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Educator peer review management for Canvas LMS. Tracks completion rates, analyzes comment quality, flags problematic reviews, sends targeted reminders, and generates instructor-ready reports. Trigger phrases include "peer review status", "how are peer reviews going", "who hasn't reviewed", "review quality", or any peer review follow-up task.
Passed 15 of 18 test cases with the skill, and 2 of 18 without it.
and 15 more in the full report
15 fixed3 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
When generating the prioritize follow-up list for peer reviews in ECON 101, I want to calculate urgency based on reviews that have been pending for more than 3 days. What parameter controls this time threshold in `get_peer_review_followup_list`?
| Checked | Without skill | With skill |
|---|---|---|
| Specifies setting `days_threshold=3` on `get_peer_review_followup_list`. | Missed | Passed |
The instructions your model reads when the skill loads.
A complete peer review management workflow for educators using Canvas LMS. Monitor completion, analyze quality, identify students who need follow-up, send reminders, and export data -- all through MCP tool calls against the Canvas API.
ENABLE_DATA_ANONYMIZATION=true in the Canvas MCP server environment to anonymize supported student identity fields. When enabled, names render as Student_xxxxxxxx hashes while preserving functional user IDs for messaging. This control does not by itself establish compliance.Ask the user which course and assignment to manage peer reviews for. Accept a course code, Canvas ID, or course name, plus an assignment name or ID.
If the user does not specify, prompt:
Which course and assignment would you like to check peer reviews for?
Use list_courses and list_assignments to help the user find the right identifiers.
Call get_peer_review_completion_analytics with the course identifier and assignment ID. This returns:
Key data points to surface:
| Metric | What It Tells You |
|---|---|
| Completion rate | Overall health of the peer review cycle |
| "None complete" count | Students who haven't started -- highest priority for reminders |
| "Partial complete" count | Students who started but didn't finish |
| Per-student breakdown | Exactly who needs follow-up |
test cases passed, vs 2 / 18 without
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