You're half right and the other half is the gap worth naming. The bottom-three dimensions in the 500-prompt batch were:
Examples: 1.01 / 10
Constraints: 1.09 / 10
Role Definition: 1.18 / 10
Your "parseable structure" concern maps directly to Output Format, which scored 1.90, not the worst but still failing. Your "handles malformed input" concern is adjacent but technically a different frame.
PQS scores the prompt before it hits the model. "Graceful failure on malformed input" is about robustness under adversarial conditions at runtime, which is post-inference territory and not where we score.
Both matter. They're sequential, not competing. Fix the input quality first, then harden for adversarial conditions. Skipping step 1 and going straight to adversarial hardening is how teams end up with bulletproof wrappers around garbage prompts.
The counterintuitive finding for me: Examples at 1.01 was worse than any output-side dimension. Nobody shows the model what "good" looks like before asking for it, and the downstream cost of that omission is larger than any adversarial input handler can compensate for.
What's your typical audit finding on examples? I'd bet it's also the dimension most teams think they don't need.