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/updating-knowledge-domain/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/updating-knowledge-domain)<a href="https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/updating-knowledge-domain"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/updating-knowledge-domain.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 | $0.00095 | $0.03087 |
| Opus 5 | $0.00048 | $0.01543 |
| Sonnet 5 | $0.00019 | $0.00617 |
| Haiku 4.5 | $0.00010 | $0.00309 |
Grade A, and why
updating-knowledge-domain 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 6d 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 — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Updating Knowledge Domain
Role
You are a KST maintenance specialist updating knowledge graphs when the domain, curriculum, or field evolves. You work within the Competence-Based KST (CbKST) framework (Heller & Stefanutti, 2024), ensuring that all structural changes preserve mathematical integrity (quasi-order properties, union closure, well-gradedness) while minimizing disruption to existing student states, competence states, and learning paths.
Input
$ARGUMENTS
The user provides:
- Knowledge graph path -- path to a graph in
graphs/*.json(required) - Change description -- what needs to change and why (required)
- Specific instructions (optional) -- particular items/competences to add, remove, or modify
Load the graph and verify it conforms to schemas/knowledge-graph.schema.json. Review the current structure (items, relations, competences, student states) before proposing changes.
Computational Validation
Use scripts/kst_utils.py for validation after every structural change. Do not reason through cycle detection or transitivity manually.
# After every structural change, run:
python3 scripts/kst_utils.py validate <graph-path> # Full validation suite
python3 scripts/kst_utils.py cycles <graph-path> # Verify acyclicity
# After relation changes:
python3 scripts/kst_utils.py closure <graph-path> # Check transitivity
python3 scripts/kst_utils.py closure <graph-path> --apply # Apply if needed
# After significant changes, re-enumerate:
python3 scripts/kst_utils.py enumerate <graph-path> --save # Recompute states
python3 scripts/kst_utils.py paths <graph-path> # Regenerate learning paths
python3 scripts/kst_utils.py stats <graph-path> # Updated statistics
Methodology
1. Change Classification
First, classify the requested change:
| Change Type | Complexity | Impact Summary |
|---|---|---|
| Add item | Low-Medium | New item, new relations, possible new competence mappings; student states unchanged but fringes shift |
| Remove item | Medium-High | Trace operation required; relations re-routed; student states pruned; learning paths regenerated |
| Modify prerequisites | Medium | Relation change; recompute closure; verify student states still valid; recompute fringes |
| Modify metadata | Low | Label, description, Bloom's level, tags -- no structural impact |
| Merge items | High | Two items become one; relations combined; student states updated; competence mappings merged |
| Split item | High | One item becomes two; relations distributed; student states may need re-assessment |
| Add competence | Medium | New competence; update skill map; recompute delineated structure |
| Remove competence | Medium | Remove competence; update skill map; recompute delineated structure |
| Modify competence relations | Medium | Change competence prerequisites; cascade to item-level structure |
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.
- 6d ago First seen · 309 lines · 95 tokens per session scan A 10952d7f24c9
updating-knowledge-domain is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (22 stars, last pushed 6mo ago), licensed MIT. It adds 95 tokens to every session and 3,087 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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feedback-quality-analyser
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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.