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/learningmatter-mit/atomisticskills/drug-docking-vinanpx skills add learningmatter-mit/AtomisticSkills --skill drug-docking-vinagit clone --depth 1 https://github.com/learningmatter-mit/AtomisticSkillsWrote 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/learningmatter-mit/atomisticskills/drug-docking-vina)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-docking-vina"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-docking-vina.svg" alt="Measured on agentmods" 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 | $0.00038 | $0.02510 |
| Opus 5 | $0.00019 | $0.01255 |
| Sonnet 5 | $0.00008 | $0.00502 |
| Haiku 4.5 | $0.00004 | $0.00251 |
Grade A, and why
drug-docking-vina 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 5d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
docking-vina
Goal
To perform molecular docking of one or more small-molecule ligands into a protein receptor using AutoDock Vina (>= 1.2.x) via its Python API, producing:
- Ranked binding poses (PDBQT)
- Docking scores (kcal/mol) and pose RMSDs
- A machine-readable JSON report with full docking parameters for reproducibility
This skill is intended for pose generation and relative ranking, not rigorous binding free energy prediction. Please refer to the original Vina method (Trott & Olson, https://doi.org/10.1002/jcc.21334) and the AutoDock Vina repo (https://github.com/ccsb-scripps/AutoDock-Vina) for more details.
Instructions
1. Prepare receptor and ligand (recommended)
Docking accuracy is strongly affected by structure preparation (protonation, missing residues, cofactors, waters, tautomer states, etc.). Use:
- protein-prep to generate
*_prepared.pdbqt - ligand-prep to generate ligand
*.pdbqt(consider multiple protomers/tautomers)
# Env: drugdisc-agent
python .agents/skills/drug-protein-prep/scripts/prepare_protein.py \
--pdb_id 1HSG \
--heterogens none \
--missing_residues ignore \
--output_dir docking/inputs/
# Env: drugdisc-agent
python .agents/skills/drug-ligand-prep/scripts/prepare_ligand.py \
--smiles "CC(=O)Oc1ccccc1C(=O)O" \
--name aspirin \
--output_dir docking/inputs/
Best practice: if you have a co-crystal ligand, keep it as a positive control for redocking validation.
2. Define the docking search box (center + size)
You must define the docking region. The most common approaches:
- Redocking / known pocket: center on the co-crystallized ligand
- Known active site residues: center on key catalytic residues
- Blind docking: large box spanning the protein (slower and less reliable, so use cautiously)
If you have a reference ligand already positioned in the binding site (PDBQT), compute a reasonable box automatically:
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.
- examples/hiv1_protease/inputs/1HSG_prepared.pdb 241 KB
- examples/hiv1_protease/inputs/1HSG_prepared.pdbqt 143 KB
- examples/hiv1_protease/inputs/indinavir/indinavir.pdbqt 5.2 KB
- examples/hiv1_protease/output/docking_results.json 2.3 KB
- examples/hiv1_protease/output/indinavir_docked.pdbqt 27 KB
- examples/hiv1_protease/README.md 2.1 KB
- scripts/collect_results.py 6.5 KB runs code
- scripts/compute_box_from_pdbqt.py 3.4 KB runs code
- scripts/run_docking.py 11 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.
- 5d ago First seen · 194 lines · 38 tokens per session scan A 4af1176e80e8
drug-docking-vina is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed 2d ago), licensed MIT. It adds 38 tokens to every session and 2,510 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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