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 uniprot-querygit 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/uniprot-query)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/uniprot-query"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/uniprot-query/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/uniprot-query"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/uniprot-query.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 | $0.00071 | $0.01355 |
| Opus 5 | $0.00036 | $0.00678 |
| Sonnet 5 | $0.00014 | $0.00271 |
| Haiku 4.5 | $0.00007 | $0.00136 |
Grade A, and why
uniprot-query 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(url) How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UniProt Query
Query the UniProt knowledgebase for comprehensive protein information.
When to Use
- Look up protein by UniProt accession (e.g., P00533 for EGFR)
- Search proteins by gene name, organism, or keywords
- Retrieve protein metadata: function, domains, diseases, PTMs
- Get protein sequences and structural annotations
Workflow
Use Case 1: Protein Lookup by ID
Fetch complete protein information including metadata.
from open_biomed.tools.tool_registry import TOOLS
import requests
import json
# Get protein sequence (existing tool)
tool = TOOLS["protein_uniprot_request"]
proteins, _ = tool.run(accession="P0DTC2") # SARS-CoV-2 Spike
protein = proteins[0]
# Fetch full metadata from UniProt API
url = f"https://rest.uniprot.org/uniprotkb/P0DTC2?format=json"
response = requests.get(url)
metadata = parse_uniprot_entry(response.json())
See examples/lookup_by_id.py for complete implementation.
Use Case 2: Search by Criteria
Search UniProt by gene name, organism, keywords, or disease.
import requests
base_url = "https://rest.uniprot.org/uniprotkb/search"
# Example queries:
queries = {
"gene_exact:EGFR AND organism_id:9606": "Human EGFR",
"gene_exact:S AND organism_id:2697049": "SARS-CoV-2 Spike",
"keyword:Kinase AND organism_id:9606": "Human kinases",
"diabetes AND organism_id:9606": "Diabetes-related proteins",
}
params = {
"query": "gene_exact:EGFR AND organism_id:9606 AND reviewed:true",
"fields": "accession,gene_primary,protein_name,organism_name,length",
"format": "json",
"size": 10
}
response = requests.get(base_url, params=params)
See examples/search_by_criteria.py for complete implementation.
Query Syntax Reference
| Field | Example | Description |
|---|---|---|
gene_exact |
gene_exact:EGFR |
Exact gene name match |
gene |
gene:BRCA |
Gene name (partial match) |
organism_id |
organism_id:9606 |
Organism by TaxID |
organism |
organism:"Homo sapiens" |
Organism by name |
protein_name |
protein_name:kinase |
Protein name search |
keyword |
keyword:Kinase |
UniProt keyword |
cc_disease |
cc_disease:diabetes |
Disease association |
reviewed |
reviewed:true |
Swiss-Prot only (curated) |
What ships with it
4 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.
- 11d ago First seen · 169 lines · 71 tokens per session scan A 7c6f915bb2a9
uniprot-query is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 71 tokens to every session and 1,355 once invoked, about $0.0004 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.
Other skills, from other repositories
uniprot_lookup
Query UniProt database for protein information including function, organism, sequence, and structure. Use this skill when: (1) User provides a UniProt accession (e.g., P38398, Q9H3P7), (2) User asks about a protein by name (e.g., "BRCA1", "hemoglobin"), (3) User wants protein function, organism, or sequence…
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…
Protein Structures — AlphaFold & PDB
Obtain and predict protein 3D structures — fetch AlphaFold predicted models from the AlphaFold DB, experimental structures from the RCSB PDB, or predict a novel sequence with ColabFold — and visualise them in the Mol desktop app.
sdrf-annotate
Use when the user wants to create or annotate an SDRF file for a proteomics dataset. Also use to plan what metadata to capture and discuss experimental design/strategy before creating the file. Triggers on PXD accessions, requests to create SDRF, planning/strategy questions, or annotation tasks.
sdrf-knowledge
Use when the user asks about the SDRF format, column naming rules, ontology mappings, modification format, reserved words, label types, or any SDRF specification question, wants a plain-language explanation of a column/error/concept, or needs to find/verify/compare ontology terms and accessions for a column. Also…
sdrf-metascreen
Use when the user needs to screen or shortlist proteomics studies from PRIDE, MassIVE, or ProteomeXchange accessions, or from a manifest, using detailed user-defined inclusion/exclusion criteria — run this before sdrf:autoresearch; extract study-level metadata from repository records and publications; and write an…