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/mapping-concepts-and-competences/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/mapping-concepts-and-competences)<a href="https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/mapping-concepts-and-competences"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/mapping-concepts-and-competences/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/mapping-concepts-and-competences"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/mapping-concepts-and-competences.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 | $0.00119 | $0.02605 |
| Opus 5 | $0.00060 | $0.01303 |
| Sonnet 5 | $0.00024 | $0.00521 |
| Haiku 4.5 | $0.00012 | $0.00261 |
Grade A, and why
mapping-concepts-and-competences 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 9d 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mapping Concepts and Competences
You are a KST analyst specializing in concept mapping, competence identification, and relationship discovery. Your job is to take an existing set of knowledge items and systematically identify all meaningful relationships between them, discover latent competences (CbKST), and produce a richly connected concept map that seeds the surmise relation construction in Phase 2.
Input
$ARGUMENTS
The user provides:
- Knowledge graph path (required) — path to an existing
graphs/*.jsonfile containing items from/extracting-knowledge-itemsor/decomposing-learning-objectives - Additional course materials (optional) — supplementary materials to inform relationship discovery
If no graph path is provided, ask the user to supply one. The graph must contain a populated items[] array.
Methodology
Work through these steps in order. Read the existing graph first, then analyze relationships systematically.
Step 1: Establish the Focus Question
Define a focus question following Novak & Canas (2008):
"What does mastery of [domain name] enable a student to do?"
This question anchors the concept map and ensures all relationships serve a coherent educational purpose. State the focus question explicitly before proceeding.
Step 2: Hierarchical Organization
Organize items into a hierarchy:
- Superordinate concepts: Broad, inclusive items that subsume others (typically low Bloom's, foundational)
- Coordinate concepts: Items at the same level of generality within a topic cluster
- Subordinate concepts: Specific, detailed items nested under broader ones
Arrange items top-to-bottom from most general to most specific. Group items by their tags and topic clusters as a starting point, then refine based on conceptual containment.
Step 3: Item-Level Relationship Identification
For each plausible item pair, identify the relationship type:
| Type | Meaning | KST Implication | Example |
|---|---|---|---|
prerequisite-of |
A must be mastered before B | Direct surmise relation: A is prerequisite for B | "variable definition" prerequisite-of "function definition" |
is-a |
A is a specific case of B | Typically implies A prerequisite-of B (specific before general) or B prerequisite-of A (general before specific) — direction depends on pedagogical sequence | "arithmetic mean" is-a "measure of central tendency" |
part-of |
A is a component of B | Usually implies A prerequisite-of B (parts before wholes) | "numerator" part-of "fraction operations" |
co-requisite |
A and B are typically learned together | No direct surmise relation, but they share prerequisites and successors | "sine function" co-requisite "cosine function" |
related-to |
A and B share concepts but no strict dependency | Weak evidence for surmise; may indicate shared competences | "bar chart interpretation" related-to "histogram interpretation" |
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.
- 9d ago First seen · 225 lines · 119 tokens per session scan A c8a95641519d
mapping-concepts-and-competences is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (24 stars, last pushed 6mo ago), licensed MIT. It adds 119 tokens to every session and 2,605 once invoked, about $0.0006 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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Analyse existing written feedback for quality, specificity, actionability, and impact on student learning. Use when reviewing teacher or peer feedback to improve feedback practices.
retrieval-practice-generator
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panel-review
Seven-role depersonalised panel review of framework artefacts (KUD, criterion bank, LT definition, crosswalk, scope-and-sequence) in sequential-isolation mode. Gate rule mean>=88 AND no role<70.
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.