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 kegg-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/kegg-query)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/kegg-query"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/kegg-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/kegg-query"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/kegg-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 | $0.00082 | $0.01822 |
| Opus 5 | $0.00041 | $0.00911 |
| Sonnet 5 | $0.00016 | $0.00364 |
| Haiku 4.5 | $0.00008 | $0.00182 |
Grade A, and why
kegg-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 12d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KEGG Query
Query the KEGG (Kyoto Encyclopedia of Genes and Genomes) database for comprehensive biomedical information.
When to Use
- Drug Lookup: Retrieve drug efficacy, targets, metabolism, drug-drug interactions
- Pathway Analysis: Get pathway genes, compounds, modules, and related pathways
- Disease Discovery: Find disease-associated drugs, genes, and therapeutic targets
Workflow
Use Case 1: Drug Information Lookup
Fetch comprehensive drug information from KEGG DRUG database.
from scripts.kegg_api import kegg_find, kegg_get, parse_drug_entry
# Step 1: Search for drug by name
results = kegg_find("drug", "aspirin")
# Returns: [("dr:D00109", "Aspirin (JP18/USP); Acetylsalicylic acid; ...")]
# Step 2: Get full entry
drug_id = "dr:D00109" # or just "D00109"
entry = kegg_get(drug_id)
drug_info = parse_drug_entry(entry)
Output includes: Names, formula, efficacy, diseases, targets, pathways, metabolism, DDI.
See examples/drug_lookup.py for complete implementation.
Use Case 2: Pathway Analysis
Analyze KEGG pathways to retrieve genes, compounds, and modules.
from scripts.kegg_api import kegg_get, parse_pathway_entry
# Get pathway by ID (e.g., hsa00010 for Glycolysis)
entry = kegg_get("hsa00010")
pathway = parse_pathway_entry(entry)
# Access parsed data
print(f"Genes: {len(pathway['genes'])}") # 50+ genes
print(f"Compounds: {len(pathway['compounds'])}") # 30+ compounds
Output includes: Description, genes with KO/EC annotations, compounds, modules, related pathways.
See examples/pathway_analysis.py for complete implementation.
Use Case 3: Disease-Drug-Target Discovery
Discover therapeutic targets and drugs for diseases.
from scripts.kegg_api import kegg_find, kegg_get, parse_disease_entry
# Step 1: Search for disease
results = kegg_find("disease", "diabetes")
# Returns multiple matches including Type 2 diabetes (H00409)
# Step 2: Get disease details
entry = kegg_get("ds:H00409")
disease = parse_disease_entry(entry)
# Access drugs and targets
print(f"Drugs: {len(disease['drugs'])}") # 60+ drugs
print(f"Genes: {len(disease['genes'])}") # 20+ genes
What ships with it
5 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.
- 12d ago First seen · 202 lines · 82 tokens per session scan A 6446ac8fd55f
kegg-query is a skill published in the GitHub repository PharMolix/OpenBioMed (1,105 stars, last pushed 1mo ago), licensed MIT. It adds 82 tokens to every session and 1,822 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
bulkrna-enrichment
Load when running pathway / GO term enrichment on a bulk RNA-seq DE result list. Skip when the input is single-cell (use sc-enrichment); the input is spatial (use spatial-enrichment); metabolite pathways (use metabolomics-pathway-enrichment).
spatial-enrichment
Load when running pathway / gene-set enrichment per cluster on a preprocessed spatial AnnData via Enrichr (over-representation), GSEA (preranked), or ssGSEA (per-cell scores). Skip when ranking spatially variable genes (use spatial-genes); comparing pathways across conditions (use spatial-condition).
metabolomics-pathway-enrichment
Load when running over-representation analysis (ORA) on a metabolite list via Fisher's exact test against a built-in 9-pathway DEMO dictionary, BH-FDR adjusted. Skip when needing real KEGG / Reactome (this skill is demo-only); mummichog / fella topology methods (CLI accepts them but only ORA runs).
proteomics-enrichment
Load when running over-representation analysis (ORA) on a list of proteins via Fisher's exact test against a built-in 8-pathway DEMO dictionary, with BH-FDR correction. Skip when needing a real pathway database (this skill is demo-only) (use bulkrna-enrichment); rank-based GSEA.
enrichment
Functional enrichment and pathway analysis including GSEA, ORA, ssGSEA, GSVA, and decoupler-based activity inference.
spatial-enrichment
Pathway and gene set enrichment analysis for spatial transcriptomics data.