Agent skill

canvas-quiz

Write and review Canvas LMS quiz JSON files (INL1Quiz-*.json) for the tilkry cryptography course. Use proactively when: (1) creating, editing, or reviewing INL1Quiz JSON files, (2) user asks to write quiz questions for a lecture topic, (3) user asks to review quiz quality, redundancy, or distractor balance, (4) user mentions Canvas quiz, INL1Quiz, quiz JSON, or quiz questions. Covers JSON structure, question design, scoring, redundancy analysis, and validation.

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npx add-skill https://github.com/dbosk/claude-skills/tree/main/canvas-quiz

SKILL.md

Canvas Quiz Authoring

Overview

Write and review Canvas LMS quizzes stored as INL1Quiz-<topic>.json files. These quizzes use AllOrNothing scoring, require 100% to pass, allow unlimited retakes with a 1-hour cooldown, and test conceptual understanding of cryptography topics.

File Naming and Location

  • Pattern: modules/week-N/INL1Quiz-<topic>.json
  • Examples: INL1Quiz-ciphers.json, INL1Quiz-zkp.json, INL1Quiz-mpc.json

Up-to-Date Format Reference

The canvaslms CLI provides canonical, up-to-date JSON examples. Run these to get the latest format (always in sync with the tool):

bash
canvaslms quizzes create --example      # Full quiz envelope + all settings
canvaslms quizzes items add --example   # All question types with examples

These show every supported question type (choice, multi-answer, matching, true-false, ordering, rich-fill-blank, essay, file-upload, formula) with correct scoring_data structure for each.

JSON Structure

Quiz Envelope

json
{
  "quiz_type": "new",
  "settings": {
    "title": "INL1Quiz <Topic Name>",
    "instructions": "<HTML instructions>",
    "...other settings..."
  },
  "items": [ ]
}

Copy settings verbatim from an existing quiz file. Only change settings.title. For the canonical settings block, see references/quiz-settings.json.

Multi-Answer Question (primary format)

json
{
  "position": 1,
  "points_possible": 1.0,
  "entry": {
    "title": "Short Descriptive Title",
    "item_body": "<p>Check all statements that are true about [topic].</p>",
    "interaction_type_slug": "multi-answer",
    "scoring_algorithm": "AllOrNothing",
    "properties": {
      "shuffle_rules": {
        "choices": {
          "to_lock": [],
          "shuffled": false
        }
      }
    },
    "interaction_data": {
      "choices": [
        {
          "position": 1,
          "item_body": "<p>Statement text here.</p>"
        }
      ]
    },
    "scoring_data": {
      "value": [1, 3, 5]
    }
  }
}

Critical: scoring_data.value lists position numbers (1-indexed) of correct choices. Every value must match a choice position in interaction_data.choices.

Matching Question (for format variety)

json
{
  "position": 1,
  "points_possible": 1.0,
  "entry": {
    "title": "Match Properties",
    "item_body": "<div>Match terms with definitions.</div>",
    "interaction_type_slug": "matching",
    "scoring_algorithm": "DeepEquals",
    "properties": {
      "shuffle_rules": {
        "questions": { "shuffled": false }
      }
    },
    "interaction_data": {
      "answers": ["Definition A", "Definition B"],
      "questions": [
        { "item_body": "Term A" },
        { "item_body": "Term B" }
      ]
    },
    "scoring_data": {
      "value": {
        "<uuid-for-term-A>": "Definition A",
        "<uuid-for-term-B>": "Definition B"
      },
      "edit_data": {
        "matches": [
          {
            "answer_body": "Definition A",
            "question_id": "<uuid-for-term-A>",
            "question_body": "Term A"
          }
        ],
        "distractors": []
      }
    }
  }
}

Generate fresh v4 UUIDs for each question_id. Ensure scoring_data.value maps each UUID to the correct answer and edit_data.matches mirrors the mapping.

Question Design Principles

Target 6-8 Questions Per Quiz

Each quiz should have 6-8 questions covering different aspects of the topic.

Cognitive Levels (aim for a mix)

  1. Definitional: Match terms with definitions
  2. Mechanical: Protocol steps, equations, verification
  3. Conceptual: What a property guarantees, implications
  4. Applied: Concrete scenario, what happens?
  5. Analytical: Why does X work / not work?

Distractor Design

False choices must target specific misconceptions:

  • "Longer keys prevent side-channel attacks" (confuses algorithmic vs implementation security)
  • "Semi-honest security implies malicious security" (wrong adversary model relationship)
  • "Knowledge extraction contradicts zero-knowledge" (confuses who has rewinding power)

Avoid obviously wrong distractors like "MPC can only compute simple functions."

True/False Ratio

Aim for 55-75% true choices per question (e.g., 5 true out of 8 choices). Avoids "default to true" strategy (>80%) and "everything is a trick" feeling (<40%).

Contrast Pairs (Variation Theory)

Include choice pairs differing in one critical aspect:

  • TRUE: "Privacy guarantees that aside from the output, no additional information is revealed"
  • FALSE: "Privacy guarantees that no information about inputs is revealed, including through the output"

The critical aspect: privacy is relative to the output, not absolute.

Matching Distractors

Matching questions can include extra answers that don't match any term. Add them to the answers array and the distractors list in edit_data:

json
"interaction_data": {
  "answers": ["Stockholm", "Oslo", "Copenhagen", "Helsinki", "Berlin"],
  "questions": [
    { "id": "q1", "item_body": "Sweden" },
    { "id": "q2", "item_body": "Norway" }
  ]
},
"scoring_data": {
  "edit_data": {
    "matches": [ ... ],
    "distractors": ["Berlin"]
  }
}

Format Variety

Include at least one non-multi-answer question per quiz. Available types:

  • multi-answer with AllOrNothing — "select all true" (primary)
  • matching with DeepEquals — match terms to definitions
  • choice with Equivalence — single correct answer from choices
  • true-false with Equivalence — true/false statement
  • ordering with DeepEquals — arrange items in correct order

Run canvaslms quizzes items add --example for JSON examples of each type.

Redundancy Analysis

Within a Quiz

  1. Do multiple questions test the same definitional knowledge in different formats? (Redundant unless testing genuinely different aspects.)
  2. Is the same property tested in more than two questions? (Likely redundant.)
  3. Do questions describe the same protocol steps? (Acceptable only if testing different aspects: commitment properties vs protocol flow vs simulation.)

Across Quizzes (students take all quizzes in a week)

  1. Does a quiz include content from another topic's quiz? (e.g., ZKPK properties in an MPC quiz — redundant.)
  2. Are the same examples reused without new insight?

Acceptable Overlap

  • Different cognitive levels (definition vs application)
  • Different protocol aspects (commitment vs verification vs simulation)
  • Progression (general concept early, specific nuance later)

Validation

After editing quiz JSON, always run the validation script:

bash
python3 ~/.claude/skills/canvas-quiz/scripts/validate_quiz.py <quiz-file.json>

To validate multiple files:

bash
python3 ~/.claude/skills/canvas-quiz/scripts/validate_quiz.py modules/week-3/INL1Quiz-*.json

The script checks: valid JSON, required fields, scoring data consistency, true/false ratios, and prints a per-question summary.

Canvas Push Workflow

After editing:

bash
cd modules/week-N
make push-quizzes-questions       # Push with question replacement
make force-push-quizzes-questions  # Force push (ignores timestamps)

Resources

File Content
scripts/validate_quiz.py Validates quiz JSON structure and scoring data
references/quiz-settings.json Canonical quiz_settings block to reuse

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