Instructions

Objective

Integrate data from Canvas LMS, user memory, and local workspace files to identify and report on unsubmitted academic work. The primary output is formatted CSV files containing sorted lists of pending quizzes and assignments.

Core Workflow

1. Initial Data Gathering

• Read User Memory: Use

  memory-read_graph

  to retrieve the user's personal information. Pay special attention to academic exemptions (e.g., English entrance exam score).
• Fetch Course List: Use

  canvas-canvas_list_courses

  to get all enrolled courses. Note the

  course_code

  and

  name

  for each.

2. Understand Output Format

• Inspect Workspace: Use

  local-python-execute

  to list files in

  /workspace/dumps/workspace/

  and examine any existing CSV files (e.g.,

  quiz_info.csv

  ,

  assignment_info.csv

  ).
• Analyze Headers: Note the required CSV columns and data format from the example rows.

3. Collect Pending Work Details

• Fetch Assignments & Quizzes: For each course ID obtained in step 1, call:

  • canvas-canvas_list_assignments

    with

    include_submissions: true

    .

  • canvas-canvas_list_quizzes

    .
• Identify Unsubmitted Items:
  • Assignments: An item is "unsubmitted" if

    submission.workflow_state

    is

    "unsubmitted"

    AND

    is_quiz_assignment

    is

    false

    .
  • Quizzes: A quiz is "required" if its

    quiz_type

    is

    "assignment"

    (graded). Ignore

    "practice_quiz"

    types.
  • Apply Exemptions: Cross-reference user memory with course announcements. For example, if a user's English entrance score is ≥90 and a course announcement grants an exemption, exclude that quiz.

4. Enrich Data & Sort

• Fetch Course Details: Optionally use

  canvas-canvas_get_course

  for each course if additional metadata (like credits) is needed but not in the initial list.
• Determine Credits: If credit information is unavailable via API, infer it logically (e.g., 101/201/301-level = 3 credits, 401-level = 4 credits) based on the

  course_code

  .
• Clean Data: Remove the

  -x

  suffix (e.g.,

  -1

  ) from

  course_code

  and

  course_name

  .
• Sort Final Lists:
  1. Primary Key: Deadline (

     due_at

     ) in chronological order.
  2. Secondary Key: Course code in dictionary (alphabetical) order for items with identical deadlines.

5. Generate Output Files

• Write CSVs: Use

  local-python-execute

  to run a Python script that:
  1. Creates the sorted lists of quizzes and assignments.
  2. Writes to the existing CSV file paths in the workspace without changing the filenames.
  3. Formats data exactly as the example headers specify.
• Verify: Read and print the final CSV contents to confirm correct formatting and sorting.

Key Decision Points

• Inclusion/Exclusion Logic: Document why specific items were included or excluded (e.g., "exempt due to high English score", "practice quiz not graded").
• Data Fallbacks: When API data is missing (e.g., credits), use reasoned defaults and note the assumption.
• Sorting Validation: Double-check that sorting by deadline then course code is correctly implemented.

Final Output

The skill is complete when:

1. The

   quiz_info.csv

   and

   assignment_info.csv

   files in the workspace are updated with the current, sorted list of unsubmitted work.
2. A clear summary is provided explaining what was found and any logical decisions made.