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 protein-function-predictiongit 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/protein-function-prediction)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/protein-function-prediction"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/protein-function-prediction.svg" alt="Measured on agentmods" 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 | $0.00073 | $0.00938 |
| Opus 5 | $0.00036 | $0.00469 |
| Sonnet 5 | $0.00015 | $0.00188 |
| Haiku 4.5 | $0.00007 | $0.00094 |
Grade A, and why
protein-function-prediction 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 8d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein Function Prediction
Predict functional annotations and properties for proteins from their amino acid sequences using the BioT5 model.
When to Use
- You have a protein FASTA sequence and need to understand its biological role
- You want to identify enzyme function, pathway involvement, or molecular mechanisms
- You need quick functional insights without experimental data
- You're characterizing novel or unannotated protein sequences
Workflow
from open_biomed.data import Protein, Text
from open_biomed.core.pipeline import InferencePipeline
# Create protein from FASTA sequence
protein = Protein.from_fasta("YOUR_AMINO_ACID_SEQUENCE")
# Create the question for functional annotation
question = Text.from_str(
"Inspect the protein sequence and offer a concise description of its properties."
)
# Load the BioT5 model for protein question answering
pipeline = InferencePipeline(
task="protein_question_answering",
model="biot5",
model_ckpt="./checkpoints/server/protein_question_answering_biot5.ckpt",
device="cuda:0"
)
# Run inference to get functional annotation
outputs = pipeline.run(protein=protein, text=question)
function_description = outputs[0][0].str
print(function_description)
See examples/basic_example.py for a complete runnable script.
Expected Outputs
The model returns a text description that typically includes:
| Output Component | Example |
|---|---|
| Enzyme name | Phosphoribosylformylglycinamidine synthase |
| Biological pathway | Purine biosynthesis pathway |
| Catalytic activity | FGAR to FGAM conversion |
| Complex membership | Part of FGAM synthase complex (PurQ, PurL, PurS) |
| Mechanism details | ATP-dependent, glutamine amidotransferase activity |
Example Output
Part of the phosphoribosylformylglycinamidine synthase complex involved in the purines biosynthetic pathway. Catalyzes the ATP-dependent conversion of formylglycinamide ribonucleotide (FGAR) and glutamine to yield formylglycinamidine ribonucleotide (FGAM) and glutamate.
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.
- 8d ago First seen · 111 lines · 73 tokens per session scan A 2bd2ce936d49
protein-function-prediction is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 938 once invoked, about $0.0004 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.
Other skills, from other repositories
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uniprot_lookup
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tao-generate-video-reasoning-annotations
Multi-step video annotation pipeline that turns raw videos into Chain-of-Thought training data — multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning traces, via VLM/LLM distillation. Use when the user wants to "create video training data", "generate video QA datasets"…