Complexa Sweep

NVIDIAOfficialCreated by Ohad MosafiDistributed by NVIDIA

Skill
Workflow
Science and research
Apache-2.0
Medium risk
Has scripts

From the author

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.

— the description by Ohad Mosafi

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The complexa-sweep skill provides a structured runbook for executing systematic parameter sweeps over Proteina-Complexa design pipelines. It automates the generation of multiple inference configurations based on a sweeper YAML, manages the execution loop across these configurations, and aggregates results into a ranked summary. This process enables users to perform ablations, tuning, and Pareto searches over quality and computational cost metrics, ensuring consistent evaluation across diverse parameter sets.

What it does

Generates multiple inference configurations from a cartesian-product sweeper YAML.
Executes the design pipeline across a generated grid of parameter combinations.
Aggregates per-config success metrics into a ranked summary CSV.
Identifies the best-performing configuration and Pareto frontiers.
Provides a manifest for result tracking and experiment reproducibility.
Includes pre-flight checks to estimate GPU budget and hardware readiness.

When to use it

When performing hyperparameter tuning or ablations on design pipelines.
When comparing multiple search algorithms or model configurations.
When conducting Pareto searches to balance design quality against wall-clock time.
When you need to rank configurations based on success rates and diversity metrics.

Advantages

Systematic exploration of parameter space via cartesian-product enumeration.
Automated aggregation of results simplifies complex multi-run analysis.
Supports multi-GPU sharding for efficient execution of large sweeps.
Provides clear visibility into cost and resource requirements before execution.

Usage examples

“Run a sweep over beam_width and nsteps for the 22_DerF21 task.”: The agent will generate the necessary inference configs using the specified sweeper YAML, execute the design pipeline for each combination, and produce a ranked sweep_summary.csv.
“Estimate the GPU cost for a 4-axis sweep with 4 values each.”: The agent will calculate the total number of configurations and estimate the total GPU-hours based on the per-run baseline, prompting for confirmation before proceeding.

Install

Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/complexa-sweep/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip complexa-sweep.zip -d .agents/skills

Documentation by Ohad Mosafi

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, 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 "$SKILL_DIR"/scripts/preflight.sh

Read ./complexa_setup/preflight.json. A sweep multiplies GPU time by the number of configs and targets. Compute the budget before launch and obtain confirmation if the user has not already authorized that scope and cost:

"This sweep produces N configs × ~M minutes per config ≈ TOTAL GPU-hours. OK to proceed? (y / reduce / cancel)"

If gpu.available=false, stop before GPU execution. Sweeper authoring, Cartesian-product enumeration, cost estimates, and analysis of supplied CSVs can run on CPU. For those requests, finish the requested artifacts and record the launch prerequisites. Label synthetic input data as synthetic and keep estimated GPU time separate from measured runtime.

For a hardware-conditional request, verify the allocated hardware before selecting the CPU fallback. Missing weights, dependencies, or sufficient VRAM for an allocated GPU are launch blockers, not evidence of a CPU-only environment. Report probe or execution failures without reclassifying the selected branch. The shared preflight's gpu.available=false also covers failed or missing nvidia-smi; check device/allocation evidence before treating it as GPU absence. In containers, /proc/driver/nvidia/gpus and loaded kernel modules may describe the host, not this task's allocation. Check container device exposure and any available scheduler allocation information together. A CPU allocation can share a GPU host; an allocated GPU with inaccessible devices is blocked. If allocation remains unclear, report it as undetermined.

Step 2: Pick the pipeline + target

Use the user's supplied pipeline, targets, and run name. Protein, ligand, and AME runs use search_binder_local_pipeline, search_ligand_binder_local_pipeline, and search_ame_local_pipeline, respectively. Ask only for missing values:

pipeline_config_name — e.g. search_binder_local_pipeline (default), search_ligand_binder_local_pipeline, search_ame_local_pipeline.
task_name — e.g. 02_PDL1, 22_DerF21, 39_7V11_LIGAND. Passed as --override generation.task_name=<task>.
run_name — short tag for output dir naming.

Examples

Step 3: Pick or author the sweeper YAML

Sweeper YAMLs live in configs/sweeps/. Each key is a dot-notation Hydra path; each value is a list. The cartesian product becomes N configs.

Canned sweepers

File	Axis	Values	Configs
`configs/sweeps/beam_width.yaml`	`generation.search.beam_search.beam_width`	1, 2, 4, 8	4
`configs/sweeps/bb_ca_temperature.yaml`	`generation.model.bb_ca.simulation_step_params.sc_scale_noise`	0.1, 0.4	2
`configs/sweeps/search_replicas.yaml`	`generation.search.best_of_n.replicas`	1, 4, 16, 64	4
`configs/sweeps/example.yaml`	beam_width × nsteps	(2,4) × (200,400)	4

Use a matching sweeper, then check that its axes affect the selected algorithm. The binder config defaults to best-of-n: a beam-width sweep must also pin generation.search.algorithm: [beam-search]. A replicas sweep pins generation.search.algorithm: [best-of-n]. For AME, reward-guided search also requires a configured reward model; its default is null.

Authoring a new sweeper

Minimal multi-axis example (saved to configs/sweeps/my_sweep.yaml):

# 3 beam widths × 2 nsteps = 6 configs
generation.search.algorithm: [beam-search]

generation.search.beam_search.beam_width:
  - 2
  - 4
  - 8

generation.args.nsteps:
  - 200
  - 400

Rules (from script_utils/generate_inference_configs.py:load_sweeper_file):

Top-level mapping only. Keys are dot-notation Hydra paths.
Keep budgets, launch status, and other planning metadata in the launch plan, outside the sweeper. Every top-level entry becomes an override axis; a separate targets list would multiply the grid again.
Values must be lists. A scalar is auto-wrapped into a single-element list (which pins a value without adding a dimension).
For a list-valued parameter, nest each candidate list: metric.sequence_types: [[self, mpnn]] pins one list. The flat form sweeps two strings; --override parses scalars only.
Cartesian product: total configs = product of list lengths. Multiply by the number of targets unless target names are already one of the axes; do not count them twice.
If a key appears in both the sweeper file and an --override, the override wins and that axis collapses.
Reject empty axes before launch: an axis of [] produces zero configurations.

See references/sweep_axes.md for the full catalogue of swept keys (typical ranges, cost multipliers, what improves/regresses).

Dry-run preview before generating

With the full project available, confirm the config count first:

python script_utils/generate_inference_configs.py \
    --config_name search_binder_local_pipeline \
    --sweeper configs/sweeps/my_sweep.yaml \
    --override generation.task_name=22_DerF21 \
    --run_name my_sweep \
    --dryrun

The output lists every axis + value list and prints DRY RUN — would generate N config pair(s). This previews enumeration only; it returns before Hydra composition and does not validate the pipeline, parameter types, or launch prerequisites.

When the full project is available, inspect the materialized inference configs before launching. Check the resolved target, selected search algorithm, and requested parameter values across the generated grid. The generator permits unknown keys during merging, so a successful write alone does not establish that a misspelled or misplaced axis changes the intended parameter. With only a partial snapshot, check each dot-path against its source config and report that materialization remains unverified.

Step 4: Generate configs + loop `complexa design`

Once the dry-run looks right, drop --dryrun to materialize inf_{idx}_{run_name}.yaml + eval_{idx}_{run_name}.yaml pairs under configs/inference_configs/ and configs/eval_configs/. Then loop complexa design over the inference configs.

For multiple targets, put generation.task_name in the sweeper as an axis, or give each target's generator invocation a distinct --run_name. Indices restart at zero on each invocation and filenames omit the target; reusing the same run name and output directories overwrites earlier configs and reuses result paths.

# 1. Generate one inf_*.yaml + one eval_*.yaml per combination.
python script_utils/generate_inference_configs.py \
    --config_name search_binder_local_pipeline \
    --sweeper configs/sweeps/my_sweep.yaml \
    --override generation.task_name=22_DerF21 \
    --run_name my_sweep

# 2. Loop complexa design over each generated inference config.
for cfg in configs/inference_configs/inf_*_my_sweep.yaml; do
    complexa design "$cfg" || echo "FAILED: $cfg"
done

This serialises the sweep on a single GPU — be honest with the user that wall-clock = N × per-run.

Multi-GPU host (optional speed-up)

On a host with K GPUs, shard the config list K ways and launch one loop per GPU in parallel. Each complexa design call uses exactly one GPU at default gen_njobs=1 / eval_njobs=1, so pinning via CUDA_VISIBLE_DEVICES keeps them from colliding:

CONFIGS=(configs/inference_configs/inf_*_my_sweep.yaml)
N=${#CONFIGS[@]}; K=4   # 4 GPUs
for gpu in $(seq 0 $((K-1))); do
    (
        for i in $(seq $gpu $K $((N-1))); do
            CUDA_VISIBLE_DEVICES=$gpu complexa design "${CONFIGS[$i]}" \
                || echo "FAILED on GPU $gpu: ${CONFIGS[$i]}"
        done
    ) &
done
wait

Drop gen_njobs / eval_njobs overrides into the loop only if you intentionally want each complexa design invocation to consume multiple GPUs (and you have a way to keep them out of each other's way).

Step 5: Collect results

Each swept config writes to its own ./inference/inf_{idx}_{run_name}/ directory; the analyze step writes ./evaluation_results/eval_{idx}_{run_name}/results_*.csv. After the sweep finishes:

ls -d ./inference/inf_*_my_sweep/
ls ./evaluation_results/eval_*_my_sweep/results_*.csv

Inspect the supplied CSV schema before aggregating. If it already contains one row per configuration with n_passed and n_designs, compute success_rate = n_passed / n_designs; flag zero denominators and invalid counts. Do not treat those summary rows as individual designs. For per-design rows, use existing pass flags, parsing string booleans explicitly. If flags are absent, use the run's analysis thresholds, result_type, column names, and units from EVALUATION_METRICS.md. PAE may be normalized by 31 and pLDDT may use different scales; do not substitute generic thresholds.

Step 6: Rank configs

Emit sweep_summary.csv to the run directory. One row per config:

Column	How to compute
`config_id`	The `{idx}` from `inf_{idx}_{run_name}`
`<axis_1>`, `<axis_2>`, ...	The swept value at this combination (read from the per-config `inf_*.yaml`)
`n_samples`	`n_designs` for a supplied config summary, otherwise per-design row count
`success_rate`	`n_passed / n_designs` for a config summary, otherwise mean of parsed per-design pass flags
`mean_i_pae`	Preserve the supplied config mean, or average per-design values in consistent units (lower = better)
`mean_i_plddt`	`i_plddt.mean()` (higher = better)
`diversity_score`	Unique sequence count / `n_samples` (or use TM-score clustering if available)
`wall_clock_min`	From the per-config log timestamps

Populate only metrics present in the inputs or computable from them. Missing diversity or timing data stays unavailable.

Then report:

Best config = argmax of success_rate. Tie-break on mean_i_pae (lower).
Pareto frontier: choose the requested objectives and directions. For time versus success, minimize time and maximize success; for time versus iPAE, minimize both. A point is dominated when another is no worse on every objective and strictly better on at least one. Keep distinct config IDs at equal nondominated coordinates; report invalid/missing measurements separately.

Save the full table to sweep_summary.csv, then reopen the saved results and print a compact summary: row count, best config and metric values, and frontier IDs with their objective values. Report the CSV paths and keep the hardware probe and configuration dumps separate from this result summary.

Step 7: Emit manifest

Capture the resolved invocation + outputs for replay. The shared helper takes a single --output-dir (it walks for CSVs and pulls the Hydra config from the run's .hydra/), so call it against the best-config's evaluation directory and pass the parent complexa design command for that run:

BEST_ID=4   # from Step 6 ranking
python3 "$SKILL_DIR"/scripts/write_manifest.py \
    --output-dir ./evaluation_results/eval_${BEST_ID}_my_sweep \
    --command "python script_utils/generate_inference_configs.py --config_name search_binder_local_pipeline --sweeper configs/sweeps/my_sweep.yaml --override generation.task_name=22_DerF21 --run_name my_sweep && for cfg in configs/inference_configs/inf_*_my_sweep.yaml; do complexa design \"\$cfg\"; done" \
    --skill complexa-sweep \
    --out ./sweep_runs/my_sweep/sweep_manifest.json

Alongside the manifest, save the ranked sweep_summary.csv from Step 6 to ./sweep_runs/my_sweep/sweep_summary.csv and surface both paths to the user.

Recommended sweep recipes

Symptom	Sweep this	Why
Quality not good enough	`beam_width × nsteps` (use `example.yaml` as a starting point)	Both raise compute → quality; find the cheapest combination that lands.
Too slow / want speed-up	`nsteps` downward (e.g. `[100, 200, 400]`) with fixed `beam_width=4`	Find the smallest nsteps that retains success rate.
Mode collapse / low diversity	`generation.model.bb_ca.simulation_step_params.sc_scale_noise` (use `bb_ca_temperature.yaml`)	Higher noise → more diverse backbones.
Reward over-fitting (high reward, bad metrics)	`generation.reward_model.reward_models.af2folding.reward_weights.{i_pae, plddt}` ratio	Re-balance composite reward.
Broaden the best-of-N candidate pool	`search_replicas.yaml` (`best_of_n.replicas`)	More candidates per search; use separate seeds/runs to measure repeatability.
Algorithm shoot-out	`generation.search.algorithm` over `[single-pass, best-of-n, beam-search, fk-steering]`	Compare search regimes; pin everything else.

Hardware

Total GPU-time for a sweep = N_configs × per_run_GPU_time. A single search_binder_local_pipeline run on one A100 is roughly 30–90 min (binder length + nsteps dependent). A 4-axis × 4-value sweep = 256 configs × ~60 min = ~256 GPU-hours — at that scale, plan on a multi-GPU host with the Step-4 sharding pattern.

Refer to references/hardware.md for the per-run baseline + VRAM minima.

Troubleshooting

Symptom	Cause	Fix
`Sweeper file not found` from `generate_inference_configs.py`	Path resolved from wrong CWD	Use a path relative to the repo root; or pass an absolute path.
`Sweeper YAML must be a mapping`	Top-level YAML is a list or scalar	Rewrite as `key: [v1, v2]` mapping.
Dry-run reports zero config pairs	One of the value lists is empty `[]`	Supply at least one value before generating or launching; the generator does not reject an empty product.
`complexa design` rejects `--sweeper`	Confusion between entrypoints	The CLI does not accept `--sweeper`. Use `generate_inference_configs.py` first, then loop `complexa design` over the generated configs.
Override silently collapses a sweep axis	`--override key=v` shadowed a sweep key	Drop either the override OR the matching key from the sweeper file.
One config in the loop fails, sweep keeps going	The `\|\| echo "FAILED…"` in Step 4 swallows the error	Re-run the failed `inf_*.yaml` standalone; check the per-config log under `./logs/`. Skip failed `config_id` when ranking.

For per-axis reference (typical ranges, cost, what gets better/worse), see references/sweep_axes.md.

For the user-facing sweep system overview (config generation, output layout), see references/SWEEP.md.

Files

ATTRIBUTION.mdAdded by AutoGineer955 B
BENCHMARK.md7.9 KiB
LICENSELicence file added by AutoGineer11.2 KiB
SKILL.md16.5 KiB
config/skillspector-baseline.yml1.2 KiB
evals/evals.json34.9 KiB
evals/files/launch_review/launch.shScript586 B
evals/files/launch_review/sweeper.yaml89 B
evals/files/pareto_results.csv84 B
evals/files/project/SNAPSHOT.md1.0 KiB
evals/files/project/configs/pipeline/binder/binder_generate.yaml9.0 KiB
evals/files/project/configs/pipeline/model_sampling.yaml1.4 KiB
evals/files/project/configs/search_binder_local_pipeline.yaml1.5 KiB
evals/files/project/configs/sweeps/example.yaml1.1 KiB
evals/files/project/script_utils/generate_inference_configs.pyScript15.1 KiB
evals/files/project/source_manifest.json670 B
evals/files/summary_review.csv297 B
evals/files/sweep_results.csv322 B
references/CONFIGURATION_GUIDE.md34.7 KiB
references/EVALUATION_METRICS.md55.9 KiB
references/INFERENCE.md14.9 KiB
references/SEARCH_METADATA.md7.7 KiB
references/SWEEP.md10.6 KiB
references/hardware.md4.8 KiB
references/sweep_axes.md11.7 KiB
scripts/preflight.shScript8.4 KiB
scripts/write_manifest.pyScript9.6 KiB
skill-card.md4.6 KiB
skill.oms.sig9.9 KiB

LICENSE

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.


                                 Apache License
                           Version 2.0, January 2004
                        http://www.apache.org/licenses/

   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION

   1. Definitions.

      "License" shall mean the terms and conditions for use, reproduction,
      and distribution as defined by Sections 1 through 9 of this document.

      "Licensor" shall mean the copyright owner or entity authorized by
      the copyright owner that is granting the License.

      "Legal Entity" shall mean the union of the acting entity and all
      other entities that control, are controlled by, or are under common
      control with that entity. For the purposes of this definition,
      "control" means (i) the power, direct or indirect, to cause the
      direction or management of such entity, whether by contract or
      otherwise, or (ii) ownership of fifty percent (50%) or more of the
      outstanding shares, or (iii) beneficial ownership of such entity.

      "You" (or "Your") shall mean an individual or Legal Entity
      exercising permissions granted by this License.

      "Source" form shall mean the preferred form for making modifications,
      including but not limited to software source code, documentation
      source, and configuration files.

      "Object" form shall mean any form resulting from mechanical
      transformation or translation of a Source form, including but
      not limited to compiled object code, generated documentation,
      and conversions to other media types.

      "Work" shall mean the work of authorship, whether in Source or
      Object form, made available under the License, as indicated by a
      copyright notice that is included in or attached to the work
      (an example is provided in the Appendix below).

      "Derivative Works" shall mean any work, whether in Source or Object
      form, that is based on (or derived from) the Work and for which the
      editorial revisions, annotations, elaborations, or other modifications
      represent, as a whole, an original work of authorship. For the purposes
      of this License, Derivative Works shall not include works that remain
      separable from, or merely link (or bind by name) to the interfaces of,
      the Work and Derivative Works thereof.

      "Contribution" shall mean any work of authorship, including
      the original version of the Work and any modifications or additions
      to that Work or Derivative Works thereof, that is intentionally
      submitted to Licensor for inclusion in the Work by the copyright owner
      or by an individual or Legal Entity authorized to submit on behalf of
      the copyright owner. For the purposes of this definition, "submitted"
      means any form of electronic, verbal, or written communication sent
      to the Licensor or its representatives, including but not limited to
      communication on electronic mailing lists, source code control systems,
      and issue tracking systems that are managed by, or on behalf of, the
      Licensor for the purpose of discussing and improving the Work, but
      excluding communication that is conspicuously marked or otherwise
      designated in writing by the copyright owner as "Not a Contribution."

      "Contributor" shall mean Licensor and any individual or Legal Entity
      on behalf of whom a Contribution has been received by Licensor and
      subsequently incorporated within the Work.

   2. Grant of Copyright License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      copyright license to reproduce, prepare Derivative Works of,
      publicly display, publicly perform, sublicense, and distribute the
      Work and such Derivative Works in Source or Object form.

   3. Grant of Patent License. Subject to the terms and conditions of
      this License, each Contributor hereby grants to You a perpetual,
      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
      (except as stated in this section) patent license to make, have made,
      use, offer to sell, sell, import, and otherwise transfer the Work,
      where such license applies only to those patent claims licensable
      by such Contributor that are necessarily infringed by their
      Contribution(s) alone or by combination of their Contribution(s)
      with the Work to which such Contribution(s) was submitted. If You
      institute patent litigation against any entity (including a
      cross-claim or counterclaim in a lawsuit) alleging that the Work
      or a Contribution incorporated within the Work constitutes direct
      or contributory patent infringement, then any patent licenses
      granted to You under this License for that Work shall terminate
      as of the date such litigation is filed.

   4. Redistribution. You may reproduce and distribute copies of the
      Work or Derivative Works thereof in any medium, with or without
      modifications, and in Source or Object form, provided that You
      meet the following conditions:

      (a) You must give any other recipients of the Work or
          Derivative Works a copy of this License; and

      (b) You must cause any modified files to carry prominent notices
          stating that You changed the files; and

      (c) You must retain, in the Source form of any Derivative Works
          that You distribute, all copyright, patent, trademark, and
          attribution notices from the Source form of the Work,
          excluding those notices that do not pertain to any part of
          the Derivative Works; and

      (d) If the Work includes a "NOTICE" text file as part of its
          distribution, then any Derivative Works that You distribute must
          include a readable copy of the attribution notices contained
          within such NOTICE file, excluding those notices that do not
          pertain to any part of the Derivative Works, in at least one
          of the following places: within a NOTICE text file distributed
          as part of the Derivative Works; within the Source form or
          documentation, if provided along with the Derivative Works; or,
          within a display generated by the Derivative Works, if and
          wherever such third-party notices normally appear. The contents
          of the NOTICE file are for informational purposes only and
          do not modify the License. You may add Your own attribution
          notices within Derivative Works that You distribute, alongside
          or as an addendum to the NOTICE text from the Work, provided
          that such additional attribution notices cannot be construed
          as modifying the License.

      You may add Your own copyright statement to Your modifications and
      may provide additional or different license terms and conditions
      for use, reproduction, or distribution of Your modifications, or
      for any such Derivative Works as a whole, provided Your use,
      reproduction, and distribution of the Work otherwise complies with
      the conditions stated in this License.

   5. Submission of Contributions. Unless You explicitly state otherwise,
      any Contribution intentionally submitted for inclusion in the Work
      by You to the Licensor shall be under the terms and conditions of
      this License, without any additional terms or conditions.
      Notwithstanding the above, nothing herein shall supersede or modify
      the terms of any separate license agreement you may have executed
      with Licensor regarding such Contributions.

   6. Trademarks. This License does not grant permission to use the trade
      names, trademarks, service marks, or product names of the Licensor,
      except as required for reasonable and customary use in describing the
      origin of the Work and reproducing the content of the NOTICE file.

   7. Disclaimer of Warranty. Unless required by applicable law or
      agreed to in writing, Licensor provides the Work (and each
      Contributor provides its Contributions) on an "AS IS" BASIS,
      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
      implied, including, without limitation, any warranties or conditions
      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
      PARTICULAR PURPOSE. You are solely responsible for determining the
      appropriateness of using or redistributing the Work and assume any
      risks associated with Your exercise of permissions under this License.

   8. Limitation of Liability. In no event and under no legal theory,
      whether in tort (including negligence), contract, or otherwise,
      unless required by applicable law (such as deliberate and grossly
      negligent acts) or agreed to in writing, shall any Contributor be
      liable to You for damages, including any direct, indirect, special,
      incidental, or consequential damages of any character arising as a
      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

   9. Accepting Warranty or Additional Liability. While redistributing
      the Work or Derivative Works thereof, You may choose to offer,
      and charge a fee for, acceptance of support, warranty, indemnity,
      or other liability obligations and/or rights consistent with this
      License. However, in accepting such obligations, You may act only
      on Your own behalf and on Your sole responsibility, not on behalf
      of any other Contributor, and only if You agree to indemnify,
      defend, and hold each Contributor harmless for any liability
      incurred by, or claims asserted against, such Contributor by reason
      of your accepting any such warranty or additional liability.

   END OF TERMS AND CONDITIONS

   APPENDIX: How to apply the Apache License to your work.

      To apply the Apache License to your work, attach the following
      boilerplate notice, with the fields enclosed by brackets "[]"
      replaced with your own identifying information. (Don't include
      the brackets!)  The text should be enclosed in the appropriate
      comment syntax for the file format. We also recommend that a
      file or class name and description of purpose be included on the
      same "printed page" as the copyright notice for easier
      identification within third-party archives.

   Copyright [yyyy] [name of copyright owner]

   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at

       http://www.apache.org/licenses/LICENSE-2.0

   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Declares allowed tools, Needs Python

Security

What our scanner noticed· Medium risk

Large base64-encoded blobs
Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by

Ohad Mosafi

Publisher

NVIDIA (Official)

Distributed by

NVIDIA

Source

NVIDIA/skills · skills/bionemo-complexa-sweep · commit fce3cca

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE-APACHE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-09

AutoGineer is not affiliated with or endorsed by Ohad Mosafi or NVIDIA.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download complexa-sweep.zip