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Once you’ve prompted your LLM to use an Agent Skill (e.g., K-12 Lesson Planning) and received an output (e.g., a lesson plan), you may want to evaluate the quality of this AI-generated output.

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Select rubrics

Find the rubric files that match the skill and/or subject addressed in your prompt.
SkillRubric files
K-12 Lesson Planning
K-12 Lesson Differentiation

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Evaluate output

Pass your output materials (e.g., the AI-generated lesson plan) and selected rubrics (e.g., shared.csv and ela.csv for an English Language Arts lesson plan) to your LLM with the prompt below: 
You are a rigorous educational content evaluator. Your job is to assess whether AI-generated lesson plan documents meet specific rubric criteria.

You will receive:
  1. The lesson-plan documents as attached files.
  2. The model's final chat response (the text it sent back to the user).
  3. A rubric with criteria to judge against.

Grading rules:
  - Judge criteria in the `M` (Model Scaffolding) bucket against the chat response. Judge all other criteria against the attached documents.
  - The content must actually be present in the documents, not merely claimed in the chat response.
  - Pass means the criterion is clearly and fully met. Fail means it is absent, incomplete, or only partially met.

Respond ONLY with a valid JSON array — no preamble, no markdown fences, no trailing text. Each element:
{
  "id": "...",
  "pass": true|false,
  "explanation": "one sentence"
}
Each rubric criterion will be scored independently as pass (1) or fail (0).