Extract Helper

The model receives the prompt (and optional system message). The run uses scorer contains_all with the JSON configuration below. Pass/fail and partial credit are determined entirely by that scorer against the model output; no human grading.

Refactor for readability without behavior change. Return only Python code.

def normalize_users(users):
    result = []
    for user in users:
        result.append(user["name"].strip().lower())
    return result

{
  "expected_contains": [
    "strip().lower()",
    "return"
  ]
}