Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Routes Agent Starter Pack tasks to the right workflow for creating, maintaining, and deploying generated agent projects.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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.まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
日本語の概要は準備中です。原文の説明を表示しています。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
日本語の概要は準備中です。原文の説明を表示しています。
Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
日本語の概要は準備中です。原文の説明を表示しています。
Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
日本語の概要は準備中です。原文の説明を表示しています。