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 agents using or contributing to CrewAI, including crews, flows, CLI projects, tools, MCP, memory, RAG, LLM providers, observability, files, multimodal inputs, and repo development.
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 when the task involves CrewAI, the crewai Python package, the crewai CLI, official CrewAI tools, CrewAI Flows, memory/knowledge/RAG, multimodal file inputs, observability hooks, or contributing to the CrewAI monorepo.
Agent, Task, Crew, Process, kickoff modes, outputs, guardrails, callbacks, planning, reasoning, checkpoint basics, JSONC crew definitions, and direct-code crew design.Flow, @start, @listen, @router, state, routing labels, plotting, persistence, checkpointing, human feedback, event listeners, and event ordering.crewai create, JSON-first and classic project scaffolds, crewai run, train, test, replay, chat, deploy, uv, project templates, and CLI troubleshooting.crewai_tools, custom BaseTool or CrewStructuredTool work, MCP adapters, tool publishing, integration tools, optional packages, and credential boundaries.Memory, Knowledge, knowledge_sources, RAG loaders, embedding providers, vector stores, RagTool, and reset-memories behavior.LLM, provider model strings, API keys, base URLs, Azure/OpenAI-compatible/Anthropic/Bedrock/Google/Snowflake settings, streaming, tool calls, response models, custom LLMs, and LiteLLM migration.crewai-files, input_files, file source resolution, provider file constraints, multimodal agents, upload cache, and file/document tool adjacency.crewai create crew <name> unless the user is maintaining an existing classic Python/YAML crew or needs decorator-heavy customization.custom:<name> tools and {"python": "module.attribute"} JSONC references as trusted-code boundaries; inspect before running projects from untrusted sources.docs/edge/<lang>/...; do not edit docs/v*/ frozen snapshots or delete/rename docs/images/ assets outside release-freeze work.python scripts/check_crewai_environment.py --json reports installed CrewAI package versions, top-level import results, and CLI command names.python sub-skills/core-runtime/scripts/validate_crew_definition.py --help explains static JSONC crew checks without importing project custom code.python sub-skills/flows-and-events/scripts/validate_flow_graph.py --help explains static flow graph checks without calling kickoff().python sub-skills/cli-and-projects/scripts/inspect_crewai_cli.py --help explains CLI/project layout checks that avoid running crews or deploy/login flows.python sub-skills/repo-development/scripts/select_native_tests.py --help suggests focused maintainer checks from changed paths without running them.Run refresh-repo-skill before relying on this skill if the current CrewAI checkout has a different commit than Repository provenance, package versions or CLI entry points changed, docs/edge/en/ workflows changed materially, or major source roots such as lib/crewai/src/crewai, lib/cli/src/crewai_cli, lib/crewai-tools/src/crewai_tools, or lib/crewai-files/src/crewai_files moved.
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。