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 agentmods add skills/naity/fm4life/diffdocknpx skills add naity/FM4Life --skill diffdockgit clone --depth 1 https://github.com/naity/FM4LifeWhat 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 | $0.00095 | $0.01740 |
| Opus 5 | $0.00048 | $0.00870 |
| Sonnet 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 yesterday.
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.
- yesterday 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 4mo ago), licensed MIT. It adds 95 tokens to every session and 1,740 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
gsva-analysis-and-visualization
Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression…
batch-effect-correction
Use when correcting batch effects in merged bulk expression matrices with sample-level batch metadata while preserving biological group structure and generating before-and-after QC plots. NOT for: single-cell integration, raw FASTQ processing, differential expression without batch labels, or datasets without…
estimate-immune-score-analysis
Use this skill to compute ESTIMATE immune-related microenvironment scores from a bulk expression matrix, generate an ESTIMATE score heatmap, and optionally generate group-wise ESTIMATE score boxplots plus significance tables when a sample group file is supplied. Trigger keywords: ESTIMATE, immune score, stromal score…
hierarchical-clustering-plot
Use when building a sample-level hierarchical clustering dendrogram from a bulk expression matrix and sample annotation table, especially for QC, batch inspection, or sample similarity assessment. Trigger keywords: hierarchical clustering, dendrogram, sample QC, batch inspection, sample similarity. NOT for…
immune-pathway-analysis
Run immune pathway GSVA or ssGSEA analysis from a bulk expression matrix, a sample group file, and a local immune Reactome gene-set table, then export differential pathway results and a heatmap for two-group comparison.
consensus-clustering-analysis
Use when identifying stable sample subtypes from bulk expression matrices with ConsensusClusterPlus, including PAC-based model selection and consensus matrix/CDF visualization. NOT for: differential expression analysis, single-cell clustering workflows, or non-expression tables.