---

name: setup-and-data description: "Install dependencies, place pretrained VLM/checkpoints, and validate LIBERO/CALVIN/ALOHA data layouts." disable-model-invocation: true metadata: disco-role: operating license: MIT

Setup and Data (external-checkout adapter)

This is documentation and a read-only layout validator, not a self-contained runtime. Work against an absolute VLA_ADAPTER_REPO_ROOT containing the native VLA-Adapter checkout; install its vla-adapter distribution with python -m pip install -e "$VLA_ADAPTER_REPO_ROOT" so the import is prismatic. Keep pretrained VLM/config assets and native VLA checkpoints in that checkout or explicit external storage. LIBERO, CALVIN, and ALOHA data and their simulator/TFDS/ROS stacks are external prerequisites, not bundled here. Use this sub-skill when planning or validating installation, external data roots, pretrained model placement, or checkpoint layout before a native workflow.

Owns

• Base dependency stack from the project README and pyproject.toml
• our_envs.txt parity checks and version sanity notes
• pretrained_models/configs placement and the companion Prismatic VLM weights
• Local checkpoint directory shape and dataset_statistics.json
• LIBERO, CALVIN, and ALOHA dataset-root layout
• Storage planning for datasets, local model mirrors, and checkpoint outputs

Fast path

1. Read data and checkpoints.

2. Validate local paths with the bundled checker from the skill path (it does not install packages or run training/evaluation):

   python "$VLA_ADAPTER_SKILL_ROOT/sub-skills/setup-and-data/scripts/validate_data_layout.py" \
     --benchmark libero --data-root "$VLA_ADAPTER_REPO_ROOT/data/libero" \
     --checkpoint "$VLA_ADAPTER_REPO_ROOT/outputs/LIBERO-Spatial-Pro" \
     --vlm-config-dir "$VLA_ADAPTER_REPO_ROOT/pretrained_models/configs"
   

3. If you are using ALOHA offline loading, review the ALOHA notes in the reference before enabling local-model mutation.

4. If any path check fails, follow troubleshooting.

Route away

• Training command profiles, VRAM sizing, LoRA choices, and run scripts: training
• Benchmark rollouts, success-rate execution, and result interpretation: evaluation
• MsgPack / ROS server-client payloads and robot execution: deployment
• Package APIs, model loading internals, and conversion helpers: package-apis

Safety boundaries

• Do not download datasets, rewrite source files, or start training/evaluation.
• Treat the bundled checker as read-only: it only inspects local paths and prints PASS/WARN/FAIL.
• Keep all internal links within this skill subtree.