e2e-test

Automated end-to-end evaluation of AI skills is done by simulating personas and orchestrating test sequences to measure performance. This approach applies persona-based E2E testing to exercise a skill in realistic contexts and generate automated scoring.

Persona-based testing for AI skill QA is the process of simulating user roles and workflows to evaluate a skill in realistic contexts. It compares outputs against defined criteria to generate scoring and insights, reducing manual QA effort and ensuring consistent testing across scenarios.

Run reproducible regression checks on AI skills by executing repeatable test sessions that simulate personas and orchestrate evaluation sequences. The process stores simulation results and scoring data, enabling consistent regression checks across multiple persona profiles.

Before triggering an AI skill simulation, you need access to personas/ directories, criteria/ evaluation rules, and the target skill's SKILL.md file. These elements provide the user roles, scoring benchmarks, and target definitions required to perform simulation and reporting.

Automated persona simulation supports testing multiple AI skill profiles by applying QA across various user roles and workflows. It exercises the target skill in multiple contexts and generates performance scoring and insights for each profile.

AI skill evaluation requires access to personas and criteria rules because the simulation needs defined user roles to exercise the skill and evaluation rules to compare outputs against benchmarks. Without these, the system cannot perform persona-based scoring or generate actionable insights.