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 kg-link-predictiongit 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/kg-link-prediction)<a href="https://agentmods.dev/skills/cuhk-aim-group/neuroclaw/kg-link-prediction"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/kg-link-prediction/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/kg-link-prediction"><img src="https://agentmods.dev/badge/skills/cuhk-aim-group/neuroclaw/kg-link-prediction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00956 |
| Opus 5 | $0.00058 | $0.00478 |
| Sonnet 5 | $0.00023 | $0.00191 |
| Haiku 4.5 | $0.00012 | $0.00096 |
Grade A, and why
kg-link-prediction 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 12d 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 — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KG Link Prediction Skill
Overview
kg-link-prediction trains embeddings or graph neural encoders on NeuroOracle
triples and scores candidate relations.
Supported models
| Model | Encoder | Decoder/evaluation |
|---|---|---|
complex |
complex-valued entity/relation embeddings | ComplEx score |
rgcn |
relation-specific graph convolution | relation-aware DistMult |
graphsage |
neighborhood aggregation | relation-aware DistMult |
gat |
graph attention | relation-aware DistMult |
GNN message passing uses training edges only. Validation and test edges are excluded from encoder adjacency, and negative samples are checked against all known positive triples.
Installation
pip install numpy torch scikit-learn
The implementation uses native PyTorch operations and does not require PyTorch Geometric.
Workflows
1. Prepare a NeuroOracle graph
Input is a NeuroOracle knowledge_graph.json containing typed nodes and
confidence-bearing edges. Low-confidence edges can be excluded with
--min-confidence.
2. Train R-GCN
python skills/kg-link-prediction/scripts/train_reference.py \
--kg neurooracle/data/full_v2/knowledge_graph.json \
--model rgcn \
--embedding-dim 128 \
--layers 2 \
--dropout 0.1 \
--epochs 100 \
--negatives 10 \
--min-confidence 0.2 \
--device cuda \
--output-dir run_models_output/kg_rgcn
3. Train GraphSAGE or GAT
Change --model to graphsage or gat. Keep the same split seed when
comparing encoders.
4. Train the existing ComplEx route
python skills/kg-link-prediction/scripts/train_reference.py \
--kg neurooracle/data/full_v2/knowledge_graph.json \
--model complex \
--embedding-dim 128 \
--epochs 100 \
--output-dir run_models_output/kg_complex
Retrain the model whenever the graph snapshot changes materially. Record the graph hash and freeze year when the embeddings are used for hindcasting.
Input / Output Summary
| Item | Format |
|---|---|
| Input | NeuroOracle knowledge graph JSON |
| Filtering | edge confidence threshold |
| Split | train/validation/test triples |
| Checkpoint | checkpoint.pt |
| Metrics | metrics.json |
| Provenance | config.json, run_manifest.json with split counts |
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.
- 12d ago First seen · 144 lines · 116 tokens per session scan A 8984a8bd6535
kg-link-prediction is a skill published in the GitHub repository CUHK-AIM-Group/NeuroClaw (84 stars, last pushed 5d ago), licensed MIT. It adds 116 tokens to every session and 956 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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