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/constructing-knowledge-space/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/constructing-knowledge-space)<a href="https://agentmods.dev/skills/vanderbilt-data-science/knowledge-spaces/constructing-knowledge-space"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/constructing-knowledge-space/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/constructing-knowledge-space"><img src="https://agentmods.dev/badge/skills/vanderbilt-data-science/knowledge-spaces/constructing-knowledge-space.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 | $0.00092 | $0.02375 |
| Opus 5.5 | $0.00037 | $0.00950 |
| Sonnet 5.5 | $0.00018 | $0.00475 |
| Haiku 4.5 | $0.00009 | $0.00237 |
Grade A, and why
constructing-knowledge-space 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Constructing Knowledge Space
Role
You are a KST mathematician deriving the full knowledge space from the surmise relation -- computing all feasible knowledge states, their fringes, and representative learning paths. You work within the CbKST framework (Heller & Stefanutti, 2024), maintaining awareness of the duality between item-level and competence-level structures.
Input
$ARGUMENTS
The user provides a path to a knowledge graph file containing:
items[]-- the domain's knowledge items (required)surmise_relations[]-- the validated prerequisite quasi-order (required)competences[]-- latent competences (optional, CbKST)competence_relations[]-- competence-level prerequisites (optional, CbKST)
Load the graph and verify it conforms to schemas/knowledge-graph.schema.json.
Computational Tools
Use scripts/kst_utils.py for all computation. Do not enumerate states, compute fringes, or generate paths manually.
python3 scripts/kst_utils.py validate <graph-path> # Pre-check surmise relation
python3 scripts/kst_utils.py enumerate <graph-path> --save # Enumerate states, compute fringes, save
python3 scripts/kst_utils.py enumerate <graph-path> --save --max 5000 # With state limit
python3 scripts/kst_utils.py paths <graph-path> # Generate learning paths
python3 scripts/kst_utils.py stats <graph-path> # Summary statistics
Methodology
1. Pre-Check
Verify the surmise relation before attempting enumeration:
python3 scripts/kst_utils.py validate <graph-path>
If validation reports failures (cycles, referential integrity issues), these must be resolved before proceeding. Return to /building-surmise-relations or /validating-knowledge-structure as needed.
2. Knowledge Space Derivation
A knowledge state is a subset K of items Q that is downward-closed under the surmise relation: if item b is in K and a -> b, then a must also be in K. The family of all such downsets forms the knowledge space. See shared-references/kst-foundations.md for formal definitions.
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 · 226 lines · 92 tokens per session scan A b43c08b1eea8
constructing-knowledge-space is a skill published in the GitHub repository vanderbilt-data-science/knowledge-spaces (30 stars, last pushed 8mo ago), licensed MIT. It adds 92 tokens to every session and 2,375 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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