Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Route Airweave tasks to the most specific sub-skill: local-development, backend-api, source-connectors, frontend-dashboard, connect-widget, mcp-search, or monke-e2e.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill when a task is about the Airweave repository itself. It is a router, not a monolith: pick the narrowest sub-skill that matches the request, then cross-link to the others only when the task spans multiple surfaces.
Start with scripts/check_env.py when you need a quick repo sanity check.
| Task family | Use | Notes |
|---|---|---|
| Local Docker stack, env seeding, ports, start/restart/recreate/destroy | sub-skills/local-development/SKILL.md | Safe stack orchestration and health checks |
| Search, collections, source connections, webhooks, usage, browse-tree routes | sub-skills/backend-api/SKILL.md | API shapes, request/response contracts, stream debugging |
| Source classes, registry metadata, auth/config, browse-tree implementation, ACLs | sub-skills/source-connectors/SKILL.md | Connector internals only |
| Dashboard UI, auth context, collections pages, search UI, billing/admin | sub-skills/frontend-dashboard/SKILL.md | React/Vite client behavior |
| Connect iframe, postMessage, OAuth popup, session modes | sub-skills/connect-widget/SKILL.md | Widget and parent messaging only |
| MCP server, tool registration, stdio/HTTP transport, auth modes | sub-skills/mcp-search/SKILL.md | Streamable HTTP and CLI diagnostics |
| Monke connector discovery, configs, runner, credential resolution | sub-skills/monke-e2e/SKILL.md | Safe discovery helper; no credentialed test runs by default |
references/overview.md for the repo map and package layoutreferences/troubleshooting.md for cross-cutting failuresreferences/repo-provenance.md for the source snapshot used to build this skill treereferences/repo-routing-metadata.json for repo-skills-router placement아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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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