This is for people who mostly use chat-style AI and keep feeling one tool behind. The worst outcome is reading a long post and still not knowing whether it matters to you. With agent safety, the easy mistake is treating it like a malware-only problem. The cost is simpler than that: you waste time, trust, and sometimes access on tools that feel helpful while asking for too much.
That is why NVIDIA SkillSpector is worth a second look. You see the name, almost scroll past, then stop because you do not want to miss the one detail that could change your next move. The useful part is not a feature list. The useful part is the judgment it pushes on you: agent safety should check intent before code.
In plain English, that means asking three boring questions before you get impressed by a tool. What does it claim it is for? What permissions does it want? Where does your data go? If those three answers do not line up, you already have a safety problem even before you inspect a single script.
The study result is what makes that point stick. Across 67,453 skills, SkillSpector caught semantic agentic-risk in 75.3% of suspicious samples, but only 6.8% of clearly malicious ones [S001]. So the stronger signal was not obvious evil. It was mismatch: the description, permissions, and data flow did not fit the claimed job.
That does not mean code stops mattering. It means code review is not the first filter. A new update is not worth your attention because of how many features it lists. It is worth your attention if it changes your next decision. Here, the decision is simple: read the intent boundary first, then read the code.
One boundary matters: this is based on study data, not a live product test. Still, the takeaway is useful right now. If you share AI tools with friends or coworkers, send them this rule: if a tool sounds small but asks for wide access, stop there and ask why.