# Complexa Sweep

Agent runbook for Proteina-Complexa parameter sweeps through the separately installed...

- Repository: https://github.com/NVIDIA/skills (3812 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-10-30
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add NVIDIA/skills --skill complexa-sweep -g -a claude-code -y`
Codex: `npx skills add NVIDIA/skills --skill complexa-sweep -g -a codex -y`
Cursor: `npx skills add NVIDIA/skills --skill complexa-sweep -g -a cursor -y`
Gemini CLI: `npx skills add NVIDIA/skills --skill complexa-sweep -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add NVIDIA/skills`

## Description

Agent runbook for Proteina-Complexa parameter sweeps through the separately installed first-party CLI and config generator. Use for cartesian-product hyperparameter scans, sweeper YAML authoring, configuration comparisons, ablations, tuning, success-rate ranking, or Pareto searches over quality and wall-clock cost. Do not use for a single existing-job launch or SLURM submission template. SKILL.md performs the orchestration; bundled executables only inspect local readiness and write a run manifest.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# complexa-sweep

Run cartesian-product parameter sweeps over Proteina-Complexa design pipelines. Pick or author a sweeper YAML in `configs/sweeps/`, expand it to N inference configs with `script_utils/generate_inference_configs.py`, loop `complexa design` over those configs, then aggregate per-config success metrics into a ranked summary CSV plus a manifest.

> **Important:** The `complexa design` CLI does **NOT** accept `--sweeper` directly. Sweeps are driven by `script_utils/generate_inference_configs.py`, which writes one `configs/inference_configs/inf_{idx}_{run_name}.yaml` per sweep combination. You then loop `complexa design` over those generated configs.

## Instructions

Sweeper authoring, Cartesian enumeration, cost estimates, and analysis of supplied
CSVs need only the relevant configs and data. Complete those artifacts without
setting up inference. Read the relevant axis section in
[sweep_axes.md](references/sweep_axes.md), then check the matching keys in the
selected source config for current defaults. Summary CSVs with supplied counts
and objective columns do not require the full inference or metric guides.
For a partial snapshot,
enumerate combinations locally and provide the future generator command;
running that command requires the full project and its dependencies.

For a requested launch, check readiness and the budget below. A single existing
job or a SLURM template does not require a parameter sweep.

## Step 1: Pre-flight

Before GPU execution, or when the request conditions its workflow on hardware,
set `SKILL_DIR` to the directory containing this manifest and run preflight once.
Use a separate shell assignment; an inline environment assignment cannot set
`$SKILL_DIR`'s expansion in the same command.
Reuse an existing probe from the same request if the environment is unchanged:

```bash
bash "$SKILL_DIR"/scripts/preflight.sh
```

The rest: https://github.com/NVIDIA/skills/blob/HEAD/skills/bionemo-complexa-sweep/SKILL.md