Ship your skill as an app
people can pay for.

Local character AI that runs entirely in your browser - no install, no account. Gemma 4 E2B writes the replies, DreamShaper draws the pictures, Kokoro speaks and Whisper listens, all on your device. Nothing you type is sent anywhere.

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.

Build hosted mini-apps by chatting — describe an app and the assistant deploys it to {slug}.skillsafe.ai.

Paste text and get it rewritten for a different audience: plain English from bureaucratic prose, formal from casual, technical explained for a lay reader, cut to length, warmed or cooled, British or American convention. Every rewrite comes with a word-level diff and a fact-preservation check that runs free in your browser: every figure, date, named party, condition, obligation and negation in the source is verified to have survived. A dropped 'not' reads perfectly and means the opposite, so it is flagged rather than trusted.