FreePublished on GitHub

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.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill

Works withDocker

See how to install

Files included (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 (original)

Review the instructions the agent will receive before you install.

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.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

More skills from this repository

3d-resnets-pytorch

Free

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

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

3ddfa

Free

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

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

3ddfa-v2

Free

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

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

ab3dmot

Free

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

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

accelerate

Free

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.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

acme

Free

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

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill

Install

Install with a command

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

Paste it into your terminal and run it (requires Node.js). You can choose which agents to install it for, such as Claude Code or Codex.

Ask your agent

Please install the following Agent Skill.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/camel-ai

First review SKILL.md and the bundled files and confirm there is nothing suspicious (such as sending data externally or reading credentials). Then place it in ~/.claude/skills/camel-ai/ for Claude Code, or ~/.agents/skills/camel-ai/ for Codex.

Send this text to Claude Code or Codex as is. It checks the contents before installing.

View source on GitHub

Repository: VectorSpaceLab/AREX-Skill
Stars: 339
Forks: 26
Category: Data & AI
License: Apache-2.0
Original language: English
Last updated: Sep 3, 2026

A skill is a set of instructions and scripts for an agent. cccskills does not guarantee that skills are safe. Review the contents before you install.