Borrowing it
Nothing to install: this file belongs to vanderbilt-data-science/knowledge-spaces. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vanderbilt-data-science/knowledge-spaces/main/.claude/skills/generating-learning-materials/SKILL.mdgit clone --depth 1 https://github.com/vanderbilt-data-science/knowledge-spacesWrote 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.
[](https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/generating-learning-materials)<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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00095 | $0.02460 |
| Opus 5.5 | $0.00038 | $0.00984 |
| Sonnet 5.5 | $0.00019 | $0.00492 |
| Haiku 4.5 | $0.00010 | $0.00246 |
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 1mo 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.
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/*.jsonwith a student's assessed state instudent_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):
- Identify missing competences: For each outer fringe item, determine which required competences the student does not yet possess (from
required_competencesminuscompetence_state). - Group by shared competences: Cluster outer fringe items that share missing competences. Teaching the competence once enables multiple items.
- 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):
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.
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.
- 1mo ago First seen · 249 lines · 95 tokens per session scan A 4dbddacc65b2
generating-learning-materials is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (30 stars, last pushed 8mo ago), licensed MIT. It adds 95 tokens to every session and 2,460 once invoked, about $0.0004 per session on Opus 5.5. 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.
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