Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skillsnpx agentmods add skills/alterlab-ieu/alterlab-academic-skills/alterlab-diffdockWrote 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/alterlab-ieu/alterlab-academic-skills/alterlab-diffdock)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-diffdock"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-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/alterlab-ieu/alterlab-academic-skills/alterlab-diffdock"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-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 | $0.00077 | $0.04249 |
| Opus 5.5 | $0.00031 | $0.01700 |
| Sonnet 5.5 | $0.00015 | $0.00850 |
| Haiku 4.5 | $0.00008 | $0.00425 |
Grade A, and why
alterlab-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.
This is a copy
89% identical to diffdock — 586 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 — 500 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DiffDock: Molecular Docking with Diffusion Models
Overview
DiffDock is a diffusion-based deep learning tool for molecular docking that predicts 3D binding poses of small molecule ligands to protein targets. The repository's default model is DiffDock-L (Feb 2024); it is widely used for blind docking, though co-folding models (Boltz-2, Chai-1) and physics-based rescoring are now common complements, and predicted poses should be checked for physical validity (e.g. with PoseBusters).
Core Capabilities:
- Predict ligand binding poses with high accuracy using deep learning
- Support protein structures (PDB files) or sequences (via ESMFold)
- Process single complexes or batch virtual screening campaigns
- Generate confidence scores to assess prediction reliability
- Handle diverse ligand inputs (SMILES, SDF, MOL2)
Key Distinction: DiffDock predicts binding poses (3D structure) and confidence (prediction certainty), NOT binding affinity (ΔG, Kd). Always combine with scoring functions (GNINA, MM/GBSA) for affinity assessment.
When to Use This Skill
This skill should be used when:
- "Dock this ligand to a protein" or "predict binding pose"
- "Run molecular docking" or "perform protein-ligand docking"
- "Virtual screening" or "screen compound library"
- "Where does this molecule bind?" or "predict binding site"
- Structure-based drug design or lead optimization tasks
- Tasks involving PDB files + SMILES strings or ligand structures
- Batch docking of multiple protein-ligand pairs
Does NOT Trigger
| Scenario | Use Instead |
|---|---|
| Binding affinity / docking score on a cloud platform (AutoDock Vina) | alterlab-rowan |
| MD simulation of a complex, pose-stability / RMSD over a trajectory | alterlab-molecular-dynamics |
| Protein–protein or protein–nucleic-acid complex structure prediction | alterlab-boltz or alterlab-chai |
| Predicting the apo protein structure itself (no ligand) | alterlab-alphafold |
| Looking up measured protein–ligand affinities for the target | alterlab-bindingdb |
What ships with it
9 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.
- assets/batch_template.csv 410 B
- assets/custom_inference_config.yaml 2.8 KB
- evals/evals.json 5.1 KB
- references/confidence_and_limitations.md 6.8 KB
- references/parameters_reference.md 5.3 KB
- references/workflows_examples.md 11 KB
- scripts/analyze_results.py 10 KB runs code
- scripts/prepare_batch_csv.py 8.5 KB runs code
- scripts/setup_check.py 7.8 KB runs code
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 Changed · +13 lines fd19159e3b7a
- 1mo ago First seen · 487 lines · 77 tokens per session scan A 0ad3b6e710da
alterlab-diffdock is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (78 stars, last pushed yesterday), licensed MIT. It adds 77 tokens to every session and 4,249 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to diffdock, differing in 586 lines, and is treated as a copy.
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