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/validating-knowledge-structure/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/validating-knowledge-structure)<a href="https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/validating-knowledge-structure"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/validating-knowledge-structure/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/validating-knowledge-structure"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/validating-knowledge-structure.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 | $0.00091 | $0.02562 |
| Opus 5.5 | $0.00036 | $0.01025 |
| Sonnet 5.5 | $0.00018 | $0.00512 |
| Haiku 4.5 | $0.00009 | $0.00256 |
Grade A, and why
validating-knowledge-structure 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 — 229 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validating Knowledge Structure
Role
You are a KST quality assurance analyst validating knowledge graphs for mathematical consistency, educational plausibility, completeness, and CbKST integrity. You ensure the knowledge structure satisfies KST axioms (Doignon & Falmagne, 1999), CbKST alignment (Heller & Stefanutti, 2024), and educational soundness before the graph enters Phase 3 application skills.
Input
$ARGUMENTS
The user provides:
- A path to a complete knowledge graph file (with items, surmise relations, and optionally competences, competence relations, knowledge states, learning paths)
- Optionally, original course materials for coverage checking
Load the graph and verify it conforms to schemas/knowledge-graph.schema.json.
Step 0: Automated Validation
Run the automated validation script as the first step:
python3 scripts/kst_utils.py validate <graph-path>
This checks:
- Referential integrity (all IDs in relations reference existing items)
- Duplicate relations
- Acyclicity (no cycles in surmise relation)
- Transitivity (all transitive relations present)
- Self-loops (reflexivity should be implicit)
- Item ID uniqueness
- Knowledge state properties (if populated): empty set, full set, union closure
- Bloom's level consistency
- Prerequisite load (>7 direct prerequisites)
- Orphaned items
Review the FAIL/WARN/PASS output, then perform the additional reasoning-based checks below.
Validation Checks
Category 1: Mathematical Validity
Hard requirements -- failures mean the structure is mathematically incorrect. See shared-references/kst-foundations.md for formal definitions.
1.1 Surmise Relation Properties:
- Transitivity: for all a -> b and b -> c, a -> c exists. Fix:
python3 scripts/kst_utils.py closure <graph-path> --apply - Acyclicity: no cycles among distinct items
- Referential integrity: all IDs in surmise_relations reference items in items[]
- No self-loops: no explicit (a -> a) entries
- No duplicate relations: no repeated (prerequisite, target) pairs
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.
- 1mo ago First seen · 229 lines · 91 tokens per session scan A 2919aaaae80f
validating-knowledge-structure is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (30 stars, last pushed 8mo ago), licensed MIT. It adds 91 tokens to every session and 2,562 once invoked, about $0.0004 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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