Skip to content
cccskills
FreePublished on GitHub

coronavirus

Use the Folding@home coronavirus repository conventions to curate molecular structures, prepare bounded OpenMM systems, and preserve target, publication, and provenance notes.

The English summary is being prepared. Showing the original description.

See how to install

Files included (23)

  • SKILL.md3.8 KB
  • references/repo-provenance.md2.0 KB
  • references/repo-routing-metadata.json337 B
  • references/troubleshooting.md2.8 KB
  • scripts/check_openmm_env.py365 B
  • sub-skills/project-context/references/templates.md7.6 KB
  • sub-skills/project-context/references/troubleshooting.md5.7 KB
  • sub-skills/project-context/SKILL.md6.2 KB
  • sub-skills/structure-curation/references/data-formats.md1.9 KB
  • sub-skills/structure-curation/references/troubleshooting.md2.6 KB
  • sub-skills/structure-curation/references/workflows.md2.1 KB
  • sub-skills/structure-curation/scripts/normalize_ligand_residue.py4.1 KB
  • sub-skills/structure-curation/scripts/select_chains_and_residues.py5.8 KB
  • sub-skills/structure-curation/scripts/validate_pdb.py2.4 KB
  • sub-skills/structure-curation/SKILL.md2.8 KB
  • sub-skills/system-preparation/references/api-reference.md3.3 KB
  • sub-skills/system-preparation/references/ligand-complex.md3.0 KB
  • sub-skills/system-preparation/references/troubleshooting.md3.0 KB
  • sub-skills/system-preparation/references/workflows.md4.3 KB
  • sub-skills/system-preparation/scripts/check_openmm_env.py2.8 KB
  • sub-skills/system-preparation/scripts/continue_openmm.py5.0 KB
  • sub-skills/system-preparation/scripts/simulate_openmm_system.py5.2 KB
  • sub-skills/system-preparation/SKILL.md3.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Coronavirus repository skill

Use this repo-specific operating skill for reusable work derived from the Folding@home coronavirus preparation repository. It covers structure curation, OpenMM preparation/equilibration mechanics, and project-context notes. It does not run Folding@home clients, network acquisition, long production trajectories, or medical/therapeutic interpretation.

Route first

  1. Need to inspect, select, truncate, or rename a PDB/FASTA/SDF input? Read structure-curation. Validate before changing anything and use its explicit-output helpers.
  2. Need solvent, ions, force fields, minimization, bounded equilibration, XML/PDB serialization, or a 4/5 fs continuation? Read system-preparation. Begin from a validated topology and use CPU OpenMM as the required baseline.
  3. Need a potential-target or publication note, evidence status, source citation, or structural provenance record? Read project-context. Keep source evidence, hypotheses, and preparation observations separate.
  4. Need a cross-cutting diagnosis? Read troubleshooting.md, then the nearest sub-skill troubleshooting reference. Run check_openmm_env.py only for environment/platform diagnostics.

Integrated operating sequence

For a new simulation input, establish source-structure identity and note provenance first; curate and validate the coordinate topology; choose and document the force-field/ligand parameter path; prepare a small CPU OpenMM system; run only a bounded minimization/equilibration smoke check; inspect energy, atom count, box, and output bundle; then record exact parameters, versions, warnings, and limitations. A later Researcher may expand the run only with an explicit scientific and compute budget.

Do not treat a filename, a successful PDB parse, a short CPU step, or XML serialization as proof of biological identity, force-field suitability, stable long-timestep dynamics, binding, efficacy, or reproducibility across platforms.

Repository-specific conventions

The source project documents rapidly prepared inputs and commonly uses equilibrated_4fs.pdb as the Folding@home handoff. Reusable generated helpers use safer explicit arguments and short defaults instead of historical hard-coded paths or multi-nanosecond defaults. A 4 amu hydrogen mass and 4/5 fs continuation are historical workflow conventions that must be disclosed and validated for the actual system, not universal recommendations.

The required backend for this selected scope is CPU OpenMM. CUDA is optional: platform enumeration succeeded in inspection, but context creation was limited by CUDA_ERROR_UNSUPPORTED_PTX_VERSION. Do not claim CUDA verification or require it for a CPU result.

Handoffs

Use repo-provenance.md to anchor claims to the source revision and evidence paths. This generated skill is intentionally left in the repository-local output tree and is not imported into the managed DisCo skill library.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

3ddfa

Free

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

acme

Free

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill