Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Route AutoGPT Platform self-hosting, backend, frontend, and legacy Classic agent work to focused operating guidance.
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Review the instructions the agent will receive before you install.
Use this skill for the AutoGPT monorepo. Start by deciding whether the task is for the current AutoGPT Platform or the unsupported Classic suite. Platform is the maintained self-hosted product; Classic is retained for educational and historical use and has known dependency/security limitations.
| If the task involves | Read next |
|---|---|
Docker Compose, .env setup, local services, self-host upgrade, ports, or local LLM configuration | platform-stack |
| FastAPI routes, graph execution, blocks, integrations, Prisma, model catalog, backend CLI, or Python tests | platform-backend |
| Next.js pages, Builder/Copilot/Library/Marketplace UI, generated API hooks, Tailwind/design system, or frontend tests | platform-frontend |
autogpt/serve CLI, Forge, workspace permissions, or direct-benchmark | classic-agents |
For a backend endpoint plus a UI consumer, begin with platform-backend, then
use platform-frontend for the generated hook and screen work. For local
end-to-end validation, also read platform-stack before starting services.
autogpt, forge, and
direct_benchmark. Treat it as unsupported and avoid presenting it as the
recommended path for new production work.Read repository map for package boundaries and common change locations. Read contributor guidance when a task will modify code or documentation. Read cross-cutting troubleshooting for setup, secrets, service, and version failures.
Use the package-specific sub-skills for complete setup. Minimal public commands are:
cd autogpt_platform && make init-env && make start-core
cd autogpt_platform/backend && poetry install
cd autogpt_platform/frontend && corepack enable && pnpm install
cd classic && poetry install
Run only the commands needed for the chosen surface. Do not combine Platform backend and Classic dependencies into one Python environment unless a task explicitly asks for that experiment.
Run python scripts/autogpt_repo_probe.py --repo <checkout> to identify the
major AutoGPT surfaces and missing host tools without starting services or
editing the checkout. Use --json when another tool needs structured output.
This operating graph describes the checked source revision recorded in repo provenance. It does not provide secrets, managed-cloud access, external provider credentials, model downloads, or a promise that Classic dependencies are safe or maintained.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
The English summary is being prepared. Showing the original description.
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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