knowledge-spaces: Skill for Claude Code

.claude/skills/planning-adaptive-instruction/SKILL.md

planning-adaptive-instruction is a skill for Claude Code from vanderbilt-data-science/knowledge-spaces. It costs 100 tokens per session (2,591 once invoked), scanned A, original, MIT.

A lesson-planning tool that uses information about what several students know and can do. It creates a class plan, student groups, and peer-tutoring pairs using Universal Design for Learning, a framework for making teaching accessible in different ways.

In plain words
What is it for?
It helps plan lectures, labs, discussions, or workshops from a knowledge graph and class data. It can also identify teaching targets and suggest when more student assessment is needed.
Why use it?
It reduces the guesswork involved in teaching a class with mixed levels of understanding.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

This is vanderbilt-data-science/knowledge-spaces's own configuration. It tells Claude Code how to work on knowledge-spaces itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything knowledge-spaces configures →

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is python3 scripts/kst_utils.py analytics <graph-path>.

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/vanderbilt-data-science/knowledge-spaces/main/.claude/skills/planning-adaptive-instruction/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/vanderbilt-data-science/knowledge-spaces

Made for: Claude Code.

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.

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README.md
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<a href="https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/planning-adaptive-instruction"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/planning-adaptive-instruction.svg" alt="Measured on agentmods" height="20"></a>
Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,591 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00100 $0.02591
Opus 5 $0.00050 $0.01295
Sonnet 5 $0.00020 $0.00518
Haiku 4.5 $0.00010 $0.00259

Measured 8d ago against content hash 620d9bbc762d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

planning-adaptive-instruction 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 8d 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/planning-adaptive-instruction/SKILL.md · 261 lines

How it starts

The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Planning Adaptive Instruction

Role

You are a KST instructional planner analyzing class-wide knowledge states and competence profiles to produce just-in-time instruction plans. You work within the Competence-Based KST (CbKST) and Universal Design for Learning 3.0 (CAST, 2024) frameworks, translating aggregate student data into actionable session plans that maximize learning across the class.


Input

$ARGUMENTS

The user provides:

  • Knowledge graph path -- path to a graph in graphs/*.json with multiple students in student_states (required)
  • Session parameters -- duration (minutes), format (lecture, lab, discussion, workshop), available resources
  • Specific goals (optional) -- particular items or competences to prioritize
  • Constraints (optional) -- room layout, technology access, student needs

Load the graph and verify that student_states contains at least 2 students with assessed states. If insufficient student data exists, recommend running /assessing-knowledge-state for the class first.


Computational Core

Use scripts/kst_utils.py analytics for all class-wide computations. Do not compute mastery rates, target scores, or clusters manually.

# Run class-wide analytics:
python3 scripts/kst_utils.py analytics <graph-path>

# Output includes:
# - mastery_rates: {item_id: fraction of students who mastered it}
# - outer_fringe_freq: {item_id: count of students with this in outer fringe}
# - target_scores: {item_id: composite score (fringe_freq * (1 + leverage) * need)}
# - leverage: {item_id: number of items this unlocks}
# - clusters: student groups by Jaccard similarity >= 0.6
# - n_students: total student count

# Supplementary checks:
python3 scripts/kst_utils.py stats <graph-path>
python3 scripts/kst_utils.py validate <graph-path>

Methodology

1. Class-Wide State Analysis

Item-level statistics (from kst_utils analytics):

  • Mastery rate per item: fraction of students who have mastered each item
  • Outer fringe frequency per item: how many students have this item on their outer fringe (ready to learn)
  • Target score per item: composite of fringe frequency, leverage (how many items it unlocks), and need (1 - mastery rate)
  • Variance items: items where mastery rate is between 0.3 and 0.7 (high disagreement -- these differentiate the class)

Read the full file on GitHub · 261 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. 8d ago First seen · 261 lines · 100 tokens per session scan A 620d9bbc762d

Subscribe to this mod's changes

planning-adaptive-instruction is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (23 stars, last pushed 6mo ago), licensed MIT. It adds 100 tokens to every session and 2,591 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.

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