name: kermt-embed description: Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, canonical SMILES, and validity arrays to user-selected host directories. license: Apache-2.0 compatibility: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron. metadata: owner: [email protected] classification: workflow-skill risk_tier: skill
Line/token budget: targets ~150 lines / ~1800 tokens — within the
500-line / 5000-token cap for skill files.
kermt-embed
Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint.
The skill is the workflow orchestrator: validate ckpt, validate CSV, clean
SMILES, launch the runner blocking, return the per-readout .npy files.
Skill and runtime paths
Set SKILL_DIR to the absolute path of this installed skill directory. Export
KERMT_REPO as the absolute path to the KERMT checkout used for model
execution. The bundled container helper mounts that checkout at
/workspace and this skill at /skill (read-only). Commands inside
the container use ; defaults are bundled in .
See for checkpoint bundle requirements.