Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CUHK-AIM-Group/NeuroClaw --skill knowledge-graph-buildergit clone --depth 1 https://github.com/CUHK-AIM-Group/NeuroClawWrote 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/cuhk-aim-group/neuroclaw/knowledge-graph-builder)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/knowledge-graph-builder"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/knowledge-graph-builder/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/cuhk-aim-group/neuroclaw/knowledge-graph-builder"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/knowledge-graph-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 78 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00116 | $0.03973 |
| Opus 5 | $0.00058 | $0.01987 |
| Sonnet 5 | $0.00023 | $0.00795 |
| Haiku 4.5 | $0.00012 | $0.00397 |
Grade A, and why
knowledge-graph-builder 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 — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Knowledge Graph Builder
Overview
This skill provides a reusable framework for constructing domain-specific knowledge graphs by combining two complementary data pipelines:
- Phase 1 — Structured Ingestion: Import concepts and relations from curated databases, ontologies, and brain atlases (e.g., NeuroNames, MeSH, DisGeNET, Cognitive Atlas, Nilearn atlases).
- Phase 2 — Literature Claim Extraction: Use LLMs to extract structured scientific claims from PubMed paper abstracts, then resolve entities and ingest into the graph.
- Phase 3 — Hypothesis Engine: Traverse the graph to find novel connections, contradictions, and unexplored gaps — turning raw claims into testable research hypotheses.
The output is a directed knowledge graph (NetworkX DiGraph + JSON serialization) where nodes represent domain concepts and claims, and edges represent typed relationships with confidence scores and provenance.
Primary implementation: neurooracle/ in the NeuroClaw project.
Architecture
┌─────────────────────┐
│ Knowledge Graph │
│ (NetworkX DiGraph) │
└──────┬──────────────┘
│
┌─────────────────┼─────────────────┐
│ │ │
┌────────▼────────┐ ┌─────▼──────────┐ ┌────▼─────────────┐
│ Phase 1: │ │ Phase 2: │ │ Phase 3: │
│ Structured │ │ Literature │ │ Hypothesis │
│ Data Ingestion │ │ Claim Extract │ │ Engine │
└────────┬────────┘ └──────┬─────────┘ └────┬─────────────┘
│ │ │
┌────────▼────────┐ ┌──────▼─────────┐ ┌────▼─────────────┐
│ - NeuroNames │ │ - PubMed search│ │ - Path finding │
│ - MeSH │ │ - LLM extract │ │ - Bridge discover│
│ - DisGeNET │ │ - Entity resol │ │ - Contradictions │
│ - Cognitive Atl │ │ - Claim ingest │ │ - Gap detection │
│ - Nilearn atlas │ │ │ │ - Ranking │
└─────────────────┘ └────────────────┘ └──────────────────┘
What ships with it
6 files 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 · 367 lines · 116 tokens per session scan A 51f671f826e1
knowledge-graph-builder is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (85 stars, last pushed 6d ago), licensed MIT. It adds 116 tokens to every session and 3,973 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 2026-09-03.
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