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-mutation-analysisgit 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-mutation-analysis)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/protein-mutation-analysis"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/protein-mutation-analysis/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/protein-mutation-analysis"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/protein-mutation-analysis.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 | $0.00081 | $0.00939 |
| Opus 5 | $0.00041 | $0.00469 |
| Sonnet 5 | $0.00016 | $0.00188 |
| Haiku 4.5 | $0.00008 | $0.00094 |
Grade A, and why
protein-mutation-analysis 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 10d 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protein Mutation Analysis
Analyze the functional impact of protein mutations using MutaPLM and visualize protein structures.
When to Use
- User provides a UniProt ID and mutation (e.g., "P04637 R248Q")
- User wants to understand the effect of a specific mutation
- User needs to visualize a mutated protein structure
- Research on disease-associated genetic variants
Workflow
Step 1: Retrieve Protein from UniProt
from open_biomed.tools.tool_registry import TOOLS
tool = TOOLS["protein_uniprot_request"]
result, message = tool.run(accession="P04637")
protein = result.get("protein")
Step 2: Explain Mutation with MutaPLM
mutation_tool = TOOLS["mutation_explanation"]
mutation_result, _ = mutation_tool.run(
protein=protein,
mutation="R248Q" # Format: OriginalAA + Position + MutantAA
)
Step 3: Predict Structure with ESMFold
folding_tool = TOOLS["protein_folding"]
fold_result, _ = folding_tool.run(protein=protein)
predicted_protein = fold_result.get("protein")
Step 4: Visualize Protein Structure
viz_tool = TOOLS["visualize_protein"]
viz_result, _ = viz_tool.run(protein=predicted_protein, style="cartoon")
See examples/basic_analysis.py for the complete implementation.
Expected Outputs
| Step | Output | Description |
|---|---|---|
| Retrieve Protein | Protein object | Name, sequence from UniProt |
| Explain Mutation | Text | Functional impact from MutaPLM |
| Predict Structure | Protein with 3D coords | Structure from ESMFold |
| Visualize | PNG file | Rendered protein structure |
Mutation Format
Single amino acid mutation: OriginalAA + Position + MutantAA
| Valid | Invalid | Reason |
|---|---|---|
| R248Q | R248 | Missing mutant AA |
| V600E | 248Q | Missing original AA |
| L858R | ARG248GLN | Use single-letter codes |
Error Handling
Missing Model Checkpoints
Symptom: FileNotFoundError or AttributeError
Solution: Check checkpoints exist:
./checkpoints/server/mutaplm.pth./checkpoints/esm2/650m/./checkpoints/biomedgpt-lm/
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.
- 10d ago First seen · 142 lines · 81 tokens per session scan A 2301fa38b58b
protein-mutation-analysis is a skill published in the GitHub repository PharMolix/OpenBioMed (1,106 stars, last pushed 1mo ago), licensed MIT. It adds 81 tokens to every session and 939 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.
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