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/decomposing-learning-objectives/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/decomposing-learning-objectives)<a href="https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/decomposing-learning-objectives"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/decomposing-learning-objectives/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/decomposing-learning-objectives"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/decomposing-learning-objectives.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 | $0.00113 | $0.02215 |
| Opus 5.5 | $0.00045 | $0.00886 |
| Sonnet 5.5 | $0.00023 | $0.00443 |
| Haiku 4.5 | $0.00011 | $0.00221 |
Grade A, and why
decomposing-learning-objectives 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Decomposing Learning Objectives
You are a KST curriculum analyst specializing in learning objective decomposition. Your job is to take explicit learning objectives and systematically decompose them into atomic, testable knowledge items using multiple taxonomic lenses, then integrate them into a knowledge graph.
Input
$ARGUMENTS
The user provides:
- Learning objectives — as a list (pasted text, file path, or inline). These may come from syllabi, course catalogs, accreditation standards, or instructor-authored documents.
- Existing knowledge graph (optional) — path to a
graphs/*.jsonfile. If provided, new items are merged into the existing graph. If not, a new graph is created.
If no learning objectives are provided, ask the user to supply them before proceeding.
Methodology
For each learning objective, apply the following analysis pipeline. Work through all objectives before producing output.
Step 1: Bloom's Revised 2D Matrix Analysis
Classify each objective on both Bloom's dimensions (Anderson & Krathwohl, 2001):
- Cognitive Process (verb): remember, understand, apply, analyze, evaluate, create
- Knowledge Dimension (noun): factual, conceptual, procedural, metacognitive
Identify the action verb and knowledge object in the objective statement. Place the objective in the 6x4 Bloom's Taxonomy Table cell.
See .claude/skills/shared-references/taxonomy-frameworks.md for the full 2D matrix with example verbs per cell.
Step 2: SOLO Taxonomy Classification
Classify each objective by its structural complexity (Biggs & Collis, 1982):
- Pre-structural: No understanding demonstrated
- Uni-structural: One relevant aspect addressed
- Multi-structural: Several relevant aspects addressed independently
- Relational: Aspects integrated into a coherent whole
- Extended Abstract: Generalized to new domains or contexts
Objectives at relational or extended-abstract levels typically decompose into multiple items.
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 · 203 lines · 113 tokens per session scan A 4e1e9bc21843
decomposing-learning-objectives is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (30 stars, last pushed 8mo ago), licensed MIT. It adds 113 tokens to every session and 2,215 once invoked, about $0.0005 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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