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 functional-protein-designgit 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/functional-protein-design)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/functional-protein-design"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/functional-protein-design/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/functional-protein-design"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/functional-protein-design.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 | $0.00006 | $0.02253 |
| Opus 5 | $0.00003 | $0.01126 |
| Sonnet 5 | $0.00001 | $0.00451 |
| Haiku 4.5 | $0.00001 | $0.00225 |
Grade A, and why
functional-protein-design 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -L -o iprscan5.py https://raw.githubusercontent.com/ebi-jdispatcher/webservice-clients/master/python/iprscan5.py How it starts
The opening of the file, as written. The whole thing — 254 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Functional Protein Design
Prerequisites
| Requirement | Details |
|---|---|
| Environment | Configured via OpenBioMed: README.md |
| Hardware | CUDA-compatible GPU (≥ 10GB VRAM) required for both generation and folding |
| Checkpoints | Download CodeFP weights & mappings from Google Drive. |
Data Preparation & Configuration
Directory Structure Organize your downloaded checkpoints and mapping files as follows:
checkpoints/
├── codefp/
│ ├── model/
│ │ └── checkpoints/
│ │ └── model.ckpt
│ └── mappings/
│ ├── go_mapping.pkl
│ ├── go_id_mapping.pkl
│ ├── desc2map_dict_statics.pkl
│ └── train_go_terms_cls_emb.pkl
How to Run
Phase 0: Environment & Prerequisites
Before getting started, ensure that your environment is fully configured. This includes a successful installation of OpenBioMed and the completion of all required model weight downloads.
Next, search the Gene Ontology website to identify 1–3 Molecular Function (MF) GO terms (e.g., ['GO:0004930', 'GO:0004984']) that best align with your functional target.
Note: Please ensure that the selected GO terms are included in go_mapping.pkl, a dictionary whose keys enumerate all supported GO terms (e.g., “GO:0004930”, “GO:0004984”), to ensure compatibility with the model.
Phase 1: GO-Guided Sequence Generation (Python)
First, we generate the protein sequence using the model. Run the following code:
from open_biomed.core.pipeline import InferencePipeline
from open_biomed.data import Protein
# 1. GO-guided sequence generation
generator = InferencePipeline(
task="go_guided_protein_generation",
model="codefp",
model_ckpt="./checkpoints/codefp/model/checkpoints/model.ckpt",
device="cuda:0"
)
# Replace with 1–3 target Molecular Function (MF) GO terms
go_terms = [['GO:0004930', 'GO:0004984']]
designed_seqs = generator.run(go_terms=go_terms)
seq_only = designed_seqs[0][0] # Protein object
seq_str = seq_only.sequence
print(f"Generated Sequence: {seq_str}")
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.
- 13d ago First seen · 254 lines · 0 tokens per session scan A 1371356743a6
functional-protein-design is a skill published in the GitHub repository PharMolix/OpenBioMed (1,105 stars, last pushed 1mo ago), licensed MIT. It adds 6 tokens to every session and 2,253 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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