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-lakehouse-perf-remediategit 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-lakehouse-perf-remediate)<a href="https://agentmods.dev/skills/patrickgallucci/fabric-skills/fabric-lakehouse-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-lakehouse-perf-remediate/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-lakehouse-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-lakehouse-perf-remediate.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 | $0.00117 | $0.02331 |
| Opus 5 | $0.00059 | $0.01166 |
| Sonnet 5 | $0.00023 | $0.00466 |
| Haiku 4.5 | $0.00012 | $0.00233 |
Grade A, and why
fabric-lakehouse-perf-remediate 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 — 238 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fabric Lakehouse Performance remediate
Systematic toolkit for diagnosing and resolving performance issues in Microsoft Fabric Lakehouse environments. Covers Delta table health, Spark compute tuning, query optimization, and automated maintenance workflows.
When to Use This Skill
- Lakehouse queries are running slowly or timing out
- Delta tables have accumulated many small files (small file problem)
- Spark notebooks or jobs are underperforming
- Direct Lake semantic models have cold-start or transcoding delays
- SQL analytics endpoint queries are slow
- Table maintenance (OPTIMIZE, VACUUM) needs to be scheduled or automated
- V-Order, Z-Order, or resource profile configuration is needed
- Capacity throttling or concurrency issues are suspected
- Streaming ingestion is creating fragmented Delta tables
Prerequisites
- Microsoft Fabric workspace with Lakehouse items
- Contributor or higher workspace role
- Fabric capacity (F2 or above) or Trial capacity
- For REST API automation: Microsoft Entra token for Fabric service
- For Spark commands: Access to Fabric notebooks or Spark Job Definitions
Quick Diagnosis Checklist
When a user reports Lakehouse performance issues, work through these areas in order:
- Identify the symptom — Slow reads, slow writes, capacity throttling, or query timeouts
- Check Delta table health — File count, file sizes, V-Order status, partition layout
- Review Spark configuration — Resource profile, autotune, shuffle partitions
- Inspect capacity utilization — Concurrency limits, burst capacity, throttling
- Evaluate maintenance history — When was OPTIMIZE/VACUUM last run?
- Assess data patterns — Streaming vs batch, read-heavy vs write-heavy
Symptom-to-Action Map
| Symptom | Root Cause | Action |
|---|---|---|
| Slow reads across all engines | Small files, no V-Order | Run OPTIMIZE VORDER, switch to readHeavy profile |
| Slow Spark queries only | Wrong shuffle partitions | Enable autotune or tune manually |
| Slow Power BI Direct Lake | Too many Parquet files/row groups | Run OPTIMIZE, check guardrail limits |
| Slow SQL analytics endpoint | Files under 400 MB, too many small files | OPTIMIZE with maxRecordsPerFile=2M |
| Write performance degraded | V-Order enabled on write-heavy workload | Switch to writeHeavy resource profile |
| Capacity throttled | Too many concurrent Spark jobs | Review concurrency limits, enable optimistic admission |
| Storage growing unexpectedly | VACUUM not running | Schedule VACUUM with 7-day retention |
| Streaming creates tiny files | No batching or trigger interval | Add processingTime trigger, run periodic OPTIMIZE |
What ships with it
9 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/concurrency-capacity.md 5.3 KB
- references/delta-table-health.md 6.0 KB
- references/resource-profiles.md 4.7 KB
- references/rest-api-maintenance.md 4.7 KB
- scripts/Get-DeltaTableHealth.ps1 5.8 KB runs code
- scripts/Invoke-FabricTableMaintenance.ps1 8.8 KB runs code
- templates/maintenance-notebook.py 4.2 KB runs code
- templates/perf-diagnostic-notebook.py 8.0 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 · 238 lines · 117 tokens per session scan A 2e52dad09c2c
fabric-lakehouse-perf-remediate is a skill published in the GitHub repository PatrickGallucci/fabric-skills (16 stars, last pushed 3mo ago), licensed MIT. It adds 117 tokens to every session and 2,331 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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