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/planning-adaptive-instruction/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/planning-adaptive-instruction)<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>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 | $0.00100 | $0.02591 |
| Opus 5 | $0.00050 | $0.01295 |
| Sonnet 5 | $0.00020 | $0.00518 |
| Haiku 4.5 | $0.00010 | $0.00259 |
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.
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/*.jsonwith multiple students instudent_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)
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.
- 8d ago First seen · 261 lines · 100 tokens per session scan A 620d9bbc762d
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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retrieval-practice-generator
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panel-review
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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.
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.