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-views-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-views-perf-remediate)<a href="https://agentmods.dev/skills/patrickgallucci/fabric-skills/fabric-lakehouse-views-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-lakehouse-views-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-views-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-lakehouse-views-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 | $0.00137 | $0.02465 |
| Opus 5 | $0.00068 | $0.01233 |
| Sonnet 5 | $0.00027 | $0.00493 |
| Haiku 4.5 | $0.00014 | $0.00247 |
Grade A, and why
fabric-lakehouse-views-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 — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fabric Materialized Lake Views Performance remediate
Diagnose and resolve performance issues with materialized lake views (MLVs) in Microsoft Fabric lakehouses. This skill covers refresh optimization, Spark job diagnostics, data quality constraint tuning, and lineage execution remediate.
When to Use This Skill
- MLV refresh runs are taking longer than expected
- Incremental refresh is falling back to full refresh unexpectedly
- MLV lineage execution shows Failed or Skipped nodes
- Spark jobs for MLV refresh are failing with errors
- "Delta table not found" errors during MLV creation or refresh
- Data quality constraints causing unexpected pipeline failures
- Need to enable or verify optimal refresh configuration
- Custom Spark environment tuning for MLV workloads
- Monitoring and interpreting MLV run history
Prerequisites
- Microsoft Fabric workspace with Lakehouse items
- Schema-enabled lakehouse (recommended for MLV support)
- Fabric notebook for executing Spark SQL commands
- Workspace Admin or Contributor role for scheduling and monitoring
- Access to Monitor Hub for viewing MLV run details
Quick Diagnostics Checklist
Run through these checks in order when remediate MLV performance:
| Step | Check | Action |
|---|---|---|
| 1 | Identify refresh mode | Verify optimal refresh toggle is enabled in lineage view |
| 2 | Check CDF status | Confirm delta.enableChangeDataFeed=true on ALL source tables |
| 3 | Review query patterns | Ensure only supported SQL constructs are used (see supported expressions) |
| 4 | Inspect run history | Open lineage view dropdown to see last 25 runs and their states |
| 5 | Check node failures | Click failed nodes in lineage to view error messages |
| 6 | Review Spark logs | Follow Detailed Logs link to Monitor Hub for Spark error logs |
| 7 | Validate data quality | Check if FAIL constraints are causing "delta table not found" errors |
| 8 | Assess source data | Determine if source has updates/deletes (forces full refresh) |
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.
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 · 204 lines · 137 tokens per session scan A a92a1f12f75d
fabric-lakehouse-views-perf-remediate is a skill published in the GitHub repository PatrickGallucci/fabric-skills (16 stars, last pushed 3mo ago), licensed MIT. It adds 137 tokens to every session and 2,465 once invoked, about $0.0007 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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