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mcp/skillhub

Agent Skill

Kermt Embed skill

kermt-embedNVIDIA/skillsskills/bionemo-kermt-embed/SKILL.md

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.

3.7kApache-2.0

always on

71 tokname + description

once used

1,888 tokSKILL.md body

bundled files

147 scripts

repo stars

3.7kGitHub

Install the Kermt Embed skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/NVIDIA/skills --skill kermt-embed

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/NVIDIA/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -R /tmp/skills/skills/bionemo-kermt-embed ~/.claude/skills/kermt-embed

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description71 tok
When the agent uses itThe SKILL.md instructions1,888 tok
Only if opened14 bundled files (146 KB)varies

Until it is used, kermt-embed costs about 19× less than the median MCP server we measured (1,363 tokens on every turn). Installing all 150 skills from NVIDIA/skills would load 20,796 tokens of descriptions. Skills vs MCP study

Checks

  • Ships scripts the agent can run. Read them before installing.
  • The name doesn't match its folder name, as the specification requires.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • kermt-embed
  • Skill and runtime paths
  • Downloads and local outputs
  • Hardware requirements
  • Inputs
  • Workflow
  • Hard rules
  • Common errors
  • Replayability

More skills from NVIDIA/skills

All skills in this repo
  1. accelerated-computing-cudfNVIDIA/skills

    Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

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  2. ambient-healthcare-agent-with-nemotron-voice-agentNVIDIA/skills

    Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

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  3. amc-run-rtsp-calibrationNVIDIA/skills

    Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

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  4. amc-run-sample-calibrationNVIDIA/skills

    Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

    3.7k164 tok idle · 3,627 in use
  5. amc-run-video-calibrationNVIDIA/skills

    Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

    3.7k157 tok idle · 4,576 in use
  6. amc-setup-calibration-stackNVIDIA/skills

    Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

    3.7k67 tok idle · 4,277 in use
  7. boltz2-nimNVIDIA/skills

    Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.

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  8. cdf-anomalygenNVIDIA/skills

    Use when running the CDF AnomalyGen pipeline over a defect dataset — mask placement, fine-tuning, synthetic defect-image generation (SDG), evaluation, quality refinement, and pseudo-labeling — even when the user names only one stage.

    3.7k54 tok idle · 9,563 in use