Brev instance operating guidance for NeMo-RL agents working in /home/ubuntu/RL with limited workspace disk, a larger /ephemeral volume, and optional /home/ubuntu/RL/.env secrets. Use when running nemo-rl-auto-research campaigns, experiments, training jobs, model or dataset downloads, shared cache-heavy commands, log-producing runs, checkpoint generation, W&B or Hugging Face authenticated workflows, or any workflow that may create large files on Brev.
npx skills add https://github.com/NVIDIA/skills --skill nemo-rl-brev-etiquette
Operate as though /home/ubuntu/RL is the source checkout and /ephemeral is the working storage for generated experiment state. Keep the repo small, reproducible, and easy to inspect. Move bulky run outputs to /ephemeral before launching anything expensive.
/home/ubuntu/RL./ephemeral, including checkpoints, run logs, Ray temp directories, W&B offline files, profiler traces, evaluation dumps, rollout samples, and per-experiment artifacts./ephemeral cache root per user, not under each experiment. This includes Hugging Face models, dataset caches, PyTorch caches, Triton caches, uv caches, and pip caches.df -h /home/ubuntu/RL /ephemeral and avoid starting if /ephemeral is missing or nearly full./ephemeral/nemo-rl/${USER:-ubuntu}/nemo-rl-auto-research/<campaign> and use one subdirectory per experiment./home/ubuntu/RL/.env as the local secret store. It may contain keys such as WANDB_API_KEY, HF_TOKEN, or HUGGING_FACE_HUB_TOKEN./home/ubuntu/RL/.env when it exists. Never print, cat, log, commit, or summarize secret values./home/ubuntu/RL/.env is absent, or a required key is still unset after loading it, remind the user to add the needed key to that file before launching authenticated work.if [ -f /home/ubuntu/RL/.env ]; then
set -a
. /home/ubuntu/RL/.env
set +a
else
echo "Missing /home/ubuntu/RL/.env; add required keys such as WANDB_API_KEY or HF_TOKEN before authenticated runs."
fi
When using nemo-rl-auto-research, keep the git ledger in the repo and heavy evidence on /ephemeral.
if [ -f /home/ubuntu/RL/.env ]; then
set -a
. /home/ubuntu/RL/.env
set +a
fi
BREV_ROOT=/ephemeral/nemo-rl/${USER:-ubuntu}
CACHE_ROOT=$BREV_ROOT/cache
CAMPAIGN_ROOT=$BREV_ROOT/nemo-rl-auto-research/<campaign>
EXP_DIR=$CAMPAIGN_ROOT/<experiment>
mkdir -p "$EXP_DIR"/{logs,checkpoints,artifacts,ray,tmp,wandb}
mkdir -p "$CACHE_ROOT"/{huggingface,torch,triton,uv,pip,xdg,wandb}
export HF_HOME=$CACHE_ROOT/huggingface
export HF_HUB_CACHE=$HF_HOME/hub
export HF_DATASETS_CACHE=$HF_HOME/datasets
export TRANSFORMERS_CACHE=$HF_HOME/transformers
export TORCH_HOME=$CACHE_ROOT/torch
export TRITON_CACHE_DIR=$CACHE_ROOT/triton
export UV_CACHE_DIR=$CACHE_ROOT/uv
export PIP_CACHE_DIR=$CACHE_ROOT/pip
export XDG_CACHE_HOME=$CACHE_ROOT/xdg
export WANDB_CACHE_DIR=$CACHE_ROOT/wandb
export RAY_TMPDIR=$EXP_DIR/ray
export TMPDIR=$EXP_DIR/tmp
export WANDB_DIR=$EXP_DIR/wandb
Record the absolute /ephemeral paths in the nemo-rl-auto-research TSV fields for log path, checkpoint path, artifacts, shared cache root, and command. If the TSV itself may grow large, store the full TSV in /ephemeral and keep a small pointer file or summary in the repo.
df -h /home/ubuntu/RL /ephemeral./ephemeral run root before editing recipes or launching jobs./ephemeral/nemo-rl/${USER:-ubuntu}/cache across experiments unless a run explicitly requires a clean cache.$EXP_DIR/logs/run.log or an equivalent file under /ephemeral.df -h /ephemeral and stop gracefully if the volume is approaching exhaustion./home/ubuntu/RL./ephemeral/nemo-rl/...; never remove shared caches or another user's run directory without an explicit instruction./ephemeral is cleaned.Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
Take nvidia/nemo-rl-brev-etiquette from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.