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-binding-site-definitionnpx skills add learningmatter-mit/AtomisticSkills --skill drug-binding-site-definitiongit 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-binding-site-definition)<a href="https://agentmods.dev/skills/learningmatter-mit/atomisticskills/drug-binding-site-definition"><img src="https://agentmods.dev/badge/skills/learningmatter-mit/atomisticskills/drug-binding-site-definition.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 | $0.00101 | $0.02846 |
| Opus 5 | $0.00051 | $0.01423 |
| Sonnet 5 | $0.00020 | $0.00569 |
| Haiku 4.5 | $0.00010 | $0.00285 |
Grade A, and why
drug-binding-site-definition 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 6d 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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
drug-binding-site-definition
Goal
To produce a standardized docking / simulation box definition (center coordinates + box dimensions in Angstroms) that downstream skills such as drug-docking-vina and drug-complex-system-builder can consume directly.
Choosing the Right Mode
Use this decision tree to pick the appropriate approach:
Do you have a reference ligand positioned in the binding site?
|-- YES --> Mode A (co-crystal ligand)
|-- NO
Do you know the key binding-site residues (from literature, mutagenesis, etc.)?
|-- YES --> Mode B (residue list)
|-- NO
Do you have a closely related protein with a known binding site?
|-- YES --> Superimpose structures, transfer the ligand,
| then use Mode A on the transferred ligand.
| (See "When You Have No Binding-Site Information" below.)
|-- NO --> Run computational pocket prediction first
(see "When You Have No Binding-Site Information" below),
then feed results into Mode A or B.
If you already have a saved box JSON from a prior run, use Mode C to reload it.
Instructions
1. Mode A: Box from a co-crystal ligand (most common)
If you have a reference ligand already positioned in the binding site (PDB, SDF, MOL2, or PDBQT), compute the box automatically:
# Env: drugdisc-agent
python .agents/skills/drug-binding-site-definition/scripts/define_binding_site.py \
--mode ligand \
--ligand_file docking/inputs/reference_ligand.sdf \
--padding 6.0 \
--min_size 20.0 \
--output_json docking/inputs/binding_site.json
Key parameters:
--padding: buffer added to the ligand bounding box on each side (default 6.0 A). Use 4-5 A for tight/buried pockets, 8-10 A for shallow or allosteric sites.--min_size: minimum box edge length per axis (default 20.0 A).
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/1HSG_protein.pdb 117 KB
- examples/hiv1-protease/1HSG_raw.pdb 131 KB
- examples/hiv1-protease/binding_site_ligand.json 235 B
- examples/hiv1-protease/binding_site_residues.json 341 B
- examples/hiv1-protease/box_visualization.png 376 KB
- examples/hiv1-protease/MK1_ligand.pdb 1.7 KB
- examples/hiv1-protease/README.md 2.1 KB
- scripts/define_binding_site.py 13 KB runs code
- scripts/visualize_box.py 3.8 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.
- 6d ago First seen · 210 lines · 101 tokens per session scan A 61cf61e99df5
drug-binding-site-definition is a skill published in the GitHub repository learningmatter-mit/AtomisticSkills (160 stars, last pushed 2d ago), licensed MIT. It adds 101 tokens to every session and 2,846 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 2026-08-30.
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