generating-learning-materials

generating-learning-materials is a skill for Claude Code, Codex from vanderbilt-data-science/knowledge-spaces. It costs 95 tokens per session (2,460 once invoked), scanned A, original, MIT.

A personalized learning-materials tool that uses a student’s assessed knowledge state, meaning a record of what they have learned. It creates lessons or practice focused on the next suitable topics.

In plain words
What is it for?
It helps create worked examples, practice problems, or explanations for selected targets or for the student’s next learning needs. The material is adjusted to the student’s current level.
Why use it?
It avoids giving every student the same material when their knowledge gaps and readiness differ.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/vanderbilt-data-science/knowledge-spaces/generating-learning-materials
Any agent
npx skills add vanderbilt-data-science/knowledge-spaces --skill generating-learning-materials
Clone the repo
git clone --depth 1 https://github.com/vanderbilt-data-science/knowledge-spaces

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for generating-learning-materials

README.md
[![agentmods](https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/generating-learning-materials.svg)](https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/generating-learning-materials)
Your own site
<a href="https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/generating-learning-materials"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/generating-learning-materials.svg" alt="Measured on agentmods" height="20"></a>
Per session 95 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,460 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00095 $0.02460
Opus 5 $0.00048 $0.01230
Sonnet 5 $0.00019 $0.00492
Haiku 4.5 $0.00010 $0.00246

Measured 3d ago against content hash 4dbddacc65b2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

generating-learning-materials scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/generating-learning-materials/SKILL.md · 249 lines

How it starts

The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Generating Learning Materials

Role

You are a KST instructional designer generating personalized learning materials grounded in Competence-Based Knowledge Space Theory (CbKST) and Universal Design for Learning 3.0 (CAST, 2024). Your task is to create learning modules that target a student's outer fringe items, organized by the competences those items require, with scaffolding appropriate to the student's current state.


Input

$ARGUMENTS

The user provides:

  • Knowledge graph path -- path to a graph in graphs/*.json with a student's assessed state in student_states (required)
  • Student identifier -- the student whose state to use (required)
  • Specific target items (optional) -- if omitted, target all outer fringe items
  • Material type preference (optional) -- e.g., "worked examples", "practice problems", "conceptual explanations"

Load the graph and verify the student has an assessed state with current_state, inner_fringe, and outer_fringe. If the student has no assessed state, recommend running /assessing-knowledge-state first.


Methodology

1. CbKST Competence-Level Targeting

Rather than treating each outer fringe item independently, organize materials around the underlying competences (Heller & Stefanutti, 2024):

  1. Identify missing competences: For each outer fringe item, determine which required competences the student does not yet possess (from required_competences minus competence_state).
  2. Group by shared competences: Cluster outer fringe items that share missing competences. Teaching the competence once enables multiple items.
  3. Design competence-first: For each missing competence, create materials that teach the competence explicitly, then demonstrate its application across the items that require it.

For the full CbKST framework, skill maps, and delineation mechanics, see .claude/skills/shared-references/cbkst-overview.md.

2. Zone of Proximal Development

Map the student's knowledge to Vygotsky's ZPD (1978):

Read the full file on GitHub · 249 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 249 lines · 95 tokens per session scan A 4dbddacc65b2

Subscribe to this mod's changes

generating-learning-materials is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (20 stars, last pushed 6mo ago), licensed MIT. It adds 95 tokens to every session and 2,460 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

criterion-referenced-rubric-generator

Generate a criterion-referenced rubric with descriptive performance levels for a task or objective. Use for marking guides and general curriculum contexts. For Manning programmes where Competent = success, use coherent-rubric-logic-builder instead.

GarethManning/education-agent-skills · 52 tokens

feedback-quality-analyser

Analyse existing written feedback for quality, specificity, actionability, and impact on student learning. Use when reviewing teacher or peer feedback to improve feedback practices.

GarethManning/education-agent-skills · 35 tokens

retrieval-practice-generator

Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

GarethManning/education-agent-skills · 36 tokens

kud-chart-author

Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.

GarethManning/education-agent-skills · 46 tokens

project-brief-designer

Design a project-based learning brief with a driving question, milestones, and assessment criteria. Use when planning PBL units, inquiry projects, or extended investigations.

GarethManning/education-agent-skills · 37 tokens

learning-analytics-interpretation-guide

Interpret learning analytics data and translate dashboard findings into actionable teaching decisions. Use when reviewing LMS data, quiz patterns, or engagement metrics.

GarethManning/education-agent-skills · 34 tokens