meridian-simulated-user-panel

To simulate user persona feedback, you generate evidence-grounded reactions from multiple personas like quant analysts and fund managers by providing an artifact bundle. The simulation grounds all feedback strictly in your provided evidence to ensure realistic, owner-minded product direction insights.

Yes, persona simulation supports release gate readiness decisions by providing mode-specific review outputs. The release gate mode applies rubric scoring and a manifest-driven evaluation workflow to measure feedback, ensuring near-ship release readiness is repeatable and deterministic.

A manifest-driven review process for usability benchmarking uses a review manifest contract, schemas, and evaluation fixtures to make feedback measurable. It applies a deterministic scoring harness to artifact-driven usability lab comparisons, ensuring repeatable results across workflow evaluations.

Yes, you need Python installed to run artifact-based persona evaluations. The Skill relies on Python scripts to construct persona panels from references, apply rubric scoring, and enforce the shared Output Contract for evidence-grounded simulated feedback.

The best way to evaluate onboarding critiques without generic opinions is using artifact-first grounding with rubric scoring. This approach forces the simulated user panel to cite specific evidence from your bundle, separating confidence evidence from subjective reactions.

You should not use simulated user panels when you lack sufficient artifact evidence to ground the evaluation. The process requires a manifest-driven review workflow and artifact-first grounding, meaning feedback quality degrades if the provided evidence bundle is incomplete or missing.