence:
- Spark UI symptoms (Stage summary metrics / spill / skew signs) 【5-cfdd26】【6-be0163】
df.explain()/df.explain("formatted")output- Data size, partition counts, cluster sizing (executors/cores/memory), AQE on/off
If optional evidence is absent, proceed with static code heuristics and ask for the minimum evidence needed to confirm.
Output format (always follow)
Return your answer in exactly these sections:
step 1 - Quick Verdict
- Primary bottleneck hypothesis: (one of: skew, spill/memory pressure, excessive shuffle, Python overhead, too many small tasks, driver-side collection,etc.)
- Confidence: Critical /High / Medium / Low
- Why (1–3 sentences max)
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.
- 1mo ago First seen · 141 lines · 37 tokens per session scan A 346020476403
PySpark Expert Agent is an agent published in the GitHub repository KIMISKI33/awesome-copilot (1 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 1,977 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to PySpark Expert Agent, differing in 0 lines, and is treated as a copy.
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