Build a grading calibration workbook from de-identified submissions
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1 method on the page: 1 prompt.
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Paste into a general LLM and build a grading calibration workbook from de-identified submissions.
LLMPrompt
Prompt
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Review the de-identified submissions, rubric, grading export, sample feedback, course outcomes, and calibration rules I provide. Create a calibration workbook that: - flags possible scoring inconsistencies - summarizes common strengths and gaps - identifies feedback themes - suggests rubric clarifications - cites evidence for every flag Do not assign or change grades. Route every scoring judgment to faculty review.
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| Field | What it is |
|---|---|
jobId | This use case. Review queue uses useCaseTitle + email instead. |
summary | Method description. Shown on the card. Also accepts title. |
products | Optional. Array of { name, url }. Legacy: tools. |
prompts | Optional. Array of prompt strings. Legacy: one prompt string. |
skills | Optional. Array of { text, url }. Legacy: skill + skillUrl. |
authorName | Author name. Legacy live field: postedBy. |
author | Optional 𝕏 handle. Legacy live field: creditHandle. |
parts | Optional. Inferred from products, prompts, and skills if omitted.standalone-product, ai-agent, ai-tool, skill, prompt. Do not send a prompt/product/agent/skill radio. |
{
"jobId": "assessment-calibration-workbook",
"summary": "Turns a Zoom recording into notes and action items with owners",
"products": [
{
"name": "Claude",
"url": "https://claude.ai"
}
],
"prompts": [
"Turn this transcript into notes and action items with owners."
],
"skills": [
{
"text": "Watch a folder. When a transcript lands, write notes with owners.",
"url": "https://example.com/zoom-notes.md"
}
],
"authorName": "Alex Rivera",
"author": "@handle"
}