Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Use this skill for maintaining, migrating, debugging, and safely operating Microsoft AutoGen Python 0.7.x applications, including AgentChat, Core runtimes, extensions, Studio, Magentic-One, AG Bench, and pyautogen compatibility.
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설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this skill when the user is working with Microsoft AutoGen Python code, especially existing AutoGen 0.7.x applications, migration from older AutoGen APIs, AgentChat teams, Core runtimes, autogen-ext integrations, AutoGen Studio, Magentic-One, AG Bench, or pyautogen compatibility.
AutoGen is in maintenance mode. For brand-new greenfield agent applications, first recommend Microsoft Agent Framework. Continue with this skill when the user is maintaining current AutoGen systems, reproducing behavior, migrating existing code, or explicitly asks for AutoGen.
sub-skills/agentchat-workflows/ for high-level autogen_agentchat agents, teams, tools-as-agents, messages, termination, handoffs, streaming, state, serialization, and v0.2 migration.sub-skills/core-runtime/ for lower-level autogen_core routed agents, runtimes, message handlers, topics/subscriptions, tools, components, serialization, cancellation, intervention, and distributed runtime design.sub-skills/extensions-integrations/ for autogen_ext model clients, optional extras, MCP, code executors, memory/cache, surfer/helper agents, provider credentials, and gRPC extension setup.sub-skills/tools-studio-bench/ for AutoGen Studio, Magentic-One CLI, AG Bench, pyautogen, safe CLI checks, benchmark/service planning, and package version-boundary decisions.references/package-map.md for package names, install commands, version boundaries, and safe import checks.references/troubleshooting.md for cross-cutting install/import, async, optional dependency, credential, and migration failures.python scripts/inspect_autogen_install.py --json in the target environment to inspect installed AutoGen packages without network calls, provider credentials, Docker, Jupyter, Studio startup, or benchmark execution.autogen-core==0.7.5 provides foundational runtimes, routed agents, messages, components, model/tool interfaces, and serialization.autogen-agentchat==0.7.5 provides high-level agents, teams, termination, streaming, UI helpers, and depends on autogen-core==0.7.5.autogen-ext==0.7.5 provides optional integrations and is intentionally extra-driven; install only the extras needed for the workflow.pyautogen==0.10.0 is a compatibility/proxy package for current autogen-agentchat; pin old pyautogen~=0.2 only for legacy v0.2 code.agbench==0.0.1a1 exposes the agbench console script for benchmark configuration, linting, runs, and result tabulation.sub-skills/tools-studio-bench/references/compatibility.md before installing them into an existing environment.autogen-agentchat, autogen-core, and only the needed autogen-ext[...] extras.max_turns, cleanup (close(), stop(), stop_when_idle()), and isolated execution directories.From the root of this skill directory or after copying the script paths into a command:
python scripts/inspect_autogen_install.py --json
python sub-skills/agentchat-workflows/scripts/agentchat_smoke.py --mode signatures
python sub-skills/core-runtime/scripts/core_runtime_smoke.py --inspect
python sub-skills/extensions-integrations/scripts/inspect_extensions.py --json
python sub-skills/tools-studio-bench/scripts/inspect_tooling.py --json
These scripts inspect package metadata, imports, signatures, and help text only. They do not start provider calls, Studio, Docker, Jupyter, MCP servers, browsers, Redis/ChromaDB, or benchmarks by default.
references/package-map.md explains package names, install choices, optional extras, console scripts, and compatibility boundaries.references/troubleshooting.md covers cross-cutting failure diagnosis before routing to a focused sub-skill.references/repo-provenance.md records the source snapshot and evidence used to generate this skill.아직 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.
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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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