Run the full end-to-end agentic pipeline (record → mimic → annotate → replay → convert → visualize → finetune → validate). Use when asked to run the whole pipeline or do an e2e, smoke, or demo run.
npx skills add https://github.com/NVIDIA/skills --skill i4h-workflow-e2e
Run the full end-to-end agentic pipeline (record, mimic, annotate/filter, replay, convert, visualize, finetune, validate). Use when the user asks to run the full pipeline, smoke the whole workflow, demo the workflow, or do an e2e run.
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 "$ROOT"
workflows/agentic/config/environments/<env>.yaml — drives every stage for <env> (robot, task, policy, cameras, arena.max_timesteps, dataset.* mappings).assemble_trocar is inference-only; the e2e script skips finetune and checkpoint validation for it.REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/agentic" ] || REPO_ROOT="$HOME/i4h-workflows"
"${REPO_ROOT}/workflows/agentic/scripts/e2e/run.sh" --dry-run --env <env>
Run the steps below in order. Each step is a separate bash call; variables persist in the local agent's tmux session.
For Claude Code --print or any other noninteractive runner, keep Step 2 in the foreground. This is a validation requirement: do not use Claude background tasks, async task mode, Bash background mode, &, nohup, tmux, disown, or any detached process/task id, and do not answer that the pipeline is still running. Do not return until run.sh exits and you have inspected logs/SUMMARY.txt on success, or the failing stage log on failure. Report the run dir, skipped stages, per-stage status, key artifacts, and cleanup/stop status before finishing.
REPO_ROOT="${I4H_WORKFLOWS:-$(git rev-parse --show-toplevel 2>/dev/null)}"; [ -d "$REPO_ROOT/workflows/agentic" ] || REPO_ROOT="$HOME/i4h-workflows"
"${REPO_ROOT}/workflows/agentic/scripts/e2e/run.sh" --env <env>
--skip-mimic, --skip-annotate, --skip-replay, --skip-viz--from-stage <stage> --run-dir <existing-run> resumes from a prior run.ARENA_HEADLESS=1 before run.sh only when the user explicitly asks for headless/no-window execution.Stages: setup record mimic annotate replay convert viz finetune validate summary.
The script prints RUN_DIR and symlinks it to runs/.latest. Subdirs:
logs/ — per-stage logs, workflow.log (full teed output), and logs/SUMMARY.txt (the final summary report)data/lerobot/checkpoint/ (trainable envs only)run.sh runs every stage in the foreground and returns only when the whole pipeline ends, so track a long run from a separate shell (do not expect to query it from the shell that launched it):
tail -f "${REPO_ROOT}/workflows/agentic/runs/.latest/logs/workflow.log" # live per-stage progress
cat "${REPO_ROOT}/workflows/agentic/runs/.latest/logs/SUMMARY.txt" # final report (once DONE)
"${REPO_ROOT}/workflows/agentic/stop.sh" all --env <env>
.venv must exist); setup is also the first pipeline stage.--env name to drive the run.assemble_trocar is inference-only; the e2e script skips finetune and checkpoint validation for it.checkpoint/ outputs are produced for trainable envs only.--from-stage <stage> and --run-dir <existing-run>..venv not found / module import fails - Cause: workflow not set up. Fix: run [[i4h-workflow-setup]] first.--env name. Fix: pass a valid env name; dry-run first with --dry-run --env <env>.--from-stage used without a matching --run-dir. Fix: pass --from-stage <stage> --run-dir <existing-run>.stop.sh all --env <env> before retrying.Report env, run dir, skipped stages, per-stage success/failure, key artifact paths.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take nvidia/i4h-workflow-e2e 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.