proteina-complexa
Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Pr
npx skills add BioTender-max/awesome-bio-agent-skills --skill proteina-complexa --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ```bash 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: ```bash echo "DATA_PATH=/path/to/proteina-data" > .env ``` ### Additional files The upstream project docum
- Source Notes
- Prerequisites
- How to Run
- Option 1: Upstream Proteina environment
- Additional files
- Recommended Use Pattern
- 1. Start from backbone generation
- 2. Prefer fold-conditioned exploration
- 3. Hand off to sequence design
- 4. Validate and filter
- Typical Workflow
- When to Prefer This Over Other Tools
- Key Ideas to Preserve
- Common Mistakes
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 . echo "DATA_PATH=/path/to/proteina-data" > .env
What does the proteina-complexa skill do?
Proteina-Complexa flow-based protein backbone generation with fold-conditioned sampling guidance. Use this skill when: (1) Generating de novo protein backbones with hierarchical fold conditioning, (2) Exploring long-chain backbone generation beyond standard diffusion baselines, (3) Using NVIDIA Proteina-style flow matching workflows for controllable backbone design, (4) Comparing flow-based backbone generation against RFdiffusion or BoltzGen, (5) Prototyping fold-guided backbone campaigns before sequence design. This skill is based on the public NVIDIA Digital Bio Proteina project and uses "Pr
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill proteina-complexa --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.