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setup-complexa-remote-tool

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Set up, launch, validate, and troubleshoot the Proteina-Complexa ToolUniverse remote tool and optionally relay it through ToolUniverse Connect. Use when deploying or auditing this implementation.

skills/setup-complexa-remote-tool/SKILL.md

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# Set up Proteina-Complexa as a remote tool

> Validation status (2026-10-02): Proteina-Complexa 1.1.0 (NVIDIA's flow-matching binder generator -- it DESIGNS proteins against a target structure, it does not fold one) ran on the NVIDIA GB10 (aarch64, cc 12.1) with the optimization kit in `fast` mode, all six levers engaged. Verified on FOUR CONSECUTIVE IDENTICAL REQUESTS, a mixed-shape sequence (100, 100, 200, 100 residues) and an ascending ladder (100/200/400), plus a sequence that changed the ENGINE KEY (`num_steps`, batch size, binder length, the target, the seed) and returned to the first request, which reproduced byte for byte. The held worker's design for the smoke target is byte-identical to the kit's own `run.sh design` in a fresh process, and to a worker built from the lock in an empty venv. Per-request peak reserved runs from 1.70 GiB (100 residues) to 20.25 GiB at the ceiling corner, which is the declared budget. One thing to know before deploying: torch 2.7.0+cu128's NVRTC does not know this card, and without the pinned 12.9 NVRTC every request dies in `torch.prod` (see the environment section). Public publication, cross-user isolation, concurrency, recovery and design quality (binding, designability) are untested; keep this deployment private.

## What a request is

A target protein STRUCTURE (PDB text), a crop of one of its chains (`A1-115`), hotspot residues on its surface (`["A37", "A39"]`, numbered as in that file), a binder length and a design count. The model samples that many binders, and each comes back as a decoded amino-acid sequence (and, with `return_pdb`, a PDB: chain A the target crop, chain B the new binder). It is NOT a predictor: nothing says a design binds, and this deployment runs upstream's **generation stage alone** -- upstream's own `++generation.search.algorithm=single-pass ++generation.reward_model=null` -- so the designs are raw, unscored and unranked. Upstream's shipped configuration also scores every design with an AlphaFold2 reward model whose parameters are not carried here.

The tool validates the target before it queues: the chain must exist in the PDB, the crop must hold residues, every hotspot must be a residue of that crop. This is not pedantry. Upstream does not refuse a hotspot it cannot find (measured: a request naming `A400` in a 115-residue target designed a binder anyway, against fewer hotspots than the caller thought).

## Prerequisites

- Run from the ToolUniverse repository root on Linux with Python 3.12.
- A CUDA GPU is required. This provider holds its model resident between requests; CPU is not a target.
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