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/synthetic-sciences/openscience/molecular-dockingnpx skills add synthetic-sciences/openscience --skill molecular-dockinggit clone --depth 1 https://github.com/synthetic-sciences/openscienceWhat 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 | $0.00038 | $0.02341 |
| Opus 5 | $0.00019 | $0.01171 |
| Sonnet 5 | $0.00008 | $0.00468 |
| Haiku 4.5 | $0.00004 | $0.00234 |
Grade A, and why
molecular-docking 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 3d 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 — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Molecular Docking Pipeline
Overview
This skill provides a complete end-to-end molecular docking workflow covering every stage from raw protein structure to ranked, annotated binding poses. It integrates classical physics-based docking (AutoDock Vina) with modern deep-learning approaches (DiffDock), and includes protein-ligand interaction fingerprinting for downstream analysis.
Pipeline Stages:
- Target Preparation -- Clean PDB structures, remove waters, add hydrogens, detect binding pockets
- Ligand Preparation -- Convert SMILES to 3D, generate conformers, assign charges
- Docking -- Run Vina or DiffDock to generate binding poses
- Scoring & Interaction Analysis -- Identify hydrogen bonds, hydrophobic contacts, pi-stacking, salt bridges
- Ranking -- Combine docking scores with interaction quality into a composite ranking
When to Use This Skill
Use this skill when the user requests any of the following:
- "Dock this ligand to a protein" or "predict how a molecule binds"
- "Prepare a protein for docking" or "clean this PDB file"
- "Find binding pockets" or "detect active sites"
- "Run virtual screening against a compound library"
- "Score docked poses" or "analyze protein-ligand interactions"
- "Rank docking results" or "find the best binders"
- Any structure-based drug design task involving PDB files and small molecules
- Lead optimization where binding pose context is needed
Do NOT use this skill for:
- Binding affinity prediction (use MM/GBSA or free energy perturbation tools)
- Protein-protein docking (use HDOCK or ClusPro)
- Covalent docking (requires specialized workflows)
- Homology modeling (use AlphaFold or ESMFold first, then dock)
Related Skills
- diffdock: For DiffDock-specific deep learning docking with all configuration options. This pipeline skill already calls DiffDock internally.
- denovo-design: For generating novel molecules to dock. Combine with this skill for a complete design-dock workflow.
- admet-prediction: For filtering docking hits by ADMET properties before experimental testing.
What ships with it
6 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.
- 3d ago First seen · 258 lines · 38 tokens per session scan A 42a847272afe
molecular-docking is a skill published in the GitHub repository synthetic-sciences/openscience (3,385 stars, last pushed yesterday), licensed Apache-2.0. It adds 38 tokens to every session and 2,341 once invoked, about $0.0002 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 2026-08-30.
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