• Name / paper / upstream repo:
• Supported: bench_name=..., env_cfg_type=..., action_type=...
• Training support: full | eval-only (training release ETA: ...)

• install.sh (or upstream-native install, documented in the policy README)
• model.py (+ init.py)
• images: only decode_image_bit / encode_image_bit are supported (two byte formats → RGB), no channel swaps (see README)
• deploy.yml (standard key set incl. protocol: ws / host / port, policy_name matches the directory)
• deploy.py aligned with demo_policy (or divergence explained)
• eval.sh + setup_eval_policy_server.sh + setup_eval_env_client.sh
• process_data.sh / train.sh (or eval-only, declared above)
• policy README with install / data / train / eval commands

• bash -n + py_compile pass
• decode/encode grep: only decode_image_bit and encode_image_bit on XPolicyLab data
• EVAL_ENV_TYPE=debug closed loop passes (paste the log tail)
• Simulator eval: task=..., success=... (if available)

<download script, Hugging Face or ModelScope preferred>

...