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 manu14357/zskills --skill azure-kustogit clone --depth 1 https://github.com/manu14357/zskillsWrote 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/manu14357/zskills/azure-kusto)<a href="https://agentmods.dev/skills/manu14357/zskills/azure-kusto"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/azure-kusto/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/manu14357/zskills/azure-kusto"><img src="https://agentmods.dev/badge/skills/manu14357/zskills/azure-kusto.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 | $0.00048 | $0.02815 |
| Opus 5 | $0.00024 | $0.01407 |
| Sonnet 5 | $0.00010 | $0.00563 |
| Haiku 4.5 | $0.00005 | $0.00281 |
Grade A, and why
azure-kusto 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 11d 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 — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Kusto
Produce clear, performant KQL queries for diagnostics, reporting, operational insights, and incident investigations.
Use This Skill When
- The user asks to write a KQL query for diagnostics or reporting
- The user needs query optimization or performance tuning
- The user is investigating incidents using Azure logs
- The user wants to aggregate telemetry, analyze trends, or build alerts
Context: Query Maturity
Immature: Ad hoc queries, unbounded scans, timeouts
Developing: Filtered queries, basic aggregation
Managed: Optimized execution, good performance, reusable queries → Target
Optimized: Query compilation caching, materialized views, automated insights
Required Inputs
- Data source: Azure Monitor (Log Analytics), Application Insights, Data Explorer?
- Table names: Exact names (AzureActivity, SecurityEvent, CustomEvents)?
- Time range: Last hour? Last 7 days? Specific dates?
- Filters: Specific resources, servers, error codes, user IDs?
- Output needed: Row count? Aggregation? Time series chart? Alert condition?
- Performance constraints: Must complete in 5 seconds? 30 seconds OK?
Decision Tree
What's the primary objective?
├─ Count/summarize → Use summarize operator, aggregate early
├─ Find specific records → Use where filter first, then project
├─ Time-series analysis → Use bin(timestamp), render timechart
├─ Correlation/join → Join multiple tables, but use small left table
└─ Alert condition → Simple condition with threshold
What's the expected data volume?
├─ < 1 million rows → Simple query, no optimization needed
├─ 1-100 million rows → Filter early, use summarize
└─ > 100 million rows → Partition by time, use external tools
How fast does this need to run?
├─ <5 seconds (dashboard) → Aggressive filtering, pre-aggregation
├─ <30 seconds (report) → Standard optimization
└─ <5 minutes (batch analysis) → Can be less optimized
Workflow
Phase 1: Start with Schema Understanding
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.
- 11d ago First seen · 355 lines · 48 tokens per session scan A 8d728fa78e45
azure-kusto is a skill published in the GitHub repository manu14357/zskills (16 stars, last pushed 1mo ago), licensed MIT. It adds 48 tokens to every session and 2,815 once invoked, about $0.0002 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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