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 PatrickGallucci/fabric-skills --skill fabric-delta-spark-perfgit clone --depth 1 https://github.com/PatrickGallucci/fabric-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/patrickgallucci/fabric-skills/fabric-delta-spark-perf)<a href="https://agentmods.dev/skills/patrickgallucci/fabric-skills/fabric-delta-spark-perf"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-delta-spark-perf/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/patrickgallucci/fabric-skills/fabric-delta-spark-perf"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-delta-spark-perf.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 | $0.00118 | $0.01917 |
| Opus 5 | $0.00059 | $0.00958 |
| Sonnet 5 | $0.00024 | $0.00383 |
| Haiku 4.5 | $0.00012 | $0.00192 |
Grade A, and why
fabric-delta-spark-perf 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 12d 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsoft Fabric Delta Lake Spark Performance remediate
Systematic workflows for diagnosing and resolving Apache Spark and Delta Lake performance issues in Microsoft Fabric Lakehouse environments.
When to Use This Skill
Activate when the user mentions any of the following:
- Spark job is slow, taking too long, or timing out
- Small file problem, too many small files, file fragmentation
- Data skew, straggler tasks, unbalanced partitions
- Out of memory (OOM) errors on driver or executor
- Shuffle spill, excessive shuffle read/write
- OPTIMIZE, VACUUM, bin-compaction, or table maintenance
- V-Order, Z-Order, or Parquet optimization
- Resource profiles: writeHeavy, readHeavyForSpark, readHeavyForPBI
- Autotune, Adaptive Query Execution (AQE), broadcast join thresholds
- Native Execution Engine configuration
- Streaming performance, microbatch tuning, checkpoint issues
- Spark pool sizing, autoscale, dynamic executor allocation
- Direct Lake performance tied to Delta table structure
- Capacity throttling, TooManyRequestsForCapacity errors
Prerequisites
- Microsoft Fabric workspace with Data Engineering or Data Science experience
- Apache Spark notebooks or Spark Job Definitions
- Lakehouse with Delta tables
- Appropriate Fabric capacity SKU (F2 through F2048)
Quick Diagnostic Workflow
When a user reports slow Spark performance, follow this triage sequence:
Step 1: Identify the Symptom Category
| Symptom | Likely Root Cause | Jump To |
|---|---|---|
| Job runs much longer than expected | Data skew or small files | Step 2 |
| OOM error on driver | collect(), toPandas(), or large broadcast |
diagnostic-checklist.md |
| OOM error on executor | Wide joins, large shuffles, insufficient memory | diagnostic-checklist.md |
| Many tasks, most finish fast, few stragglers | Data skew | diagnostic-checklist.md |
| High shuffle read/write in Spark UI | Missing broadcast join or too many partitions | diagnostic-checklist.md |
| Query reads thousands of small files | Small file problem, needs OPTIMIZE | Step 3 |
| Capacity throttled (HTTP 430) | Too many concurrent jobs for SKU | spark-configurations.md |
| Streaming lag increasing | Microbatch interval or partition mismatch | diagnostic-checklist.md |
What ships with it
7 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.
- LICENSE.txt 11 KB
- references/diagnostic-checklist.md 12 KB
- references/spark-configurations.md 10 KB
- references/table-maintenance-guide.md 11 KB
- scripts/diagnose-delta-performance.py 12 KB runs code
- scripts/table-maintenance.py 9.2 KB runs code
- templates/notebook-performance-template.py 5.9 KB runs code
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.
- 12d ago First seen · 157 lines · 118 tokens per session scan A f9437ae166f7
fabric-delta-spark-perf is a skill published in the GitHub repository PatrickGallucci/fabric-skills (16 stars, last pushed 3mo ago), licensed MIT. It adds 118 tokens to every session and 1,917 once invoked, about $0.0006 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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