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-udf-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-udf-perf-remediate)<a href="https://agentmods.dev/skills/patrickgallucci/fabric-skills/fabric-udf-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-udf-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-udf-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-udf-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 | $0.00081 | $0.02406 |
| Opus 5.5 | $0.00032 | $0.00962 |
| Sonnet 5.5 | $0.00016 | $0.00481 |
| Haiku 4.5 | $0.00008 | $0.00241 |
Grade A, and why
fabric-udf-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 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.
How it starts
The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Microsoft Fabric User Data Functions Performance remediate
Systematic guide for diagnosing and resolving performance issues with Fabric User Data Functions (UDFs). Covers cold starts, execution timeouts, capacity consumption, connection bottlenecks, and Python code optimization.
When to Use This Skill
- Function invocations are slow or intermittently timing out
- Capacity metrics show unexpected CU consumption from UDF operations
- Functions fail with timeout, response size, or connection errors
- Cold start latency is impacting downstream consumers (Pipelines, Notebooks, Power BI)
- Historical logs show increasing duration trends
- Need to optimize UDF code for better performance within service limits
Prerequisites
- Access to the Fabric portal with permissions on the User Data Functions item
- Microsoft Fabric Capacity Metrics app installed (for CU analysis)
- Python 3.11+ locally (for code profiling outside Fabric)
- PowerShell 7+ (for running diagnostic scripts)
Service Limits Quick Reference
| Limit | Value | Impact |
|---|---|---|
| Request payload | 4 MB | All input parameters combined |
| Execution timeout | 240 seconds | Maximum function runtime |
| Response size | 30 MB | Maximum return value size |
| Log retention | 30 days | Historical invocation log window |
| Private library max | 28.6 MB | Per .whl file upload |
| Test session timeout | 15 minutes | Idle timeout in Develop mode |
| Daily log ingestion | 250 MB | Logs may be sampled beyond this |
| Python version (Run) | 3.11 | Published functions runtime |
| Python version (Test) | 3.12 | Develop mode test runtime |
Step-by-Step remediate Workflow
Step 1: Identify the Symptom
Determine which category your issue falls into:
| Symptom | Likely Root Cause | Go To |
|---|---|---|
| First invocation slow, subsequent fast | Cold start / initialization | Step 2 |
| All invocations consistently slow | Code inefficiency or data volume | Step 3 |
| Intermittent timeouts | Connection issues or capacity throttling | Step 4 |
| Response too large error | Unbounded query results | Step 5 |
| High CU consumption in Metrics app | Excessive execution frequency or duration | Step 6 |
| Function fails with import errors | Library loading overhead | Step 7 |
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.
- 1mo ago First seen · 258 lines · 81 tokens per session scan A 9dea43e64830
fabric-udf-perf-remediate is a skill published in the GitHub repository PatrickGallucci/fabric-skills (17 stars, last pushed 4mo ago), licensed MIT. It adds 81 tokens to every session and 2,406 once invoked, about $0.0003 per session on Opus 5.5. 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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