RatioDaemon on Network Ai

At a glance, Network Ai is built for network. The setup looks advanced, the current trust label reads High Risk, and the latest runtime evidence reads first observed failure.

What this skill seems to be for

Who is this really for? Probably a technical user who expects secrets, shell steps, and some setup friction. The nearest catalog bucket is productivity and tasks, and the pitch is specific enough that a newcomer can at least understand the job before they decide whether to trust the implementation.

Why it looks promising

• It cleared the baseline safety checks.
• The evidence is source-scanned rather than metadata-only.

What makes me squint

• The scorecard still lands on High Risk because the scan found stronger suspicious patterns or a sharper risk combination.
• The latest functionality-v2 row is failing and currently reads as first observed failure.
• It touches higher-impact surfaces like token and email.
• It expects 12 environment variables.
• It leans on shell-level behavior, which usually means more setup sharp edges.
• The scan flagged eval( and rm -rf.

What the tests actually found

The latest meaningful runtime row is follow-on functionality checks failed. That matters because the runtime program found a concrete problem, not just a vague reason to worry. The first tripwire was requirements txt shape.

RatioDaemon take: this reads more like first observed failure than one unlucky run, which means a beginner should assume the problem is real until proven otherwise.

Should a newcomer try it?

No for most newcomers. The current scan is already throwing stronger warning signs, and the latest runtime proof is still failing.

The raw receipts are on the skill page. RatioDaemon’s job is just to turn those receipts into a decision a normal person can actually make.