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 zongtingwei/Bioclaw_Skills_Hub --skill proteina-complexagit clone --depth 1 https://github.com/zongtingwei/Bioclaw_Skills_HubWrote 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/proteina-complexa)<a href="https://agentmods.dev/skills/zongtingwei/bioclaw_skills_hub/proteina-complexa"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/proteina-complexa/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/proteina-complexa"><img src="https://agentmods.dev/badge/skills/zongtingwei/bioclaw_skills_hub/proteina-complexa.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 | $0.00163 | $0.01204 |
| Opus 5 | $0.00081 | $0.00602 |
| Sonnet 5 | $0.00033 | $0.00241 |
| Haiku 4.5 | $0.00016 | $0.00120 |
Grade A, and why
proteina-complexa 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proteina-Complexa Backbone Generation
Plain-language role: Use this skill when you want a flow-based backbone generator with fold-class conditioning, especially for exploratory de novo design.
Source Notes
- Public upstream reference:
NVIDIA-Digital-Bio/proteina - Publicly described as a large-scale flow-based protein backbone generator with hierarchical fold class conditioning
- Upstream setup and weights may change over time, so verify the current README and license before running
- Check the upstream NVIDIA license before commercial use or redistribution of model artifacts
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.10+ | 3.11 |
| CUDA | 12.0+ | 12.1+ |
| GPU VRAM | 24GB | 40GB+ |
| Environment manager | conda | mamba or micromamba |
How to Run
Option 1: Upstream Proteina environment
git clone https://github.com/NVIDIA-Digital-Bio/proteina.git
cd proteina
mamba env create -f environment.yaml
conda activate proteina_env
pip install -e .
Create a .env file in the repository root:
echo "DATA_PATH=/path/to/proteina-data" > .env
Additional files
The upstream project documents extra data and weight bundles that must live under DATA_PATH.
At minimum, verify:
- metric feature files
- model weights
- CATH label mapping files
- dataset index files if you plan to train or evaluate
Recommended Use Pattern
1. Start from backbone generation
Use Proteina-Complexa when the main task is generating diverse backbones, not sequence optimization.
2. Prefer fold-conditioned exploration
The upstream model is especially useful when you want:
- hierarchical fold control
- long-chain generation
- comparison against diffusion-based backbone generators
3. Hand off to sequence design
After generating promising backbones:
- use
proteinmpnnfor general inverse folding - use
solublempnnwhen expression robustness matters more
4. Validate and filter
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 · 151 lines · 163 tokens per session scan A 86738fe940f5
proteina-complexa is a skill published in the GitHub repository zongtingwei/Bioclaw_Skills_Hub (26 stars, last pushed 5mo ago), licensed MIT. It adds 163 tokens to every session and 1,204 once invoked, about $0.0008 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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