Introduction

Overview of Learning Commons Agent Skills for K-12 education workflows.

The problem

AI agents can produce plausible-looking lesson materials quickly, but quality varies widely.

Standards alignment, grade-level rigor, curriculum coherence, and classroom usability are hard to guarantee from a single prompt.

What we’re building

Agent Skills

Our Agent Skills package the instructions, references, and guardrails an agent needs to reliably complete specific K-12 teaching workflows.

Each skill is cross-platform, model-agnostic, and produces stronger results when paired with Learning Commons Knowledge Graph.

Skill	Description	Development partner
K-12 Lesson Planning	Builds classroom-ready, standards-aligned lesson plans, optionally aligned to a teacher’s curriculum.	Anthropic ↗
K-12 Lesson Differentiation	Adapts an existing lesson into tiered versions (below / at / above proficiency level) and for specific student needs, keeping core content consistent across tiers.	Anthropic ↗

See example prompts ↗ for prompts that exercise each workflow.

Rubrics

This is part of a larger evaluator harness that Learning Commons plans to publish in full at a later date. Manual use instructions are included for now.

Each Agent Skill comes with a corresponding rubric that assesses whether your AI-generated K-12 content is explainable and tied to authoritative sources like academic standards, learning science research, and high-quality instructional materials.

Skill	Rubrics
K-12 Lesson Planning	Common rubric: shared.csv Subject-specific rubric math.csv ela.csv science.csv social_studies.csv
K-12 Lesson Differentiation	differentiation.csv clarifying_question.csv

Each rubric is represented as a CSV file with the following fields:

Field	Description
id	Unique criterion identifier (e.g., P1, R3)
bucket	Top-level category: P (Pedagogy), R (Rigor), O (Output/Formatting), or M (Model Scaffolding)
criterion	Short name for the criterion
what_pass_requires	Specific and scoreable condition that constitutes a pass
conditional	If non-empty, the criterion applies only when this condition is met (e.g., K-5, ELA-Gr8+) conditional criteria are applied only when the specified condition is met; otherwise, they are skipped (not failed).
notes	Rationale or design notes

Track per-criterion pass rates across a prompt suite rather than relying on aggregate scores alone.

Since criteria score independently, a failing “Rigor” criterion simply tells you something specific about cognitive demand – it does NOT mean that the output is “bad”.

Scope and limitations

Remember that LLM outputs can vary across runs, especially on borderline cases. We recommend keeping a human in the loop and treating outputs as directional signals vs. definitive classroom decisions.

Limitation	Details
Validated workflows	Initial skills focus on K-12 lesson planning and differentiation across math, ELA, science, and social studies.
Knowledge Graph dependency	Skills work without Knowledge Graph but produce the strongest outputs when connected to standards and curriculum data.
Curriculum coverage	Curriculum-specific branches cover supported materials such as Illustrative Mathematics and OpenSciEd; other curricula fall back to general best practice.

Agent Skills outputs should not be used for high-stakes applications like grading, assessment, or placement decisions without human review.

Related topics

K-12 Lesson Planning

See shared and subject-specific rubrics for lesson plan outputs.

K-12 Lesson Differentiation

See rubrics for tiered differentiation and clarifying questions.

Core concepts

Learn how skills, rubrics, and the P/R/O/M framework work together.

Quickstart

Install skills, connect Knowledge Graph, and try example prompts.