Menu

Individual skill

complexa-sweep

0upvotes

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.

skills/bionemo-complexa-sweep/SKILL.md

Read pinned source

Source commit fce3cca4170be75e82c64920654d56ee2811b005

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 9, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
3,917
Repository last updated
Nov 9, 2026
License
Apache-2.0

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt


# 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
Back to top