Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Routes Agent Starter Pack tasks to the right workflow for creating, maintaining, and deploying generated agent projects.
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Review the instructions the agent will receive before you install.
Use this repo skill for the agent-starter-pack Python package and CLI. It helps future agents choose templates, generate projects, maintain generated output, and wire projects into deployment and Gemini Enterprise workflows.
If you only need to confirm that the package is installed and importable, use the bundled helper first:
python scripts/check_agent_starter_pack.py
For a quick command discovery check, also run:
agent-starter-pack --help
| User intent | Go here |
|---|---|
| Create a new project, compare templates, or parse a remote template | sub-skills/project-scaffolding/SKILL.md |
| Enhance, extract, or upgrade an existing generated project | sub-skills/project-maintenance/SKILL.md |
| Set up CI/CD, deploy a generated project, inspect observability, configure data ingestion, or register with Gemini Enterprise | sub-skills/deployment-ops/SKILL.md |
references/package-overview.md for package identity, install guidance, and the command family map.references/cli-reference.md for the command-to-subskill mapping.references/template-catalog.md for the built-in agent/template list.references/troubleshooting.md for cross-cutting install, import, and CLI issues.references/repo-provenance.md for the source snapshot used to build this skill.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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