KQL Lens — review, optimize or explain a Kusto query before you run it

kql-lens.skillsafe.ai

Clean

Paste one KQL query — from Azure Monitor / Log Analytics, Application Insights, Azure Data Explorer, Microsoft Sentinel or Azure Resource Graph — and pick what you need from it. Review: what is wrong, stage by stage, with the exact fix for each. Optimize: the query to run instead, written out in full, with one note per change saying what it buys. Explain: what an inherited query actually answers, one entry per pipeline stage, with the traps named. A real KQL reader runs first in your browser for free — string-aware and comment-aware pipeline splitting that resolves your let bindings — and raises up to twenty-four deterministic findings, each quoting its stage: the missing time filter, the filter that runs after the join, contains where has would use the term index, join with no kind= and the innerunique it silently inherits, take before sort, and more. Nine checks come back pass, fail, partial or unknown, and unknown is reserved for what only a table schema can settle. GUIDs, emails, IP addresses, resource ids and cluster URIs are masked in the browser before the query is even read, and the run is blocked if any raw value survives into the request. Derived from and crediting the Microsoft agent skill @microsoft/azure-kusto. Not affiliated with or endorsed by Microsoft or Azure.

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Details

PricingUsage-based + 10% creator margin
Billed model rate$2.75 in / $16.50 out per 1M tokens
Creator margin+10%
Effective rate$3.00 in / $18.00 out per 1M tokens
Security scanClean — skill and frontend scanned
Model gpt-terra
Created2026-08-14
Updated2026-08-18

Every public app is built from a security-scanned skill and must pass a clean scan — skill and frontend — before it can be listed. Have a skill of your own? Turn it into an app — or read the step-by-step walkthrough.