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/kdevos12/alkyl/py3dmolnpx skills add Kdevos12/ALKYL --skill py3dmolgit clone --depth 1 https://github.com/Kdevos12/ALKYLWhat 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 | $0.00068 | $0.00625 |
| Opus 5 | $0.00034 | $0.00313 |
| Sonnet 5 | $0.00014 | $0.00125 |
| Haiku 4.5 | $0.00007 | $0.00063 |
Grade A, and why
py3Dmol 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
py3Dmol — Molecular Visualization
Purpose
Interactive 3D molecular visualization in Jupyter notebooks and scripts. Wraps 3Dmol.js (WebGL). Used for docking pose inspection, protein-ligand complexes, conformer overlays, trajectory snapshots.
When to Use This Skill
- Visualizing docking poses from Vina/Gnina
- Inspecting protein-ligand binding pockets
- Displaying conformer ensembles
- Annotating pharmacophore features on 3D structures
- Quick structure QC after homology modeling or MD prep
Reference Files
| File | Content |
|---|---|
references/basics.md |
Installation, view creation, loading PDB/SDF/SMILES, stick/sphere/cartoon/surface basics |
references/protein-ligand.md |
Protein+ligand display, binding pocket zoom, dual-structure overlay, docking pose batch |
references/selections-styles.md |
Selection language (chain/resi/resn/atom), color schemes, surfaces, labels, transparency |
references/jupyter-patterns.md |
Jupyter embed, ipywidgets sliders, NGLview alternative, saving PNG, RDKit interop |
Quick Routing
"Show me a docking pose" → protein-ligand.md
"Show all conformers overlaid" → jupyter-patterns.md (animation loop)
"Highlight binding pocket / surface" → selections-styles.md
"I just need a quick look at a molecule" → basics.md
Minimal Pattern
import py3Dmol
view = py3Dmol.view(width=800, height=500)
view.addModel(open('complex.pdb').read(), 'pdb')
view.setStyle({'cartoon': {'color': 'spectrum'}}) # protein
view.setStyle({'resn': 'LIG'}, {'stick': {'colorscheme': 'greenCarbon'}})
view.zoomTo({'resn': 'LIG'})
view.show()
Key Facts
py3Dmolrenders via 3Dmol.js in Jupyter — requires a running notebook kernel- For non-Jupyter contexts: use
view.png()→ base64 PNG, orview.write_html() - NGLview is an alternative with better trajectory support (use for MD)
setStyleis cumulative by default; usesetStyle({}, {})to reset all- Ligand residue name varies: 'LIG', 'UNL', 'MOL' — check with grep before scripting
What ships with it
4 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 · 58 lines · 68 tokens per session scan A 81c0938be10c
py3Dmol is a skill published in the GitHub repository Kdevos12/ALKYL (6 stars, last pushed 5mo ago), licensed MIT. It adds 68 tokens to every session and 625 once invoked, about $0.0003 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.
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