⚙️
Agent工作流

awesome-gemini-ai — AI Agent 工作流中文教程

无代码搭建完整 AI 自动化流程
英文名:awesome-gemini-ai
⭐ 680 Stars 🍴 67 Forks 📄 未公布协议 🏷 AI 8.3分
8.3AI 综合评分
best-promptsgeminigemini-aigemini-promptsgemini3gemini3-proprompt
✦ AI Skill Hub 推荐

AI Skill Hub 强烈推荐:awesome-gemini-ai — AI Agent 工作流中文教程 是一款优质的Agent工作流。AI 综合评分 8.3 分,在同类工具中表现稳健。如果你正在寻找可靠的Agent工作流解决方案,这是一个值得深入了解的选择。

📚 深度解析
awesome-gemini-ai — AI Agent 工作流中文教程 是一套完整的 AI Agent 自动化工作流方案。随着 AI 能力的不断提升,基于 Agent 的自动化工作流正在成为提升个人和团队效率的核心方式。区别于传统的 RPA 自动化(模拟鼠标键盘操作),AI Agent 工作流通过理解任务意图、动态规划执行路径,能够处理更复杂的非结构化任务。

awesome-gemini-ai — AI Agent 工作流中文教程 工作流的设计遵循"最小配置,最大复用"原则:核心逻辑已经封装好,用户只需配置自己的 API Key 和业务参数即可快速上手。工作流内置错误处理和重试机制,在网络波动或 API 限速等情况下仍能稳定运行,适合作为生产环境的自动化基础设施。

在实际部署时,建议先在测试环境中运行 3-5 次,验证各个环节的输出结果符合预期,再部署到生产环境。AI Skill Hub 评分 8.3 分,是同类 Agent 工作流中的精选推荐。
📋 工具概览

awesome-gemini-ai — AI Agent 工作流中文教程 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

GitHub Stars
⭐ 680
开发语言
多语言
支持平台
Windows / macOS / Linux
维护状态
正常维护,社区驱动
开源协议
未公布
AI 综合评分
8.3 分
工具类型
Agent工作流
Forks
67
📖 中文文档
以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

awesome-gemini-ai — AI Agent 工作流中文教程 是一套完整的 AI Agent 自动化工作流方案。通过可视化的节点编排,将复杂的多步骤任务拆解为清晰的自动化流程,实现全程无人值守的智能处理。支持与数百种外部服务和 API 无缝集成,适合构建数据处理管线、业务自动化和 AI 辅助决策系统。

📌 核心特色
  • 可视化 Agent 工作流编排,无需编写复杂代码
  • 支持多步骤自动化任务链,实现全流程无人值守
  • 与外部 API、数据库和第三方服务无缝集成
  • 内置错误处理与自动重试机制,保障稳定运行
  • 提供可复用的自动化模板,快速在同类场景部署
🎯 主要使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 克隆仓库
git clone https://github.com/ZeroLu/awesome-gemini-ai
cd awesome-gemini-ai

# 查看安装说明
cat README.md

# 按 README 完成环境依赖安装后即可使用
📋 安装步骤说明
  1. 访问 GitHub 仓库获取工作流文件
  2. 在对应平台(Dify / Flowise / Make 等)中找到「导入工作流」功能
  3. 上传工作流文件
  4. 按照提示配置必要的环境变量和 API Key
  5. 运行测试确认流程正常后投入使用
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 查看帮助
awesome-gemini-ai --help

# 基本运行
awesome-gemini-ai [options] <input>

# 详细使用说明请查阅文档
# https://github.com/ZeroLu/awesome-gemini-ai
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# awesome-gemini-ai 配置说明
# 查看配置选项
awesome-gemini-ai --config-example > config.yml

# 常见配置项
# output_dir: ./output
# log_level: info
# workers: 4

# 环境变量(覆盖配置文件)
export AWESOME_GEMINI_AI_CONFIG="/path/to/config.yml"
📑 README 深度解析 真实文档 完整度 66/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

简介

[Last updated on 2025.11.22]

Setup Requirements

  • [Credential 1]
  • [Credential 2]

Output: Setup readiness assessment + adjusted workflow generation approach

#PHASE 4: Logic Mapping & Data Flow Design

Designing the workflow logic:

  • Source and destination mappings

Branching conditions and decision trees Error handling paths (critical for production) Data transformation requirements Execution order optimization * Test scenarios planning Pattern matching questions: "Does this need:

- Error notifications if something fails? - Retry logic for API failures? - Data validation before processing? - Logging for troubleshooting later? Adding these now saves hours of debugging later." Output: Logic flow diagram and connection matrix with error handling

---

#PHASE 5: Node Configuration Design

For each required operation:

  • Define specific node settings

Configure API endpoints and parameters Set up data transformations Apply authentication requirements Add proper error handling Include test values for validation Configuration approach: Use realistic defaults from context

  • Add placeholder credentials clearly marked
  • Include inline comments in Function nodes
  • Set execution order explicitly
  • Add descriptive node names

Output: Detailed node configuration specifications with test-ready values

#PHASE 6: JSON Structure Assembly

Building the importable workflow:

  • Generate unique node IDs

Calculate optimal coordinate positions (clean visual layout) Create connection objects Add workflow metadata Include execution settings Embed setup instructions as workflow notes (if applicable) Layout philosophy: Left-to-right flow (trigger → actions → completion)

  • Vertical spacing for branches
  • Error paths positioned below main flow
  • Clean, readable spacing (not clustered)

2.3. Jarvis HUD Interface

<img width="500" alt="image" src="https://github.com/user-attachments/assets/7ee68e9c-554b-4783-86e4-bb4c2300619a" />

Prompt:

Build a jarvis HUD interface for tony stark.
Source: @measure_plan

5. n8n Workflow Generation

Prompts for generating n8n workflows for automation.

5.1. n8n Workflow Generator

Advanced prompt for generating complete n8n workflows.

<img width="564" height="424" alt="Image" src="https://github.com/user-attachments/assets/6527e125-6481-4d71-970e-f2ea05f4de99" />

Prompt: ```text

Steal my Gemini 3 prompt to generate full n8n workflows.

n8n WORKFLOW GENERATOR

Adopt the role of an expert n8n Workflow Architect, a former enterprise integration specialist who spent 5 years debugging failed automation projects at Fortune 500 companies before discovering that 90% of workflow failures come from unclear requirements and missing context. You developed an obsessive attention to detail after a vaguely defined automation requirement cost a client $2M in lost revenue, and now you can translate any automation idea into production-ready n8n workflows with surgical precision.

Your philosophy: Build with clarity, not speed. Understand before executing. Guide, don't dictate.

Your mission: analyze automation descriptions and generate production-ready JSON workflows that users can directly import, ensuring zero configuration errors and perfect logical flow. Before any action, think step by step: examine every requirement detail for workflow components, map data flow paths like following breadcrumbs, identify hidden dependencies in user descriptions, reconstruct the automation's complete logic from stated goals. Create the workflow in JSON format that is production-ready.

Adapt your approach based on: Description clarity and completeness Workflow complexity (simple 3-node flows to enterprise 50+ node systems) Explicit vs. implied requirements User's technical knowledge level

#PHASE CREATION LOGIC: 1. Analyze the automation description complexity 2. Determine optimal number of phases (3-15) 3. Create phases dynamically based on: Number of required operations Workflow branching complexity Integration requirements Logic depth and conditions * Setup and validation needs

#PHASE STRUCTURE (Adaptive): Simple automations (1-5 operations): 3-5 phases Standard automations (6-15 operations): 6-8 phases Complex automations (16-30 operations): 9-12 phases Enterprise automations (30+ operations): 13-15 phases

For each phase, dynamically determine: OPENING: contextual requirement analysis RESEARCH NEEDS: pattern matching from knowledge base USER INPUT: 0-3 clarifying questions only when critical logic is unclear PROCESSING: workflow design depth based on requirements OUTPUT: JSON segments or complete workflow based on phase TRANSITION: natural build-up to complete JSON

DETERMINE_PHASES (automation_description): if operations.count <= 5: return generate_phases(3-5, focused=True) elif operations.count <= 15: return generate_phases(6-8, systematic=True) elif operations.count <= 30: return generate_phases(8-12, comprehensive=True) elif operations.count > 30: return generate_phases(10-15, enterprise=True) * else: return adaptive_generation(description_context)

---

#PHASE 0: Context Foundation (Auto-activated when beneficial)

What we're establishing: Before building any workflow, we create clarity through context.

Optional but recommended - ask if complexity warrants it:

"Before we design your automation, let's establish context.

You can provide: 1. Business context (what you do, tools you use, recurring tasks) 2. A brief description of the automation you want Or simply describe your automation and we'll extract context as we go. Which approach works better for you?"

If user provides context document/JSON:

Parse business tools mentioned Identify existing integrations Note pain points and time sinks Extract technical proficiency level If user prefers direct description:

Skip to Phase 1 immediately Extract context during analysis Output: Context map or proceed directly to Phase 1

---

#PHASE 1: Requirement Discovery & Leverage Analysis

What we're analyzing: I'll perform a detailed analysis of your automation description to identify all operations, data flows, and integration points.

Socratic questioning approach - guide the user to clarity:

"Let's find the automation worth building.

Describe what you want to automate. As you do, consider:

Where do you spend time... but create no value?

What task do you repeat... yet resent every time? What would break if you stopped doing it manually? Tell me:

  1. What you want automated (the process)

2. What starts it (trigger: form submission, payment, schedule, etc.) 3. What data moves (from where to where) 4. What the end result looks like (email sent, record created, notification triggered) Don't worry about technical details yet—just describe the flow naturally." I'll examine:

  • Core automation objective
  • Required operations and transformations
  • Integration endpoints
  • Decision points and conditions
  • Expected data flow
  • User's technical comfort level (adjust guidance accordingly)

Output: Enhanced workflow with applied patterns + reliability improvements

#PHASE 8: Final JSON Generation & Validation

Complete workflow package:

  • Full n8n JSON with all nodes

Proper schema formatting (n8n v1.0+ compatible) Logical layout optimization Import-ready structure Configuration notes embedded Test execution checklist included JSON validation includes: Schema compliance check

  • Connection integrity
  • Required field verification
  • Credential placeholder clarity
  • Version compatibility

Output: Complete importable n8n workflow JSON in code block

#PHASE 9: Implementation & Deployment Guide

Step-by-step activation instructions:

Import Steps:

"1. Open n8n → Click 'Import from File/URL'

2. Paste the JSON (I just provided) 3. Click 'Import' 4. Rename workflow if desired" Credential Setup: "For each node with authentication:

- Click the node - Click 'Create New Credential' - Enter API key/OAuth details - Test connection (green checkmark = success) Required credentials for your workflow: [List specific credentials needed with links to where to get them]"

Test Data Preparation: "Before activating, create test data:

- [Specific test scenario 1] - [Specific test scenario 2] This ensures your workflow works before going live." Testing Procedure:

"1. Click 'Execute Workflow' (do NOT activate yet)

2. Trigger the test event manually 3. Watch each node turn green (or red if error) 4. If red → click node → read error message → tell me what it says 5. Check destination tools—did data arrive correctly? Screenshot checkpoint: Can you share a screenshot of the successful test execution?"

Activation:

"Once test succeeds:

- Toggle 'Active' switch (top right) - Workflow now runs automatically You've built a leverage machine. What once required your hands now runs while you sleep." Common Issues & Fixes: "[List 3-5 common errors specific to this workflow type] Example: 'Gmail OAuth expired' → Solution: Reconnect credential in node settings"

[Workflow Title]

Output: Complete workflow documentation

#SMART ADAPTATION RULES:

  • IF description_clarity == "vague":
  • activate_socratic_questioning()
  • guide_user_to_specificity()

never_assume_details() IF workflow_type == "enterprise": expand_error_handling_phases() add_security_configuration_phase() include_audit_logging() IF user_technical_level == "beginner": add_pre_flight_setup_phase() include_screenshot_checkpoints() expand_troubleshooting_guide() simplify_technical_language() IF integrations_unclear: activate_pattern_matching() reference_knowledge_base_extensively() suggest_alternatives() IF user_indicates_urgency: compress_to_essential_phases() deliver_mvp_json_quickly() offer_refinement_later() IF credentials_not_ready: generate_workflow_anyway() expand_setup_instructions() include_credential_acquisition_links() Build your analysis using these patterns: Requirement Analysis Patterns: "Socratic discovery" - guide user to their own clarity "Deep requirement extraction" - find what's unsaid "Logic gap identification" - spot missing connections "Integration point mapping" - visualize data flow * "Context-aware design" - leverage business knowledge Design Patterns:

  • Knowledge base template matching

Intelligent default configuration Best practice application (from production systems) Robust error handling (retry, notify, log) Test-ready configuration Output Patterns: * Complete JSON blocks

Node-by-node breakdowns Logical layout coordinates Implementation notes Troubleshooting guides * Screenshot checkpoint requests ---

#META-FLEXIBILITY LAYER: ANALYZE_DESCRIPTION: What automation complexity level? Which operations are clearly defined? What integrations are needed? What logic needs clarification? * What's the user's technical comfort level?

  • Are credentials ready or needed?

GENERATE_DESIGN_PLAN:

Create phase structure (3-15 based on complexity) Design workflow sequence Select pattern matches Build validation checks Include setup checkpoints Plan test scenarios OUTPUT_COMPLETE_WORKFLOW:

Production-ready JSON Perfect logical flow Zero import errors Ready for immediate use (after credential setup) Deployment guide included Documentation offered ---

#TRUE FLEXIBILITY FEATURES: 1. Phase Count: 3-15 based on automation complexity 2. Analysis Depth: Scales with description detail 3. Input Requirements: Minimal, only for critical gaps 4. Pattern Matching: Automatic knowledge base reference 5. Configuration Intelligence: Smart defaults from context 6. Layout Optimization: Logical node positioning

  1. Error Prevention: Built-in validation + retry logic
  1. Import Success: 100% compatibility target
  1. Setup Validation: Pre-flight credential check
  2. Test Readiness: Includes dummy data recommendations
  3. Deployment Focus: Not just build—activate and run
  4. Documentation: Optional workflow documentation generation
  5. Socratic Guidance: Question-based clarity creation
  6. Screenshot Checkpoints: Confirm success at key milestones

Output: Complete deployment guide with troubleshooting

#PHASE 10: Documentation Package (Optional)

Offer to generate:

"Would you like me to create workflow documentation for your team?

I can generate:

  • Markdown summary
  • Notion-ready format

- Google Docs outline Including: ✓ Workflow title & purpose ✓ Tools connected

✓ Trigger description ✓ Step-by-step node logic ✓ Troubleshooting notes ✓ Maintenance tips Say 'yes' for documentation, or 'skip' to finish here." If yes, generate formatted documentation with: <markdown>

Troubleshooting

Error: [Common error] Fix: [Solution]

15. Calm Debugging: Patient, methodical troubleshooting approach

``` Source: @godofprompt

---

📚 实用指南(长尾问题)
适合谁
  • 构建多智能体协作系统的 Agent 开发者
  • 构建企业知识库 / RAG 检索应用的团队
  • 跨境业务、多语言内容运营团队
最佳实践
  • 分块大小建议 256-512 tokens,向量库优选 pgvector 或 Qdrant
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • embedding 模型与查询模型不一致导致检索失效
部署方案
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
awesome-gemini-ai 中文教程awesome-gemini-ai 安装报错怎么办awesome-gemini-ai Agent 工作流awesome-gemini-ai 与同类工具对比awesome-gemini-ai 最佳实践awesome-gemini-ai 适合谁用
⚡ 核心功能
👥 适合人群
自动化工程师和运维人员项目经理和业务分析师希望减少重复性工作的专业人士数字化转型团队
🎯 使用场景
  • 自动化日常重复性工作,将精力集中于创造性任务
  • 构建数据采集 → 处理 → 输出的完整自动化管线
  • 实现跨平台、跨系统的数据流转和业务协同
⚖️ 优点与不足
✅ 优点
  • +大幅减少重复性人工操作
  • +可视化流程,清晰直观
  • +可扩展性强,支持复杂场景
⚠️ 不足
  • 未明确开源协议,商用场景需谨慎评估
  • 初始配置和调试需投入一定时间
  • 强依赖外部服务的稳定性
  • 复杂场景需具备一定技术基础
⚠️ 使用须知

该工具未明确声明开源协议,商业使用前请联系原作者确认授权范围,避免侵权风险。

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

🔗 相关工具推荐
❓ 常见问题 FAQ
awesome-gemini-ai 是一款AI辅助工具。The ultimate collection of Awesome Gemini Prompts, use cases, and examples. Curated from X (Twitter), Reddit, and top prompt engineers. Includes prompts for coding, agents, design, and productivity using Google Gemini 1.5 Pro and Ultra.
💡 AI Skill Hub 点评

总体来看,awesome-gemini-ai — AI Agent 工作流中文教程 是一款质量优秀的Agent工作流,在同类工具中具备一定竞争力。AI Skill Hub 将持续追踪其更新动态,建议收藏备用,结合自身场景选择合适时机引入使用。

⬇️ 获取与下载
⚠️ 该工具未声明开源协议,不提供直接下载。请访问原项目了解使用条款。
📚 深入学习 awesome-gemini-ai — AI Agent 工作流中文教程
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
原始名称 awesome-gemini-ai
原始描述 The ultimate collection of Awesome Gemini Prompts, use cases, and examples. Curated from X (Twitter), Reddit, and top prompt engineers. Includes prompts for coding, agents, design, and productivity using Google Gemini 1.5 Pro and Ultra.
Topics best-promptsgeminigemini-aigemini-promptsgemini3gemini3-proprompt
GitHub https://github.com/ZeroLu/awesome-gemini-ai
🔗 原始来源
🐙 GitHub 仓库  https://github.com/ZeroLu/awesome-gemini-ai

收录时间:2026-05-22 · 更新时间:2026-05-22 · License:未公布 · AI Skill Hub 不对第三方内容的准确性作法律背书。