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 antibody-design-iggmgit 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/antibody-design-iggm)<a href="https://agentmods.dev/skills/pharmolix/openbiomed/antibody-design-iggm"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/antibody-design-iggm/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/antibody-design-iggm"><img src="https://agentmods.dev/badge/skills/pharmolix/openbiomed/antibody-design-iggm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
- medium Data Exfiltration · line 35 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00063 | $0.01166 |
| Opus 5 | $0.00032 | $0.00583 |
| Sonnet 5 | $0.00013 | $0.00233 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
antibody-design-iggm 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 13d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IgGM Antibody De Novo Design
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.10 |
| CUDA | 11.7+ | 11.8 |
| GPU VRAM | 24GB | 80GB (A800) |
| RAM | 32GB | 64GB |
How to run
Local installation
git clone https://github.com/TencentAI4S/IgGM.git
cd IgGM
pip install torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118
pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.0.1+cu118.html
pip install tqdm requests numpy==1.23.5 termcolor==2.4.0 biopython==1.79 openmm==8.2 pdbfixer ml-collections==0.1.1
Predict epitope based on antigen-antibody complex structure
python design.py --fasta complex_sequence.fasta --antigen complex_structure.pdb --cal_epitope
# --antigen: the structure of a known complex
# --fasta: the sequence of a known complex
- The generated epitope format is (The serial number starts at 1): 7 8 9 10 11 12 13 14 108 109 110 111 112 113 114 115 116 118 167
- If you specify epitope according to the sequence, make sure that the order of the sequence is consistent with the order in the PDB file, and mark the serial number of the corresponding position.
Given the structure of an antigen, design an antibody
python design.py --fasta design_requirement.fasta --antigen antigen_structure.pdb --epitope 7 8 9 10 11 --output output_dir
# --fasta: Directory path to input design requirement FASTA files, X for design region
# --antigen: Directory path to input antigen PDB files
# --epitope: epitope residues in antigen chain A , for example: 7 8 9 10 11
# --output: Directory path to output PDB files
Affinity maturation for an antibody sequence
python design.py --fasta design_requirement.fasta --antigen antigen_structure.pdb --fasta_origin original_antibody_sequence.fasta --run_task affinity_maturation --num_samples 10 --output output_dir
# --fasta: Directory path to input design requirement FASTA files, X for design region
# --antigen: Directory path to input antigen PDB files
# --fasta_origin: Directory path to original antibody FASTA files for affinity maturation
# --num_samples: number of samples for residue
# --output: Directory path to output PDB files
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.
- 13d ago First seen · 100 lines · 63 tokens per session scan A c6cbcbb43c2f
antibody-design-iggm is a skill published in the GitHub repository PharMolix/OpenBioMed (1,105 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 1,166 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
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Antibody and nanobody CDR design using IgGM (generative model by TencentAI4S). Use this skill when: (1) Designing nanobody (VHH) CDR loops against a target, (2) Designing full antibody (heavy + light chain) CDRs, (3) Redesigning existing antibody CDRs, (4) Need antigen-conditioned antibody generation, (5) Generating…
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De novo antibody and nanobody (VHH) design with Germinal. Use this skill when: (1) Designing epitope-targeted nanobodies or scFvs, (2) Needing CDR design on a fixed framework, (3) Working on antibody-format binders rather than miniproteins. For miniprotein binders, use binder-design (BoltzGen, BindCraft, RFdiffusion…
bindcraft
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high…