Scientific Agent Skills is a collection of reusable procedures that give AI agents capabilities for scientific research across areas such as biology, chemistry, medicine, and drug discovery. It is used by researchers and by people building AI scientist workflows with compatible coding agents. The catalogue contains many of the project's skills and supporting instructions.
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 K-Dense-AI/scientific-agent-skills --skill torch-geometricgit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWrote 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/k-dense-ai/scientific-agent-skills/torch-geometric)<a href="https://agentmods.dev/skills/k-dense-ai/scientific-agent-skills/torch-geometric"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/torch-geometric/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/k-dense-ai/scientific-agent-skills/torch-geometric"><img src="https://agentmods.dev/badge/skills/k-dense-ai/scientific-agent-skills/torch-geometric.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 3 findings, 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 Data Exfiltration · line 5 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 27 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 33 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00071 | $0.05610 |
| Opus 5.5 | $0.00028 | $0.02244 |
| Sonnet 5.5 | $0.00014 | $0.01122 |
| Haiku 4.5 | $0.00007 | $0.00561 |
Grade A, and why
torch-geometric 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 25d 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.
Copies of this mod
5 near-identical copies found in the catalogue:
- torch-geometric — 100% identical, 124 lines differ
- torch-geometric — 95% identical, 0 lines differ
- torch-geometric — 95% identical, 143 lines differ
- torch-geometric — 95% identical, 143 lines differ
- torch-geometric — 95% identical, 141 lines differ
How it starts
The opening of the file, as written. The whole thing — 488 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyTorch Geometric (PyG)
PyG is the standard library for Graph Neural Networks built on PyTorch. It provides data structures for graphs, 60+ GNN layer implementations, scalable mini-batch training, and support for heterogeneous graphs.
Installation
Reviewed released torch-geometric 2.8.0.post1 (2026-10-01); CPU examples tested with Python 3.13 / PyTorch 2.14.1. Rolling latest docs identify 2.9.0; check the installed release before adopting new APIs. PyG 2.8 requires PyTorch 2.9+; its original release table covers 2.9–2.12. Our 2.14.1 core tests do not establish every extension/backend combination.
# Install the PyTorch build for your platform from https://pytorch.org/get-started/locally/
uv pip install torch==2.14.1
uv pip install torch-geometric==2.8.0.post1
python -c "import torch, torch_geometric; print(torch.__version__, torch.version.cuda, torch_geometric.__version__)"
Basic tensor-based layers need no extensions. Neighbor sampling requires pyg-lib or torch-sparse; spatial k-NN operators require pyg-lib in 2.8. torch-cluster and torch-spline-conv are deprecated and ignored. Inspect the wheel index for your exact Python/OS/Torch/CUDA tuple. Never install wheels for a different Torch release merely because core imports succeed. The tested macOS ARM CPU extension is below; choose a different matching wheel for other platforms, and verify its operators:
uv pip install --only-binary=:all: "pyg-lib==0.9.0+pt214" \
-f https://data.pyg.org/whl/torch-2.14.0+cpu.html
Conda packages are no longer provided for Torch >2.5. See installation and 2.8 release changes. Optional sampling/GPU/distributed/download examples below are illustrative unless covered by the CPU checks in review notes.
Core Concepts
Graph Data: Data and HeteroData
What ships with it
7 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.
- 25d ago Changed · +10 lines 89805e17cdbc
- 26d ago Changed · +2 lines 0883ce8db717
- 1mo ago First seen · 476 lines · 71 tokens per session scan A 3b6dd242f763
torch-geometric is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (54,777 stars, last pushed yesterday), licensed MIT. It adds 71 tokens to every session and 5,610 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
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discovery-director
Operate as a research director making original discoveries from a given biological question and dataset. Use when the task is open-ended scientific research, exploring omics or experimental data for findings, hypothesis generation and testing, screening a large candidate space of genes, variants, features or…
bulk-rnaseq-counts-to-de-deseq2
Run differential expression analysis on bulk RNA-seq count data with DESeq2 (R). Covers DESeqDataSet construction from a count matrix, tximport (Salmon/Kallisto), featureCounts, or SummarizedExperiment; pre-filtering; design formulas (simple, batch, paired, interaction, multi-factor, LRT); result extraction by…
seurat-skill
Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial…