Replay a recorded HDF5 episode inside Isaac Sim for visual verification. Use when the user asks to replay, play back, or step through an HDF5 recording.
npx skills add https://github.com/NVIDIA/skills --skill i4h-workflow-dataset-replay
Replay a recorded HDF5 episode inside Isaac Sim for visual verification. Use when the user asks to replay, play back, or step through an HDF5 recording.
These steps drive the i4h-workflows base code (the workflows/agentic/ tree). To reuse an existing checkout, set I4H_WORKFLOWS to its path (no clone happens). Otherwise this resolves the current repo, or clones to ~/i4h-workflows — pick that default without prompting. Run every command below from the resolved root:
# Resolve the i4h-workflows base code (provides workflows/agentic/).
ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"
if [ ! -d "$ROOT/workflows/agentic" ]; then
ROOT="${I4H_WORKFLOWS:-$HOME/i4h-workflows}"
[ -d "$ROOT/workflows/agentic" ] || git clone https://github.com/isaac-for-healthcare/i4h-workflows "$ROOT"
fi
export I4H_WORKFLOWS="$ROOT"; cd "$ROOT"
workflows/agentic/config/environments/<env>.yaml — the <env> scene, robot, and cameras Arena replays against.arena/run.sh --replay against the env that produced the HDF5.0, "second episode" -> 1, etc.Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.
REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/agentic" ] || REPO_ROOT="$HOME/i4h-workflows"
ENV_ID=scissor_pick_and_place
RUNS_ROOT="${REPO_ROOT}/workflows/agentic/runs"
EPISODE_INDEX="${EPISODE_INDEX:-0}" # For "Replay second episode", set EPISODE_INDEX=1.
# Point HDF5_PATH at a real recording (absolute path). Recordings come from teleop, mimic, or
# validate (which writes data/verify.hdf5 under each runs/eval_* dir). List candidates newest-first:
# find "${RUNS_ROOT}" -name '*.hdf5' -printf '%TY-%Tm-%Td %TH:%TM %p\n' | sort -r | head
HDF5_PATH="${HDF5_PATH:-}"
if [ ! -f "${HDF5_PATH}" ]; then
echo "replay: set HDF5_PATH to an existing .hdf5 (got '${HDF5_PATH:-<unset>}'). Candidates:" >&2
find "${RUNS_ROOT}" -name '*.hdf5' -printf '%TY-%Tm-%Td %TH:%TM %p\n' 2>/dev/null | sort -r | head
exit 1
fi
RUN_DIR="${RUNS_ROOT}/replay_${ENV_ID}_$(date +%Y%m%d_%H%M%S)"
mkdir -p "${RUN_DIR}/logs"
ln -sfn "${RUN_DIR}" "${RUNS_ROOT}/.latest"
"${REPO_ROOT}/workflows/agentic/arena/run.sh" \
--env "${ENV_ID}" \
--replay "${HDF5_PATH}" \
--episode-index "${EPISODE_INDEX}" \
2>&1 | tee "${RUN_DIR}/logs/replay.log"
HDF5_PATH must be an absolute path to an existing recording — --replay resolves a relative path against runs/<env>/, not your cwd, so a bare/relative path silently fails to load. The block lists real candidates if it's unset or wrong.data/verify.hdf5 under each runs/eval_* dir). There is no default demo.hdf5.--episode-index selects the episode within the HDF5 (zero-based).--episode-index 1..venv must exist).--episode-index..venv not found / replay fails to launch - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.replay: set HDF5_PATH to an existing .hdf5 (or recording fails to load) - Cause: HDF5_PATH unset or not a real file. Fix: pick an absolute path from the printed candidates (e.g. a verify.hdf5 under runs/eval_*/data/).--episode-index exceeds the episodes in the HDF5. Fix: use a valid zero-based index.--env differs from the env that produced the recording. Fix: use the same env id.Report env, HDF5 path, episode index, launch outcome, visible mismatches.
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/i4h-workflow-dataset-replay 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.