What problem does it solve? Grading with AI risks stale data, fabricated claims about student work, duplicate comments, and FERPA violations. This Skill enforces a human-in-the-loop protocol so every grade and comment that reaches a student in Canvas is instructor-reviewed, honestly disclosed, and privacy-safe. ## Core Features & Use Cases - Consensus grading pipeline: Fetch submissions with grader_fetch.py, run 3-pass consensus grading (grader_grade.py --bulk → grader_consensus.py), and push results through grader_push.py with two mandatory in-chat instructor approval pop-ups. - FERPA de-identification: Submissions are stripped of names into opaque keys before reaching the LLM, then re-identified by key (never sort order) via grader_reidentify.py; Zone-2 name-bearing files are never read or displayed. - Disclosure tags and standing columns: Every comment carries a provenance tag (ai, hybrid, or script), and instructor-computed "your grade" columns are pushed roster-keyed with grader_standing.py. - Use Case: An instructor asks the agent to grade a challenge: the agent fetches fresh submissions, de-identifies them, drafts consensus grades and feedback, shows the full review surface in chat, and pushes only after the instructor clicks Allow at two guardian pop-ups. ## Quick Start Ask the agent to grade the submissions in grading/<challenge-name> and it will fetch fresh Canvas data, run consensus grading, show you the comments and old-to-new grade preview, and push only after your approval.