Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV.
$ npx skills add NVIDIA/skills --skill kermt-infer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install NVIDIA/skills kermt-infer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bionemo-kermt-infer .claude/skills/kermt-infer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "kermt-infer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-kermt-infer into .claude/skills/kermt-infer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kermt-infer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/bionemo-kermt-inferType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add NVIDIA/skills --skill kermt-infer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install NVIDIA/skills kermt-infer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/bionemo-kermt-infer .agents/skills/kermt-infer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "kermt-infer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-kermt-infer into .agents/skills/kermt-infer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kermt-infer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill kermt-infer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install NVIDIA/skills kermt-infer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/bionemo-kermt-infer .cursor/skills/kermt-infer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "kermt-infer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-kermt-infer into .cursor/skills/kermt-infer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kermt-infer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/bionemo-kermt-infer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add NVIDIA/skills --skill kermt-infer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install NVIDIA/skills kermt-infer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/bionemo-kermt-infer .gemini/skills/kermt-infer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "kermt-infer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-kermt-infer into .gemini/skills/kermt-infer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kermt-infer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install NVIDIA/skills kermt-inferInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add NVIDIA/skills --skill kermt-infer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/bionemo-kermt-infer .github/skills/kermt-infer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "kermt-infer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-kermt-infer into .github/skills/kermt-infer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kermt-infer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add NVIDIA/skills --skill kermt-infer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install NVIDIA/skills kermt-infer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/bionemo-kermt-infer .opencode/skills/kermt-infer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "kermt-infer" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/bionemo-kermt-infer into .opencode/skills/kermt-infer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "kermt-infer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
kermt-inferRun predictions with a finetuned KERMT checkpoint on a SMILES-only CSV.
Kermt Infer is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit2d features), then launches main.py predict inside the kermt container (blocking, minutes-scale).
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 14 other files, including scripts (for example `BENCHMARK.md`, `config/defaults_inference.json` and `evals/evals.json`). Compatibility notes: Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron.
It sits in Research & Science, covering Drug discovery and cheminformatics, Fine-tuning and CSV and tabular files. It works with RDKit and CUDA. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.
7 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 67a13c0. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 6 files in scripts/ (Python and Shell), which the agent can run.
Shell commands in SKILL.md call:
jqgitFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron.
From compatibility in the SKILL.md frontmatter.
Kermt Infer loads about 1.5k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 559 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from NVIDIA/skills at commit 67a13c0, republished under its Apache-2.0 licence (© NVIDIA). 559 words, ~1,508 tokens.
.claude/skills/kermt-infer/SKILL.md (or your agent's skills folder). This skill also uses 11 other files; get the full folder from GitHub.Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill is the workflow orchestrator: validate ckpt, validate CSV, prepare data, launch the runner blocking, return the predictions CSV.
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 /skill/scripts/; defaults are bundled in config/.
batch_size 32.Required:
--ckpt <path> — finetuned checkpoint (must have task FFN heads). The
validator refuses pretrain ckpts with a redirect to kermt-finetune.--csv <path> — SMILES-only CSV. First column is smiles; other columns
are ignored.Optional:
--batch-size N — override the configured default (32).--seed N — random seed for inference (deterministic featurization paths).--gpus 0 — single GPU id (default 0). Multi-GPU rejected.--from-prepare <dir> — skip the prepare step and reuse an existing
prepare_data.json in <dir>.Let $KERMT_REPO be the path to your kermt repo checkout, and assume
kermt-setup has built kermt:latest.
Pre-flight: ensure container + system probe.
"$SKILL_DIR/scripts/kermt_container.sh" check_systemRefuse to proceed on ok: false.
Compute run directory.
RUN_DIR=$KERMT_REPO/runs/infer_$(date -u +%Y-%m-%dT%H-%M-%SZ)Validate the checkpoint.
"$SKILL_DIR/scripts/kermt_container.sh" run --ckpt <user-ckpt> -- \
"python /skill/scripts/check_checkpoint.py --mode inference --ckpt /ckpt"Parse the JSON. Abort on ok: false. The validator rejects pretrain ckpts
(has_task_ffn: false) with a redirect to kermt-finetune.
Validate the data.
"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> -- \
"python /skill/scripts/check_data.py --mode inference --csv /data/<basename>"Abort on ok: false.
Prepare the data.
"$SKILL_DIR/scripts/kermt_container.sh" run --data <user-csv> --run-dir $RUN_DIR -- \
"python /skill/scripts/prepare_data.py --mode inference \\
--csv /data/<basename> --out /runs/data"Outputs land at $RUN_DIR/data/prepare_data.json with clean_csv +
clean_npz paths (rdkit_2d_normalized features).
Launch the runner (blocking).
"$SKILL_DIR/scripts/kermt_container.sh" run \\
--ckpt <user-ckpt> --run-dir $RUN_DIR -- \\
"python /skill/scripts/run_inference.py \\
--ckpt /ckpt \\
--prepare-manifest /runs/data/prepare_data.json \\
--out /runs \\
[--gpus 0 --batch-size N --seed N]"Returns the predictions CSV path on success.
Report to the user. Output a short summary:
$RUN_DIR/out/predictions.csv (smiles + per-target columns)$RUN_DIR/run.json (cmd_replay + image digest + applied args)$RUN_DIR/logs/inference.log<N> molecules predicted across <K> targets<out>/ckpt_link/ subdir so main.py predict --checkpoint_dir
picks it up; the source file stays untouched.run.json but does not pass arch flags into
main.py predict — predict reads them from the loaded ckpt's saved_args.args_applied field of run.json records
every flag's value + source (user / default-config). Surface a short
summary of any default-filled flag.inference requires a finetuned ckpt with task FFN heads → ckpt is a
pretrain ckpt; use kermt-finetune first.prepare_data manifest reports ok=False → check the manifest errors for
the failed step (typically clean_smiles or save_features).could not convert string to float: '<value>' from save_features or main.py
predict → input CSV has a non-numeric passthrough column (e.g. a 'split'
label). The prep step now strips the CSV to SMILES-only at inference; if
this error still surfaces, the CSV is being read by a runner that bypassed
prepare_data. Re-run via the skill, not main.py directly.--gpus '0,1' is single-GPU only → pass a single id.The run.json cmd_replay field is a single-line command that re-runs the
inference with the same inputs. To replay inside the kermt container:
$(jq -r .cmd_replay $RUN_DIR/run.json)If ok_to_replay: false (dirty kermt repo worktree at launch time), pin
the commit via repo.commit and git checkout it first.
© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 11 other files (scripts) in skills/bionemo-kermt-infer of NVIDIA/skills.
Open the folder on GitHubat commit 67a13c0
We found 3 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in NVIDIA/skills, which our catalogue first saw on October 7, 2026.
Kermt Infer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Kermt Infer this skillNVIDIA/skills | 3.5k | 1 repos | ~1.5k | Automated safety check: Pass | Apache-2.0 | |
| Unimoljinzhezenggroup/computational-chemistry-agent-skills | 148 | 1 repos | ~1.5k | Automated safety check: Pass | LGPL-3.0-or-later | |
| DiffDock Molecular DockingK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3k | Automated safety check: Notes | MIT | |
| RDKit Descriptors and Fingerprintsjinzhezenggroup/computational-chemistry-agent-skills | 148 | 1 repos | ~2.3k | Automated safety check: Pass | LGPL-3.0 | |
| TorchdrugK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| Nvmolkit UsageNVIDIA-BioNeMo/bionemo-agent-toolkit | 478 | — | ~4.4k | Automated safety check: Pass | Apache-2.0 |
jinzhezenggroup/computational-chemistry-agent-skills
A standardized CLI wrapper for Uni-Mol molecular ML workflows that handles representation extraction (embeddings), model training (regression/classification), and property prediction with built-in…
K-Dense-AI/scientific-agent-skills
Predicts how small molecules bind to a protein with DiffDock, covering batch docking, pose ranking by confidence and checks on the results; not for binding affinity.
jinzhezenggroup/computational-chemistry-agent-skills
Computes RDKit physicochemical descriptors and molecular fingerprints from SMILES through a uv-run CLI script that skips and logs invalid molecules.
K-Dense-AI/scientific-agent-skills
Builds and troubleshoots TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and…
NVIDIA-BioNeMo/bionemo-agent-toolkit
Write code that calls the installed nvMolKit Python API for GPU-accelerated, batched RDKit-style operations - Morgan fingerprints, Tanimoto/cosine similarity, ETKDG conformer embedding, MMFF/UFF…
wy51ai/edulab
把一个化学反应做成自包含的微观 3D 交互演示网页：左/上为 Three.js 可交互分子动画 （拖滑块看断键·成键·原子重组，分步高亮），右为 KaTeX 反应方程 + 分步讲解 + 原子守恒计数 + 可选能量-反应进程曲线。支持三入口——给定文字反应/方程、随机出题、上传图片识别后演示。
NVIDIA/skills
A skill your agent uses when the user wants to deploy, run, debug, tear down, or call the REST API of the RTVI-CV 2D detection / tracking microservice.
NVIDIA/skills
Generates, validates, compares and explains HOLOLINK_def.svh macro files for the HSB IP, using bundled Python scripts and asking before it writes anything.
NVIDIA/skills
Runs and validates an end-to-end Mission Control demo in a locally installed Isaac Sim, with a Nova Carter robot driven through a Python server.
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
NVIDIA/skills
Orchestrates video data augmentation and auto-labeling workflows on OSMO, from flow selection and preflight checks to submission, monitoring and output download.
NVIDIA/skills
Runs NVIDIA TAO Data Services KPI analysis on object detection results, comparing predictions to ground truth and writing per-class precision, recall and AP to a CSV.
Categories
Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. Kermt Infer is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV.
Kermt Infer fits situations like: tasks that involve Drug discovery and cheminformatics; tasks that involve Fine-tuning; tasks that involve CSV and tabular files.
Run `npx skills add NVIDIA/skills --skill kermt-infer -a claude-code`. Or copy the skill folder (skills/bionemo-kermt-infer in NVIDIA/skills) into .claude/skills/kermt-infer in your project. Claude Code loads it when a task matches its description.
Run `npx skills add NVIDIA/skills --skill kermt-infer -a codex`. Or copy the skill folder (skills/bionemo-kermt-infer in NVIDIA/skills) into .agents/skills/kermt-infer in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill kermt-infer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kermt-infer, .gemini/skills/kermt-infer, .github/skills/kermt-infer and .opencode/skills/kermt-infer in your project.
Going by SKILL.md and its folder, Kermt Infer needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (jq and git). Our summary lists: Python 3; A Bash shell; Docker. Compatibility (from SKILL.md): Requires docker, nvidia-container-toolkit, and a CUDA-capable NVIDIA GPU. Designed for Claude Code, Codex, and Nemotron..
SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Kermt Infer is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 1.5k tokens (SKILL.md is roughly 6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Kermt Infer: Unimol (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars), RDKit Descriptors and Fingerprints (jinzhezenggroup/computational-chemistry-agent-skills, 148 stars) and Torchdrug (K-Dense-AI/scientific-agent-skills, 48k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,539 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.
Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.
