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camel-ai

Use CAMEL-AI to build, configure, tool, remember, evaluate, and troubleshoot multi-agent Python systems with self-contained routing across agents, models, tools, RAG, and evaluation workflows.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

VectorSpaceLab/AREX-Skill

연동 도구Docker

설치 방법 보기

포함된 파일(36)

SKILL.md3.8 KB
references/package-overview.md2.3 KB
references/repo-provenance.md1.5 KB
references/repo-routing-metadata.json480 B
references/troubleshooting.md2.6 KB
scripts/inspect_camel_install.py3.2 KB
sub-skills/agents-and-societies/references/api-reference.md10.7 KB
sub-skills/agents-and-societies/references/troubleshooting.md8.3 KB
sub-skills/agents-and-societies/references/workflows.md10.5 KB
sub-skills/agents-and-societies/scripts/inspect_agent_basics.py6.1 KB
sub-skills/agents-and-societies/SKILL.md3.5 KB
sub-skills/datagen-evaluation-and-benchmarks/references/api-reference.md5.8 KB
sub-skills/datagen-evaluation-and-benchmarks/references/benchmarks.md4.6 KB
sub-skills/datagen-evaluation-and-benchmarks/references/troubleshooting.md5.1 KB
sub-skills/datagen-evaluation-and-benchmarks/references/workflows.md5.1 KB
sub-skills/datagen-evaluation-and-benchmarks/scripts/inspect_eval_components.py7.2 KB
sub-skills/datagen-evaluation-and-benchmarks/SKILL.md2.4 KB
sub-skills/memory-rag-and-data/references/api-reference.md9.3 KB
sub-skills/memory-rag-and-data/references/data-workflows.md7.1 KB
sub-skills/memory-rag-and-data/references/storage-selection.md7.0 KB
sub-skills/memory-rag-and-data/references/troubleshooting.md7.5 KB
sub-skills/memory-rag-and-data/references/workflows.md7.8 KB
sub-skills/memory-rag-and-data/scripts/inspect_rag_components.py9.9 KB
sub-skills/memory-rag-and-data/SKILL.md3.2 KB
sub-skills/models-and-configuration/references/api-reference.md7.3 KB
sub-skills/models-and-configuration/references/configuration.md6.0 KB
sub-skills/models-and-configuration/references/provider-patterns.md5.9 KB
sub-skills/models-and-configuration/references/troubleshooting.md7.3 KB
sub-skills/models-and-configuration/scripts/inspect_model_registry.py6.3 KB
sub-skills/models-and-configuration/SKILL.md3.2 KB
sub-skills/tools-runtimes-and-services/references/api-reference.md6.6 KB
sub-skills/tools-runtimes-and-services/references/runtime-service-patterns.md7.2 KB
sub-skills/tools-runtimes-and-services/references/toolkit-catalog.md5.4 KB
sub-skills/tools-runtimes-and-services/references/troubleshooting.md6.5 KB
sub-skills/tools-runtimes-and-services/scripts/inspect_tool_schema.py3.2 KB
sub-skills/tools-runtimes-and-services/SKILL.md2.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

CAMEL-AI Repo Skill

Use this repo skill when a task mentions CAMEL-AI, camel-ai, the camel Python package, ChatAgent, RolePlaying, Workforce, CAMEL toolkits, CAMEL model backends, CAMEL memory/RAG, or CAMEL benchmark/datagen/evaluation workflows.

First Checks

Install the base package with pip install camel-ai for core agents, models, messages, tools, and package imports.
Install only the optional extras needed for the chosen workflow, such as camel-ai[model_platforms], camel-ai[rag], camel-ai[storage], camel-ai[document_tools], camel-ai[web_tools], or camel-ai[dev_tools].
Run scripts/inspect_camel_install.py for a no-network import, version, and route check.
Read references/troubleshooting.md before installing broad extras, debugging provider credentials, or running network/Docker/browser/benchmark workflows.

Route By Task

Task or signal	Read
Build a `ChatAgent`, system/user/assistant messages, task tree, role-playing society, or `Workforce`	sub-skills/agents-and-societies/SKILL.md
Configure model providers, `ModelFactory`, `ModelManager`, provider enums, local/OpenAI-compatible endpoints, structured output, audio, or multimodal models	sub-skills/models-and-configuration/SKILL.md
Attach `FunctionTool`, built-in toolkits, MCP/OpenAPI tools, code execution, interpreters, runtimes, browser/Docker/terminal tools, or CAMEL services	sub-skills/tools-runtimes-and-services/SKILL.md
Add memory, retrieval/RAG, embeddings, vector/key-value/object/graph stores, loaders, datahubs, or datasets	sub-skills/memory-rag-and-data/SKILL.md
Generate synthetic data, use data collectors, run verifiers/environments, or plan CAMEL benchmarks	sub-skills/datagen-evaluation-and-benchmarks/SKILL.md

Cross-Cutting References

references/package-overview.md summarizes package surfaces, optional extras, and support modules such as prompts, personas, schemas, responses, caches, parsers, configs, and utilities.
references/troubleshooting.md covers install/import failures, optional dependencies, credentials, data/config validation, and workflow-specific escalation.
references/repo-provenance.md records the source commit, package version, evidence paths, and dirty-state baseline used to create this skill.
references/repo-routing-metadata.json is structured metadata for the managed repo-skills-router import process.

Safety And Scope

Start with credential-free inspection scripts and constructor/signature checks before calling models, services, datasets, browsers, Docker, or remote vector stores.
Do not install camel-ai[all] by default in automated environments; select extras by workflow to avoid heavyweight or unsupported optional dependencies.
Treat source examples and native tests as evidence; this skill is self-contained and does not require the original repository checkout at runtime.
Keep API keys, service URLs with secrets, OAuth tokens, model credentials, and local data paths outside generated prompts and skill files.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

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

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

같은 저장소의 스킬

3d-resnets-pytorch

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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.

VectorSpaceLab/AREX-Skill3392026년 9월 3일 업데이트

3ddfa

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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

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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

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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

accelerate

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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.

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

설치

명령으로 설치

npx skills add https://github.com/VectorSpaceLab/AREX-Skill --skill camel-ai

터미널에 붙여 넣고 실행합니다(Node.js 필요). Claude Code, Codex 등 설치할 에이전트를 고를 수 있습니다.

에이전트에게 부탁하기

다음 Agent Skill을 설치해 주세요.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/camel-ai

먼저 SKILL.md와 첨부 파일의 내용을 확인해 수상한 처리(외부 전송, 인증 정보 읽기 등)가 없는지 확인한 뒤 Claude Code는 ~/.claude/skills/camel-ai/, Codex는 ~/.agents/skills/camel-ai/ 에 배치해 주세요.

이 문장을 그대로 Claude Code나 Codex에 보내면 내용을 확인한 뒤 설치해 줍니다.

GitHub에서 소스 보기

저장소: VectorSpaceLab/AREX-Skill
스타: 339
포크: 26
카테고리: 데이터·AI
라이선스: Apache-2.0
원문 언어: 영어
최종 업데이트: 2026년 9월 3일

스킬은 에이전트를 위한 지시와 스크립트의 묶음입니다. cccskills는 내용의 안전성을 보증하지 않습니다. 설치하기 전에 내용을 확인하세요.