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-rti-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-rti-perf-remediate)<a href="https://agentmods.dev/skills/patrickgallucci/fabric-skills/fabric-rti-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-rti-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-rti-perf-remediate"><img src="https://agentmods.dev/badge/skills/patrickgallucci/fabric-skills/fabric-rti-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 | $0.00101 | $0.01237 |
| Opus 5 | $0.00051 | $0.00619 |
| Sonnet 5 | $0.00020 | $0.00247 |
| Haiku 4.5 | $0.00010 | $0.00124 |
Grade A, and why
fabric-rti-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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fabric Real-Time Intelligence Performance remediate
Systematic toolkit for diagnosing and resolving performance issues across the Microsoft Fabric Real-Time Intelligence stack: Eventhouse, KQL databases, Eventstream, ingestion pipelines, and capacity management.
When to Use This Skill
- Eventhouse queries running slowly or timing out
- Ingestion latency or failures into KQL databases
- Eventstream throughput bottlenecks or backlog growth
- Capacity throttling errors (HTTP 430, TooManyRequestsForCapacity)
- High CPU, memory, or cache utilization on Eventhouse
- Materialized view lag or freshness issues
- Always-On and minimum consumption sizing decisions
- Workspace monitoring setup and dashboard interpretation
- KQL query optimization for Real-Time Intelligence workloads
Prerequisites
- Microsoft Fabric workspace with Contributor or higher permissions
- Workspace monitoring enabled (for query/ingestion logs)
- Fabric Capacity Metrics app installed (for capacity-level analysis)
- KQL Queryset or Eventhouse query editor access
Step-by-Step Workflows
Workflow 1: Diagnose Slow KQL Queries
- Enable workspace monitoring if not already active. See workspace-monitoring.md
- Identify expensive queries using the diagnostic script: Run diagnose-slow-queries.kql against the monitoring Eventhouse
- Analyze query patterns — filter by Top CPU Time, Top Duration, or Memory Peak
- Apply KQL optimization rules from kql-optimization.md
- Validate improvement by re-running the query and comparing duration/CPU metrics
Workflow 2: Troubleshoot Ingestion Issues
- Check ingestion results logs using diagnose-ingestion.kql
- Review Eventstream data insights — check IncomingMessages, OutgoingMessages, BackloggedInputEvents, and WatermarkDelay metrics
- Identify failure patterns — deserialization errors, schema mismatches, throttling
- Apply throughput tuning per ingestion-remediate.md
- Validate pipeline health by monitoring runtime logs on source and destination nodes
What ships with it
10 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/capacity-and-sizing.md 6.5 KB
- references/ingestion-troubleshooting.md 6.8 KB
- references/kql-optimization.md 7.3 KB
- references/workspace-monitoring.md 4.2 KB
- scripts/diagnose-capacity.kql 6.1 KB
- scripts/diagnose-ingestion.kql 5.4 KB
- scripts/diagnose-slow-queries.kql 4.9 KB
- scripts/Invoke-RTIDiagnostics.ps1 8.0 KB runs code
- templates/health-check-report.md 2.4 KB
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 · 79 lines · 101 tokens per session scan A cd7ed83a78fd
fabric-rti-perf-remediate is a skill published in the GitHub repository PatrickGallucci/fabric-skills (16 stars, last pushed 3mo ago), licensed MIT. It adds 101 tokens to every session and 1,237 once invoked, about $0.0005 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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