getting-started 是做什么的？
CrewAI 架构决策与项目脚手架。用于启动新的 CrewAI 项目，选择 LLM.call()、Agent.kickoff()、Crew.kickoff() 或 Flow，通过 'crewai create flow' 搭建脚手架，配置 YAML（agents.yaml、tasks.yaml），编写 @CrewBase crew.py，编写带 @start/@listen 的 Flow main.py，或使用 {variable} 插值。
CrewAI 架构决策与项目脚手架。用于启动新的 CrewAI 项目，选择 LLM.call()、Agent.kickoff()、Crew.kickoff() 或 Flow，通过 'crewai create flow' 搭建脚手架，配置 YAML（agents.yaml、tasks.yaml），编写 @CrewBase crew.py，编写带 @start/@listen 的 Flow main.py，或使用 {variable} 插值。
该技能（crewaiinc/getting-started）为 CrewAI 的架构决策和项目脚手架提供全面指导。它帮助开发者在新项目启动时，根据需求选择适当的执行模式，如 LLM.call()、Agent.kickoff()、Crew.kickoff() 或 Flow，并支持使用 'crewai create flow' 命令快速生成项目骨架。技能详细介绍了 YAML 配置文件（agents.yaml、tasks.yaml）的设置方法，以及如何通过 @CrewBase 装饰器在 crew.py 中连接智能体和任务。此外，还覆盖了在 Flow main.py 中使用 @start/@listen 装饰器定义工作流，以及利用 {variable} 插值实现动态配置，从而加速开发过程并确保项目结构的一致性。
npx skills add https://github.com/crewaiinc/skills/tree/main/skills/getting-startedInstalled? Explore more 生产力与协作 skills: steipete/gemini, steipete/gh-issues, steipete/skill-creator · View all 6 →
CrewAI 架构决策与项目脚手架。用于启动新的 CrewAI 项目，选择 LLM.call()、Agent.kickoff()、Crew.kickoff() 或 Flow，通过 'crewai create flow' 搭建脚手架，配置 YAML（agents.yaml、tasks.yaml），编写 @CrewBase crew.py，编写带 @start/@listen 的 Flow main.py，或使用 {variable} 插值。
This repo contains 3 individual skills — each has its own dedicated page.
Query the official CrewAI documentation for answers. Use when the user has a CrewAI question that isn't fully covered by the getting-started, design-agent, design-task skills — e.g., specific API details, configuration options, advanced features, troubleshooting errors, enterprise features, tool references, or anything where the latest docs are the best source of truth.
CrewAI agent design and configuration. Use when creating, configuring, or debugging crewAI agents — choosing role/goal/backstory, selecting LLMs, assigning tools, tuning max_iter/max_rpm/max_execution_time, enabling planning/code execution/delegation, setting up knowledge sources, using guardrails, or configuring agents in YAML vs code.
CrewAI task design and configuration. Use when creating, configuring, or debugging crewAI tasks — writing descriptions and expected_output, setting up task dependencies with context, configuring output formats (output_pydantic, output_json, output_file), using guardrails for validation, enabling human_input, async execution, markdown formatting, or debugging task execution issues.
Gemini CLI one-shot prompts, summaries, generation, skills, hooks, MCP, or Gemma routing.
Fetch GitHub issues, select candidates, spawn background fix agents, open PRs, and optionally process PR review comments.
Create, edit, audit, tidy, validate, or restructure AgentSkills and SKILL.md files.
Search, install, update, sync, or publish agent skills with the ClawHub CLI and registry.
Delegate coding work to Codex, Claude Code, or OpenCode as background workers; not simple edits or read-only code lookup.
List, configure, authenticate, call, and inspect MCP servers/tools with mcporter over HTTP or stdio.