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 naity/FM4Life --skill diffdockgit clone --depth 1 https://github.com/naity/FM4LifeWrote 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/naity/fm4life/diffdock)<a href="https://agentmods.dev/skills/naity/fm4life/diffdock"><img src="https://agentmods.dev/badge/skills/naity/fm4life/diffdock/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/naity/fm4life/diffdock"><img src="https://agentmods.dev/badge/skills/naity/fm4life/diffdock.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 | $0.00095 | $0.01740 |
| Opus 5.5 | $0.00038 | $0.00696 |
| Sonnet 5.5 | $0.00019 | $0.00348 |
| Haiku 4.5 | $0.00010 | $0.00174 |
Grade A, and why
diffdock 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 — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DiffDock: Diffusion-Based Molecular Docking
Overview
DiffDock predicts 3D protein-ligand binding poses using a diffusion model. Given a protein structure and a ligand SMILES string, it generates multiple candidate poses ranked by a confidence score.
Key properties:
- Blind docking — no binding site specification required; searches entire protein surface
- Diffusion model — generates diverse poses via iterative denoising (score-based diffusion)
- SE(3)-equivariant — architecture respects 3D symmetry (rotations, translations)
- Confidence model — separate network ranks poses by predicted accuracy (not binding affinity)
- Current default: DiffDock-L (ICLR 2024) — improved generalization over original (ICLR 2023)
Important distinction: The confidence score predicts pose quality (RMSD to true binding pose), not binding affinity. High confidence ≠ strong binder.
Installation
DiffDock requires a conda environment — no pip-only install available:
git clone https://github.com/gcorso/DiffDock.git
cd DiffDock
conda env create --file environment.yml
conda activate diffdock
Or with Docker (includes GPU support):
docker pull rbgcsail/diffdock
docker run -it --gpus all --entrypoint /bin/bash rbgcsail/diffdock
micromamba activate diffdock
Key dependencies: PyTorch 1.13 + CUDA 11.7, torch-geometric 2.2, e3nn 0.5.1, RDKit, fair-esm (ESM-2 for protein embeddings), pytorch-lightning.
First run: precomputes SO(2)/SO(3) distribution caches (~2 min). Not repeated.
Core Usage
Single complex
cd DiffDock
python -m inference \
--config default_inference_args.yaml \
--protein_path protein.pdb \
--ligand_description "COc(cc1)ccc1C#N" \
--out_dir results/my_docking
From protein sequence (ESMFold folds it automatically)
python -m inference \
--config default_inference_args.yaml \
--protein_sequence "MVHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH" \
--ligand_description "CC(=O)Nc1ccc(O)cc1" \
--out_dir results/my_docking
What ships with it
2 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.
- 1mo ago First seen · 171 lines · 95 tokens per session scan A 487c0d72109c
diffdock is a skill published in the GitHub repository naity/FM4Life (2 stars, last pushed 6mo ago), licensed MIT. It adds 95 tokens to every session and 1,740 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 2026-08-31.
Other skills, from other repositories
statistical-modeling
Statistical modeling and machine learning for biomarker discovery, survival analysis, classification, regression, and model interpretation.
genomic-variants
Analysis of called genomic variants — filtering, annotation, GWAS, and population-genetics summaries from VCF and PLINK-format data.
chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.
bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
drug-repurposing
Systematic drug repurposing via signature matching, target-based analysis, network proximity, genetic evidence scoring, and clinical evidence mining.