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 agentmods add skills/pharmolix/openbiomed/ppi-string-querynpx skills add PharMolix/OpenBioMed --skill ppi-string-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/ppi-string-query)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/ppi-string-query"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/ppi-string-query.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00057 | $0.00970 |
| Opus 5 | $0.00028 | $0.00485 |
| Sonnet 5 | $0.00011 | $0.00194 |
| Haiku 4.5 | $0.00006 | $0.00097 |
Grade A, and why
ppi-string-query 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 7d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
STRING Protein-Protein Interaction Query
Query the STRING database to retrieve protein-protein interactions with comprehensive confidence scores.
When to Use
- Find interaction partners for a protein (by UniProt ID)
- Retrieve confidence scores for PPIs (experimental, text mining, database)
- Build protein interaction networks for pathway analysis
- Identify potential protein complexes or functional modules
Workflow
Basic Query
from open_biomed.tools.tool_registry import TOOLS
# Query STRING for interaction partners
tool = TOOLS["ppi_string_request"]
results, _ = tool.run(uniprot_id="P04637") # TP53
# Access results
for interaction in results:
print(f"{interaction['partner_gene']}: {interaction['combined_score']}")
Custom Parameters
# High confidence interactions only, limit to 20
results, _ = tool.run(
uniprot_id="P04637",
species=9606, # Human (default)
required_score=700, # High confidence (default)
limit=20 # Max interactors
)
Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
uniprot_id |
str | required | UniProt accession (e.g., P04637) |
species |
int | 9606 | NCBI taxonomy ID (9606=human) |
required_score |
int | 700 | Min confidence (150/400/700/900) |
limit |
int | 50 | Max interactors to return |
Confidence Score Thresholds
| Score | Level | Use Case |
|---|---|---|
| 150 | Low | Exploratory analysis |
| 400 | Medium | Balanced retrieval |
| 700 | High | Reliable interactions (default) |
| 900 | Highest | Very confident only |
Expected Output
[
{
"query_protein": "TP53",
"partner_string_id": "9606.ENSP00000340989",
"partner_gene": "SFN",
"combined_score": 0.999,
"scores": {
"experimental": 0.981,
"text_mining": 0.859,
"database": 0.75,
"coexpression": 0.0,
"phylogenetic": 0.0,
"gene_fusion": 0.0,
"neighborhood": 0.0
},
"ncbi_taxon_id": 9606
}
]
What ships with it
2 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.
- 7d ago First seen · 131 lines · 57 tokens per session scan A 646a826e91b1
ppi-string-query is a skill published in the GitHub repository PharMolix/OpenBioMed (1,107 stars, last pushed 1mo ago), licensed MIT. It adds 57 tokens to every session and 970 once invoked, about $0.0003 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
bulkrna-ppi-network
Load when querying STRING for the protein-protein interaction subgraph induced by a bulk RNA-seq DEG list and finding hub genes. Skip when pathway enrichment of the same list (use bulkrna-enrichment); de novo co-expression network discovery (use bulkrna-coexpression).
bulkrna-coexpression
Load when discovering gene co-expression modules and hub genes in a bulk RNA-seq cohort via WGCNA-style soft-thresholded networks. Skip when direct DE comparison (use bulkrna-de); PPI lookup of an existing gene list (use bulkrna-ppi-network); single-cell co-expression (use sc-grn).
literature_search
Search PubMed for biomedical research papers by topic, disease, or gene. Use this skill when: (1) User asks to find papers about a topic, (2) User wants recent literature on a disease or gene, (3) User needs citations for research, (4) User asks "papers about X" or "literature on Y".
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…