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 adaptyvbio/protein-design-skills --skill protenixgit clone --depth 1 https://github.com/adaptyvbio/protein-design-skillsWrote 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/adaptyvbio/protein-design-skills/protenix)<a href="https://agentmods.dev/skills/adaptyvbio/protein-design-skills/protenix"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/protenix/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/adaptyvbio/protein-design-skills/protenix"><img src="https://agentmods.dev/badge/skills/adaptyvbio/protein-design-skills/protenix.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 | $0.00084 | $0.00807 |
| Opus 5 | $0.00042 | $0.00404 |
| Sonnet 5 | $0.00017 | $0.00161 |
| Haiku 4.5 | $0.00008 | $0.00081 |
Grade A, and why
protenix 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 11d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Protenix Structure Prediction
Protenix is ByteDance's open PyTorch
reproduction of AlphaFold3 (Apache 2.0). It is an AF3-class complex predictor, useful
next to boltz and chai for cross-checking designed complexes. Runnable through
biomodals.
Use Protenix-v2 for antibody-antigen complexes. The v2 model (464M params, April
2026) adds 9 to 13 percentage points of antibody-antigen accuracy over v1 at the
DockQ > 0.23 threshold and is more sample-efficient (v2 at 5 seeds exceeds v1 at 1000).
Select it with --model-name protenix-v2. For general complexes, the v1 base model is
fine.
Prerequisites
| Requirement | Value |
|---|---|
| Runner | Modal (biomodals) |
| GPU | L40S (default; GPU env var) |
| Setup | See Getting started |
How to run
git clone https://github.com/hgbrian/biomodals && cd biomodals
printf '>protein|A\nMAWTPLLLLLLSHCTGSLSQ...\n' > target.faa
uv run --with modal modal run modal_protenix.py \
--input-faa target.faa \
--seeds 42 \
--no-use-msa
Key parameters
| Parameter | Default | Description |
|---|---|---|
--input-faa |
one required | FASTA input (or --input-json) |
--seeds |
42 |
Comma-separated seeds |
--use-msa / --no-use-msa |
MSA on | Pass --no-use-msa for single-sequence |
--model-name |
v1 base | Set protenix-v2 for antibody-antigen complexes |
--use-mini |
off | Switch to the smaller protenix_mini model |
--out-dir |
./out/protenix |
Output directory |
When to use Protenix vs Boltz vs Chai
| Need | Tool |
|---|---|
| Affinity head (small molecules) | boltz (Boltz-2) |
| Fastest, ligand support | chai |
| Open AF3 reproduction | protenix (v1 base) |
| Antibody-antigen complexes | protenix-v2 |
Ranking a shortlist across more than one predictor is more reliable than trusting a single model.
Troubleshooting
| Issue | Cause | Fix |
|---|---|---|
| Missing input error | No --input-faa/--input-json |
Provide one |
| Slow run | MSA enabled | Add --no-use-msa |
| OOM | Large complex | Use --use-mini or a larger GPU |
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.
- 11d ago First seen · 84 lines · 84 tokens per session scan A 19d8ca88ac5b
protenix is a skill published in the GitHub repository adaptyvbio/protein-design-skills (158 stars, last pushed 3mo ago), licensed MIT. It adds 84 tokens to every session and 807 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
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…
boltz-structure-prediction
Boltz-1 / Boltz-2 structure prediction for proteins, complexes, and ligand-aware validation. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC…
boltz
Structure prediction using Boltz-1/Boltz-2, an open biomolecular structure predictor. Use this skill when: (1) Predicting protein complex structures, (2) Validating designed binders, (3) Need open-source alternative to AF2, (4) Predicting protein-ligand complexes, (5) Using local GPU resources. For QC thresholds, use…
protenix
Structure prediction using Protenix, ByteDance's open-source PyTorch reproduction of AlphaFold 3. Use this skill when: (1) Predicting protein/DNA/RNA/ligand/ion complex structures, (2) Need AF3-level accuracy with open-source code, (3) MSA-free fast prediction (--no-use-msa), (4) Multi-seed ensemble predictions, (5)…
chai
Structure prediction using Chai-1, a foundation model for molecular structure. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2.…
chai1-structure-prediction
Chai-1 structure prediction for protein complexes and design validation. Use this skill when: (1) Predicting protein-protein complex structures, (2) Validating designed binders, (3) Predicting protein-ligand complexes, (4) Using the Chai API for high-throughput prediction, (5) Need an alternative to AlphaFold2. For QC…