canvas-bulk-grading skill | PlugMCP

canvas-bulk-grading

vishalsachdev319stars

Bulk grading workflows for Canvas LMS assignments using rubrics. Covers single grading, batch grading, and code execution strategies with safety-first dry runs.

Install GitHub

1---2name: canvas-bulk-grading3description: Bulk grading workflows for Canvas LMS assignments using rubrics. Covers single grading, batch grading, and…4---56# skills/canvas-bulk-grading/SKILL.md

SKILL.mdAgent skill

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SKILL.md

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1npx skills add vishalsachdev/canvas-mcp --skill canvas-bulk-grading -a cursor -y

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1npx skills add vishalsachdev/canvas-mcp --skill canvas-bulk-grading -a codex -y

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1Install this skill: https://github.com/vishalsachdev/canvas-mcp/tree/HEAD/skills/canvas-bulk-grading

Download skill skills/canvas-bulk-grading/SKILL.md on GitHub

skills/canvas-bulk-grading/SKILL.mdMarkdown · UTF-8

Skill facts

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File: skills/canvas-bulk-grading/SKILL.md
Publisher: vishalsachdev
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SKILL.md

name: canvas-bulk-grading
description: Bulk grading workflows for Canvas LMS assignments using rubrics. Covers single grading, batch grading, and code execution strategies with safety-first dry runs.

Canvas Bulk Grading

Grade Canvas LMS assignments efficiently using rubric-based workflows. This skill requires the Canvas MCP server to be running and authenticated with an instructor or TA token.

Prerequisites

Canvas MCP server running and connected
Authenticated with an educator (instructor/TA) Canvas API token
Assignment must exist and have submissions to grade
Rubric must be created and associated with the assignment with use_for_grading=true. Use create_rubric for creation and associate_rubric for an existing rubric; use update_rubric (two-call preview + token) for text/point edits; add or remove criteria in the Canvas UI.

Workflow

Step 1: Gather Assignment and Rubric Information

Before grading, retrieve the assignment details and its rubric criteria.

get_assignment_details(course_identifier, assignment_id)

Then get the rubric. Use get_rubric if the rubric is already linked to the assignment, or list_rubrics to browse all rubrics in the course:

get_rubric(course_identifier, assignment_id=assignment_id)
list_rubrics(course_identifier)
get_rubric(course_identifier, rubric_id=rubric_id)

Record the criterion IDs (often prefixed with underscore, e.g., _8027) and rating IDs from the rubric response. These are required for rubric-based grading.

Step 2: List Submissions

Retrieve all student submissions to determine how many need grading:

list_submissions(course_identifier, assignment_id)

Note the user_id for each submission and the workflow_state (submitted, graded, pending_review). Count the submissions that need grading to determine which strategy to use.

Step 3: Choose a Grading Strategy

Use this decision tree based on the number of submissions to grade:

How many submissions need grading?
|
+-- 1-9 submissions
|   Use grade_with_rubric (one call per submission)
|
+-- 10-29 submissions
|   Use bulk_grade_submissions (concurrent batch processing)
|   Set max_concurrent: 5, rate_limit_delay: 1.0
|   Run with dry_run: true first (Safety Rule 1)
|
+-- 30+ submissions OR custom grading logic needed
    Use execute_typescript with bulkGrade function
    Grading logic runs locally; only selected output returns to the model
    Pass dryRun: true on the first run

grade_with_rubric(
  course_identifier,
  assignment_id,
  user_id,
  rubric_assessment: {
    "criterion_id": {
      "points": <number>,
      "rating_id": "<string>",    // optional
      "comments": "<string>"      // optional per-criterion feedback
    }
  },
  comment: "Overall feedback"     // optional
)

bulk_grade_submissions(
  course_identifier,
  assignment_id,
  grades: {
    "user_id_1": {
      "rubric_assessment": {
        "criterion_id": {"points": 85, "comments": "Good analysis"}
      },
      "comment": "Overall feedback"
    },
    "user_id_2": {
      "grade": 92,
      "comment": "Excellent work"
    }
  },
  dry_run: true,          // VALIDATE FIRST
  max_concurrent: 5,
  rate_limit_delay: 1.0
)

execute_typescript(code: `
  import { bulkGrade } from './canvas/grading/bulkGrade.js';

  await bulkGrade({
    courseIdentifier: "COURSE_ID",
    assignmentId: "ASSIGNMENT_ID",
    dryRun: true,  // preview first; re-run with false after review
    gradingFunction: (submission) => {
      // Custom grading logic runs locally -- no token cost
      const notebook = submission.attachments?.find(
        f => f.filename.endsWith('.ipynb')
      );

      if (!notebook) return null; // skip ungraded

      return {
        points: 100,
        rubricAssessment: { "_8027": { points: 100 } }
        // No `comment` here on purpose -- see Safety Rule 6. Add one only when
        // the instructor asked for written feedback, and make it feedback.
      };
    }
  });
`)

Strategy	When	Token Cost	Why
`grade_with_rubric`	1-9 submissions	Low	Few round-trips, small payloads
`bulk_grade_submissions`	10-29 submissions	Medium	One call with batch data
`execute_typescript`	30+ submissions	Workload-dependent	Grading logic runs locally; only the code and selected output need to enter model context

Error	Cause	Action
401 Unauthorized	Token expired or invalid	Regenerate Canvas API token
403 Forbidden	Not an instructor/TA for this course	Verify Canvas role
404 Not Found	Wrong course, assignment, or rubric ID	Re-check IDs with `list_assignments` or `list_rubrics`
422 Unprocessable	Invalid rubric assessment format	Verify criterion IDs and point ranges match the rubric
Partial failures in bulk	Some grades submitted, others failed	Check each status. Unconfirmed assessments may already be saved: inspect Canvas before retrying to avoid duplicate comments. Retry only confirmed unsaved failures

canvas-bulk-grading

Install

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SKILL.md

Canvas Bulk Grading

Prerequisites

Workflow

Step 1: Gather Assignment and Rubric Information

Step 2: List Submissions

Step 3: Choose a Grading Strategy

Strategy A: Single Grading (1-9 submissions)

Strategy B: Bulk Grading (10-29 submissions)

Strategy C: Code Execution (30+ submissions)

Token Efficiency

Safety Rules

Example Prompts

Error Recovery