Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Routes AppAgent setup, exploration, and deployment workflows for Android-device task automation.
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See how to installReview the instructions the agent will receive before you install.
AppAgent operates Android apps through ADB, screenshot/XML capture, multimodal model calls, and generated UI documentation.
Use this skill when the task is about:
references/setup.md for Python, adb, device/emulator, and config prerequisites.references/configuration.md for the config.yaml keys and backend selection rules.references/workflows.md for the high-level exploration/deployment output layout.references/troubleshooting.md for the most common failures and recovery steps.references/api-reference.md when you need exact helper signatures or parser behavior.Use the bundled checker before launching a real device session:
scripts/check_setup.py --repo-root <AppAgent checkout>scripts/check_setup.py --repo-root <AppAgent checkout> --skip-adb when you only want Python/config validation.The checker is read-only. It confirms imports, config keys, and whether adb is on PATH.
Use the setup references and scripts/check_setup.py when the request is about:
config.yaml,apps/ and tasks/ folders should live.Use sub-skills/exploration/SKILL.md when the request is about:
apps/<app>/auto_docs/ or apps/<app>/demo_docs/.The exploration launcher lives at:
sub-skills/exploration/scripts/start_exploration.pyUse sub-skills/deployment/SKILL.md when the request is about:
The deployment launcher lives at:
sub-skills/deployment/scripts/start_deployment.pyadb.apps/ and tasks/ relative to the chosen root_dir.references/troubleshooting.md first.tasks/ or apps/<app>/demos/ tree.No reviews yet. Tell others what you thought.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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