OpenBioMed is an agent platform and toolkit collection for biomedical research and drug discovery, covering areas such as molecular design, protein analysis, and single-cell data analysis. It is intended for researchers and provides the biomedical skills listed in the catalogue as workflows for Claude Code.
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/PharMolix/OpenBioMednpx agentmods add skills/pharmolix/openbiomed/protein-ligand-binding-analysis-plipWrote 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/pharmolix/openbiomed/protein-ligand-binding-analysis-plip)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/protein-ligand-binding-analysis-plip"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/protein-ligand-binding-analysis-plip/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/pharmolix/openbiomed/protein-ligand-binding-analysis-plip"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/protein-ligand-binding-analysis-plip.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00102 | $0.00872 |
| Opus 5 | $0.00051 | $0.00436 |
| Sonnet 5 | $0.00020 | $0.00174 |
| Haiku 4.5 | $0.00010 | $0.00087 |
Grade A, and why
protein-ligand-binding-analysis-plip 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 12d 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein-Ligand Binding Analysis with PLIP
Analyze protein-ligand interactions in PDB structures, generate comprehensive interaction reports, and create 3D visualizations.
When to Use
- Analyzing binding modes from crystal structures or docking results
- Identifying key interactions driving binding affinity
- Comparing ligand binding patterns across multiple structures
- Generating publication-ready interaction visualizations
Workflow
Step 1: Load PDB and Identify Ligands
from plip.structure.preparation import PDBComplex
complex = PDBComplex()
complex.load_pdb(pdb_file)
# Filter ligands by molecular weight (exclude ions/cofactors, MW > 150 Da)
ligands = []
for lig in complex.ligands:
if lig.mol.molwt > 150: # OpenBabel molecule object
ligands.append(lig)
complex.characterize_complex(lig)
Step 2: Analyze Interactions
from plip.exchange.report import BindingSiteReport
complex.analyze()
for key, interactions in complex.interaction_sets.items():
report = BindingSiteReport(interactions)
report_lines = report.generate_txt()
# Parse interaction data from report_lines
Step 3: Generate Visualizations
from plip.basic.remote import VisualizerData
from plip.visualization.visualize import visualize_in_pymol
from plip.basic import config
config.PICS = True
config.OUTPATH = output_dir
config.BACKGROUND = "white"
config.CARTOON = True
for key in complex.interaction_sets:
data = VisualizerData(complex, key)
visualize_in_pymol(data)
Expected Outputs
| Output | Description |
|---|---|
| Interaction Report | Markdown summary of all interaction types per ligand |
| Visualization Images | PNG files showing 3D interaction diagrams |
| Summary Statistics | Counts of H-bonds, hydrophobic, π-stacking, etc. |
Interaction Types Reported
| Type | Description |
|---|---|
| Hydrogen bonds | H-bonds with ligand/protein as donor |
| Hydrophobic contacts | Non-polar interactions |
| Water bridges | Water-mediated interactions |
| π-stacking | Aromatic ring interactions |
| Salt bridges | Ionic interactions |
| Halogen bonds | Halogen-mediated contacts |
| Metal complexes | Metal coordination |
What ships with it
3 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.
- 12d ago First seen · 123 lines · 102 tokens per session scan A a6cc8ef4bc42
protein-ligand-binding-analysis-plip is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 872 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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Drive the user's Atrium desktop: open files in installed viewer apps (Viv bioimages, Vitessce/Spatial 3D omics, Mol structures, IGV/Gosling genomics, Volume 3D, Cytoscape, MSA, PhyloTree, RDKit), read and steer ANY window — including ones the user opened — and call app backends.
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fin-paper-figure
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