본문으로 건너뛰기
cccskills
무료GitHub에 공개

crewai

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(61)

  • SKILL.md5.4 KB
  • references/installation-and-dependencies.md4.3 KB
  • references/repo-provenance.md3.0 KB
  • references/repo-routing-metadata.json489 B
  • references/troubleshooting.md4.1 KB
  • scripts/check_crewai_environment.py4.1 KB
  • sub-skills/cli-and-projects/references/cli-reference.md8.2 KB
  • sub-skills/cli-and-projects/references/deployment.md8.2 KB
  • sub-skills/cli-and-projects/references/project-templates.md10.0 KB
  • sub-skills/cli-and-projects/references/troubleshooting.md8.0 KB
  • sub-skills/cli-and-projects/scripts/inspect_crewai_cli.py9.2 KB
  • sub-skills/cli-and-projects/SKILL.md3.2 KB
  • sub-skills/core-runtime/references/api-reference.md9.0 KB
  • sub-skills/core-runtime/references/jsonc-projects.md7.5 KB
  • sub-skills/core-runtime/references/troubleshooting.md9.0 KB
  • sub-skills/core-runtime/references/workflows.md9.4 KB
  • sub-skills/core-runtime/scripts/validate_crew_definition.py15.1 KB
  • sub-skills/core-runtime/SKILL.md2.9 KB
  • sub-skills/files-and-multimodal/references/file-inputs.md7.4 KB
  • sub-skills/files-and-multimodal/references/provider-constraints.md7.1 KB
  • sub-skills/files-and-multimodal/references/troubleshooting.md7.9 KB
  • sub-skills/files-and-multimodal/references/uploaders-and-cache.md6.6 KB
  • sub-skills/files-and-multimodal/scripts/check_file_inputs.py20.1 KB
  • sub-skills/files-and-multimodal/SKILL.md4.0 KB
  • sub-skills/flows-and-events/references/api-reference.md8.8 KB
  • sub-skills/flows-and-events/references/event-hooks.md8.5 KB
  • sub-skills/flows-and-events/references/flow-patterns.md9.6 KB
  • sub-skills/flows-and-events/references/troubleshooting.md10.7 KB
  • sub-skills/flows-and-events/scripts/validate_flow_graph.py15.4 KB
  • sub-skills/flows-and-events/SKILL.md2.5 KB
  • sub-skills/llm-and-providers/references/custom-llm.md7.4 KB
  • sub-skills/llm-and-providers/references/provider-reference.md10.9 KB
  • sub-skills/llm-and-providers/references/streaming-and-tool-calls.md7.5 KB
  • sub-skills/llm-and-providers/references/troubleshooting.md7.6 KB
  • sub-skills/llm-and-providers/scripts/check_llm_config.py10.7 KB
  • sub-skills/llm-and-providers/SKILL.md3.6 KB
  • sub-skills/memory-knowledge-and-rag/references/embedding-and-storage.md9.2 KB
  • sub-skills/memory-knowledge-and-rag/references/memory-and-knowledge.md10.5 KB
  • sub-skills/memory-knowledge-and-rag/references/rag-loaders.md9.6 KB
  • sub-skills/memory-knowledge-and-rag/references/troubleshooting.md10.6 KB
  • sub-skills/memory-knowledge-and-rag/scripts/check_rag_imports.py8.1 KB
  • sub-skills/memory-knowledge-and-rag/SKILL.md3.8 KB
  • sub-skills/observability-and-hooks/references/hooks-reference.md10.1 KB
  • sub-skills/observability-and-hooks/references/integrations.md9.6 KB
  • sub-skills/observability-and-hooks/references/tracing-reference.md7.8 KB
  • sub-skills/observability-and-hooks/references/troubleshooting.md12.6 KB
  • sub-skills/observability-and-hooks/scripts/check_tracing_config.py7.4 KB
  • sub-skills/observability-and-hooks/SKILL.md3.5 KB
  • sub-skills/repo-development/references/contributor-guidance.md6.1 KB
  • sub-skills/repo-development/references/docs-maintenance.md6.1 KB
  • sub-skills/repo-development/references/testing.md7.2 KB
  • sub-skills/repo-development/references/troubleshooting.md6.5 KB
  • sub-skills/repo-development/scripts/select_native_tests.py14.6 KB
  • sub-skills/repo-development/SKILL.md3.8 KB
  • sub-skills/tools-and-mcp/references/custom-tools.md8.1 KB
  • sub-skills/tools-and-mcp/references/mcp-reference.md8.2 KB
  • sub-skills/tools-and-mcp/references/optional-dependencies.md5.6 KB
  • sub-skills/tools-and-mcp/references/tool-catalog.md7.4 KB
  • sub-skills/tools-and-mcp/references/troubleshooting.md7.4 KB
  • sub-skills/tools-and-mcp/scripts/list_tool_exports.py6.8 KB
  • sub-skills/tools-and-mcp/SKILL.md3.3 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

CrewAI

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.

Start Here

  • Read Repository provenance before deciding whether this skill matches a current CrewAI checkout or needs refresh.
  • Read Installation and dependencies when choosing package extras, optional dependencies, Python versions, or inspection commands.
  • Read Troubleshooting for cross-cutting install/import, optional dependency, credentials, CLI/project-root, telemetry, and repo-development failures.
  • Run check_crewai_environment.py for a safe local import/version/CLI diagnostic; it does not run crews, call LLMs, start MCP servers, or use credentials.

Route by Task

  • Use core-runtime for Agent, Task, Crew, Process, kickoff modes, outputs, guardrails, callbacks, planning, reasoning, checkpoint basics, JSONC crew definitions, and direct-code crew design.
  • Use flows-and-events for Flow, @start, @listen, @router, state, routing labels, plotting, persistence, checkpointing, human feedback, event listeners, and event ordering.
  • Use cli-and-projects for crewai create, JSON-first and classic project scaffolds, crewai run, train, test, replay, chat, deploy, uv, project templates, and CLI troubleshooting.
  • Use tools-and-mcp for official crewai_tools, custom BaseTool or CrewStructuredTool work, MCP adapters, tool publishing, integration tools, optional packages, and credential boundaries.
  • Use memory-knowledge-and-rag for Memory, Knowledge, knowledge_sources, RAG loaders, embedding providers, vector stores, RagTool, and reset-memories behavior.
  • Use llm-and-providers for 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.
  • Use observability-and-hooks for tracing, telemetry, observability providers, event listeners, LLM/tool hooks, kickoff hooks, output logs, task outputs, and security fingerprints.
  • Use files-and-multimodal for crewai-files, input_files, file source resolution, provider file constraints, multimodal agents, upload cache, and file/document tool adjacency.
  • Use repo-development only for contributing to this CrewAI checkout: focused tests, docs versioning, Edge docs edits, frozen snapshot rules, workspace metadata, and safe native verification selection.

Common Decisions

  • Prefer JSON-first projects from crewai create crew <name> unless the user is maintaining an existing classic Python/YAML crew or needs decorator-heavy customization.
  • Treat custom:<name> tools and {"python": "module.attribute"} JSONC references as trusted-code boundaries; inspect before running projects from untrusted sources.
  • Do not run LLM-backed crews, hosted deploy/login commands, MCP servers, network-backed tools, or credential-bound integrations just to inspect a project. Use the bundled diagnostic scripts first.
  • Use provider-specific sub-skills for optional dependency and credential choices instead of installing all extras or assuming all integrations are available.
  • For repo docs edits, modify only docs/edge/<lang>/...; do not edit docs/v*/ frozen snapshots or delete/rename docs/images/ assets outside release-freeze work.

Safe Diagnostics

  • 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.

Refresh Triggers

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.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa

무료

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa-v2

무료

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

ab3dmot

무료

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

acme

무료

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

VectorSpaceLab의 스킬 모두 보기

이 스킬의 문제 신고