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 dralkh/iktinah --skill torchdruggit clone --depth 1 https://github.com/dralkh/iktinahWrote 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/dralkh/iktinah/torchdrug)<a href="https://agentmods.dev/skills/dralkh/iktinah/torchdrug"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/torchdrug/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/dralkh/iktinah/torchdrug"><img src="https://agentmods.dev/badge/skills/dralkh/iktinah/torchdrug.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 | $0.00067 | $0.03462 |
| Opus 5 | $0.00034 | $0.01731 |
| Sonnet 5 | $0.00013 | $0.00692 |
| Haiku 4.5 | $0.00007 | $0.00346 |
Grade A, and why
torchdrug 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 7d 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.
This is a copy
88% identical to torchdrug — 24 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TorchDrug
Overview
TorchDrug is a comprehensive PyTorch-based machine learning toolbox for drug discovery and molecular science. Apply graph neural networks, pre-trained models, and task definitions to molecules, proteins, and biological knowledge graphs, including molecular property prediction, protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis planning, with 40+ curated datasets and 20+ model architectures.
When to Use This Skill
This skill should be used when working with:
Data Types:
- SMILES strings or molecular structures
- Protein sequences or 3D structures (PDB files)
- Chemical reactions and retrosynthesis
- Biomedical knowledge graphs
- Drug discovery datasets
Tasks:
- Predicting molecular properties (solubility, toxicity, activity)
- Protein function or structure prediction
- Drug-target binding prediction
- Generating new molecular structures
- Planning chemical synthesis routes
- Link prediction in biomedical knowledge bases
- Training graph neural networks on scientific data
Libraries and Integration:
- TorchDrug is the primary library
- Often used with RDKit for cheminformatics
- Compatible with PyTorch and PyTorch Lightning
- Integrates with AlphaFold and ESM for proteins
Getting Started
Installation
TorchDrug 0.2.1 (latest on PyPI, July 2023) requires Python 3.7–3.10 and PyTorch 1.8–2.0. Install PyTorch and torch-scatter / torch-cluster first (wheel URL depends on your PyTorch and CUDA versions — see installation docs).
uv pip install torch
# Match torch/CUDA in the URL, e.g. torch-2.0.0+cu118 or cpu
uv pip install torch-scatter torch-cluster -f https://pytorch-geometric.com/whl/torch-2.0.0+cu118.html
uv pip install torchdrug==0.2.1
On Apple Silicon, compile scatter/cluster from source; TorchDrug runs on CPU only (no MPS). Conda: conda install torchdrug -c milagraph -c conda-forge -c pytorch -c pyg.
Quick Example
What ships with it
8 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.
- references/core_concepts.md 15 KB
- references/datasets.md 11 KB
- references/knowledge_graphs.md 8.3 KB
- references/models_architectures.md 13 KB
- references/molecular_generation.md 9.8 KB
- references/molecular_property_prediction.md 5.7 KB
- references/protein_modeling.md 7.6 KB
- references/retrosynthesis.md 11 KB
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.
- 7d ago First seen · 460 lines · 67 tokens per session scan A f258d624bfcc
torchdrug is a skill published in the GitHub repository dralkh/iktinah (77 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 3,462 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to torchdrug, differing in 24 lines, and is treated as a copy.
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