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 iupac-name-identification-biot5git 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/iupac-name-identification-biot5)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/iupac-name-identification-biot5"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/iupac-name-identification-biot5/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/iupac-name-identification-biot5"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/iupac-name-identification-biot5.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 | $0.00086 | $0.00777 |
| Opus 5 | $0.00043 | $0.00388 |
| Sonnet 5 | $0.00017 | $0.00155 |
| Haiku 4.5 | $0.00009 | $0.00078 |
Grade A, and why
iupac-name-identification-biot5 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 11d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IUPAC Name Identification (BioT5)
This skill identifies the IUPAC name of a molecule using the BioT5 question answering model.
When to Use
- User asks for the IUPAC name of a molecule
- User provides a SMILES string and wants systematic nomenclature
- User asks "What is the IUPAC name?" or "What's the systematic name?"
Workflow
Step 1: Get the Molecule
If user provides a molecule name (e.g., "aspirin"):
from open_biomed.tools.tool_registry import TOOLS
tool = TOOLS["molecule_name_request"]
result, message = tool.run(accession="aspirin")
molecule = result[0] # Returns a list of molecules
If user provides a SMILES string:
from open_biomed.data import Molecule
molecule = Molecule.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")
Step 2: Ask for IUPAC Name
Use the molecule question answering tool:
from open_biomed.data import Text
from open_biomed.tools.tool_registry import TOOLS
qa_tool = TOOLS["molecule_question_answering"]
question = Text.from_str("What's the IUPAC name of this molecule?")
result, message = qa_tool.run(molecule=molecule, text=question)
print(result) # IUPAC name
Expected Outputs
| Input | Output | Description |
|---|---|---|
| SMILES or molecule name | IUPAC name string | Systematic chemical nomenclature |
Example Usage
Input: "What is the IUPAC name of aspirin?"
Workflow:
- Retrieve aspirin molecule from PubChem
- Ask BioT5: "What's the IUPAC name of this molecule?"
- Return the IUPAC name
Expected output: "2-acetyloxybenzoic acid" or similar systematic name
Model Options
The molecule_question_answering tool supports multiple models:
| Model | Description |
|---|---|
biot5 (default) |
BioT5 model for biomedical QA |
molt5 |
MolT5 model specialized for molecules |
Error Handling
Molecule Not Found
Symptom: PubChem request fails for molecule name.
Solution: Ask user for SMILES string directly.
What ships with it
1 file 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.
- 11d ago First seen · 108 lines · 86 tokens per session scan A 29f473d86219
iupac-name-identification-biot5 is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 86 tokens to every session and 777 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
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
deepchem
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first…
rdkit
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom…
binding-affinity
Empirical affinity estimates, ligand energy inspection, docking-score consensus, and batch virtual screening. Full MM/GBSA requires a validated external workflow.
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
molecular-optimization
Iterative lead optimization with analyze-reason-generate-verify-evaluate loop. Paper-backed (MT-Mol, DrugR, MultiMol).