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
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 skills add PharMolix/OpenBioMed --skill binding-affinity-prediction-prodigygit clone --depth 1 https://github.com/PharMolix/OpenBioMedWrote 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/binding-affinity-prediction-prodigy)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/binding-affinity-prediction-prodigy"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/binding-affinity-prediction-prodigy/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/binding-affinity-prediction-prodigy"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/binding-affinity-prediction-prodigy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00431 |
| Opus 5 | $0.00019 | $0.00216 |
| Sonnet 5 | $0.00008 | $0.00086 |
| Haiku 4.5 | $0.00004 | $0.00043 |
Grade A, and why
binding-affinity-prediction-prodigy scanned grade A with 1 finding 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 10d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
output = subprocess.run(command, capture_output=True, text=True).stdout.split("##########################################")[1] What it actually says
Prodigy Binding Affinity Prediction for Protein Complex
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.10 |
| RAM | 32GB | 64GB |
How to run
Local installation
pip install prodigy-prot
Predict binding affinity of a protein complex structure
import os
def pred_binding_affinity(complex_pdb_file, distance_cutoff=5.5):
"""
:param complex_pdb_file: path to the protein complex structure file
:param distance_cutoff: distance cutoff to calculate ICs
:return binding affinity score for protein complex
"""
command = [
'prodigy', pdb_file,
'--distance-cutoff', distance_cutoff
]
try:
output = subprocess.run(command, capture_output=True, text=True).stdout.split("##########################################")[1]
score = output.split("[++] Predicted binding affinity (kcal.mol-1):")[1].split("\n")[0]
except:
output = "No contacts found for selection"
score = "0.0"
return float(score.strip())
# Predict binding affinity score for protein complex (PDB file)
binding_affinity_score = pred_binding_affinity(complex_pdb_file)
Decision tree
Should I use Prodigy?
│
└─ What type of complex are evaluated?
├─ Protein complex → binding-affinity-prediction-prodigy ✓
└─ Protein-ligand complex → structure-prediction-boltz-2
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.
- 10d ago First seen · 67 lines · 38 tokens per session scan A 95ac4c145d35
binding-affinity-prediction-prodigy is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 38 tokens to every session and 431 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
binding-affinity
Empirical affinity estimates, ligand energy inspection, docking-score consensus, and batch virtual screening. Full MM/GBSA requires a validated external workflow.
rfdiffusion
Generate protein backbones using RFdiffusion, a diffusion-based generative model for de novo protein structure generation. Use this skill when: (1) Designing binder scaffolds for a target protein, (2) Generating novel protein backbones from scratch, (3) Scaffolding functional motifs into new proteins, (4) Specifying…
boltzgen
All-atom protein design using BoltzGen diffusion model. Use this skill when: (1) Need side-chain aware design from the start, (2) Designing around small molecules or ligands, (3) Want all-atom diffusion (not just backbone), (4) Require precise binding geometries, (5) Using YAML-based configuration. For backbone-only…
esm
ESM protein language models for embeddings, sequence scoring, structure prediction, and binder design. Use this skill when: (1) Computing pseudo-log-likelihood (PLL) or mutation-effect scores, (2) Getting protein embeddings for clustering or filtering, (3) Predicting complex structures with ESMFold2, (4) Designing…
proteinmpnn
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design.…
alphafold
Validate protein designs using AlphaFold2 structure prediction. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction…