Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_Hubnpx agentmods add skills/zongtingwei/bioclaw_skills_hub/alphafold2-multimerWrote 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/zongtingwei/bioclaw_skills_hub/alphafold2-multimer)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/alphafold2-multimer"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/alphafold2-multimer/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/zongtingwei/bioclaw_skills_hub/alphafold2-multimer"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/alphafold2-multimer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00117 | $0.01610 |
| Opus 5 | $0.00059 | $0.00805 |
| Sonnet 5 | $0.00023 | $0.00322 |
| Haiku 4.5 | $0.00012 | $0.00161 |
Grade A, and why
alphafold2-multimer 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 — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AlphaFold2 / AlphaFold-Multimer Validation
Plain-language role: Use AlphaFold when you want a reference-grade structure prediction check for a designed sequence or complex.
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.8+ | 3.10 |
| CUDA | 11.0+ | 12.0+ |
| GPU VRAM | 32GB | 40GB (A100) |
| RAM | 32GB | 64GB |
| Disk | 100GB | 500GB (for databases) |
How to run
First time? See Installation Guide to set up Modal and biomodals.
Option 1: ColabFold (recommended for multimer)
cd biomodals
modal run modal_colabfold.py \
--input-faa sequences.fasta \
--out-dir output/
GPU: A100 (40GB) | Timeout: 3600s default
Option 2: Local installation
git clone https://github.com/deepmind/alphafold2-multimer.git
cd alphafold2-multimer
python run_alphafold.py \
--fasta_paths=query.fasta \
--output_dir=output/ \
--model_preset=monomer \
--max_template_date=2026-01-01
Option 3: ESMFold (fast single-chain)
modal run modal_esmfold.py \
--sequence "MKTAYIAKQRQISFVK..."
Key parameters
| Parameter | Default | Options | Description |
|---|---|---|---|
--model_preset |
monomer | monomer/multimer | Model type |
--num_recycle |
3 | 1-20 | Recycling iterations |
--max_template_date |
- | YYYY-MM-DD | Template cutoff |
--use_templates |
True | True/False | Use template search |
Output format
output/
├── ranked_0.pdb # Best model
├── ranked_1.pdb # Second best
├── ranking_debug.json # Confidence scores
├── result_model_1.pkl # Full results
├── msas/ # MSA files
└── features.pkl # Input features
Extracting metrics
import pickle
with open('result_model_1.pkl', 'rb') as f:
result = pickle.load(f)
plddt = result['plddt']
ptm = result['ptm']
iptm = result.get('iptm', None) # Multimer only
pae = result['predicted_aligned_error']
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.
- 12d ago First seen · 203 lines · 117 tokens per session scan A c3fff37af766
alphafold2-multimer is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 117 tokens to every session and 1,610 once invoked, about $0.0006 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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