Back to Details

awesome-openclaw-usecases-discovery

Compare original and translation side by side

🇺🇸

Original

English
🇨🇳

Translation

Chinese

awesome-openclaw-usecases-discovery

OpenClaw优质用例探索库

Skill by ara.so — Hermes Skills collection.
This skill enables AI agents to discover, recommend, and help implement real-world use cases from the awesome-openclaw-usecases repository — a community-curated collection of 42+ production-tested OpenClaw automations across social media, creative workflows, DevOps, productivity, research, and finance.
由ara.so开发的Skill — 属于Hermes Skills集合。
该Skill能够让AI Agent发现、推荐并帮助实现来自awesome-openclaw-usecases仓库的真实世界用例——这是一个由社区精选的集合，包含42+经过生产环境测试的OpenClaw自动化方案，覆盖社交媒体、创意工作流、DevOps、生产力、研究和金融领域。

What It Covers

涵盖范围

The awesome-openclaw-usecases repository organizes battle-tested OpenClaw implementations into categories:
  • Social Media: Reddit/YouTube digests, X automation, multi-source news aggregation
  • Creative & Building: Autonomous task generation, content pipelines, game dev automation
  • Infrastructure & DevOps: n8n orchestration, self-healing servers
  • Productivity: Project management, multi-channel customer service, CRM, health tracking
  • Research & Learning: Knowledge bases, paper readers, semantic search
  • Finance & Trading: Prediction market automation
Each use case includes detailed implementation guides, skill requirements, and real-world patterns.
awesome-openclaw-usecases仓库将经过实战检验的OpenClaw实现方案划分为以下类别：
  • 社交媒体：Reddit/YouTube摘要、X平台自动化、多源新闻聚合
  • 创意与开发：自主任务生成、内容流水线、游戏开发自动化
  • 基础设施与DevOps：n8n编排、自修复服务器
  • 生产力：项目管理、多渠道客户服务、CRM、健康追踪
  • 研究与学习：知识库、论文阅读器、语义搜索
  • 金融与交易：预测市场自动化
每个用例都包含详细的实现指南、Skill要求和真实场景模式。

Installation

安装方式

This is a reference repository, not a package. Access it via:
bash
undefined
这是一个参考仓库，而非软件包。可通过以下方式访问：
bash
undefined

Clone the repository

Clone the repository

git clone https://github.com/hesamsheikh/awesome-openclaw-usecases.git cd awesome-openclaw-usecases
git clone https://github.com/hesamsheikh/awesome-openclaw-usecases.git cd awesome-openclaw-usecases

Browse use cases

Browse use cases

ls usecases/

Or view online at: https://github.com/hesamsheikh/awesome-openclaw-usecases
ls usecases/

或在线查看：https://github.com/hesamsheikh/awesome-openclaw-usecases

Repository Structure

仓库结构

awesome-openclaw-usecases/
├── usecases/
│   ├── daily-reddit-digest.md
│   ├── youtube-content-pipeline.md
│   ├── n8n-workflow-orchestration.md
│   ├── autonomous-project-management.md
│   ├── semantic-memory-search.md
│   └── ... (42+ use cases)
├── CONTRIBUTING.md
└── README.md
Each use case follows a standard format:
  • Overview: What it does and why
  • Skills Required: OpenClaw plugins/skills needed
  • Implementation: Step-by-step setup
  • Configuration: Environment variables, API keys
  • Examples: Real prompts and outputs
  • Tips & Troubleshooting: Common issues
awesome-openclaw-usecases/
├── usecases/
│   ├── daily-reddit-digest.md
│   ├── youtube-content-pipeline.md
│   ├── n8n-workflow-orchestration.md
│   ├── autonomous-project-management.md
│   ├── semantic-memory-search.md
│   └── ... (42+ use cases)
├── CONTRIBUTING.md
└── README.md
每个用例遵循标准格式：
  • 概述：功能与应用价值
  • 所需Skill：需要的OpenClaw插件/Skill
  • 实现步骤：分步搭建指南
  • 配置说明：环境变量、API密钥
  • 示例：真实提示词与输出结果
  • 技巧与故障排除：常见问题解决方法

Key Use Case Categories

核心用例类别

Social Media Automation

社交媒体自动化

markdown
undefined
markdown
undefined

Daily Reddit Digest

Daily Reddit Digest

Summarize curated subreddits based on preferences Skills: reddit-skill, summarization
根据偏好汇总精选子版块内容 Skills: reddit-skill, summarization

X/Twitter Automation

X/Twitter Automation

Post, reply, like, DM, search via TweetClaw plugin Skills: tweetclaw-plugin, scheduling
通过TweetClaw插件实现发帖、回复、点赞、私信、搜索功能 Skills: tweetclaw-plugin, scheduling

Multi-Source Tech News

Multi-Source Tech News

Aggregate from 109+ sources (RSS, X, GitHub, web) Skills: rss-reader, web-search, content-scoring
undefined
从109+来源（RSS、X、GitHub、网页）聚合内容 Skills: rss-reader, web-search, content-scoring
undefined

Creative Workflows

创意工作流

markdown
undefined
markdown
undefined

Goal-Driven Autonomous Tasks

目标驱动型自主任务

Brain dump → auto-generate tasks → build mini-apps overnight Skills: task-generation, autonomous-execution, git-integration
头脑风暴 → 自动生成任务 → 夜间构建迷你应用 Skills: task-generation, autonomous-execution, git-integration

YouTube Content Pipeline

YouTube内容流水线

Automate idea scouting, research, tracking Skills: youtube-api, notion-integration, scheduling
自动化创意挖掘、调研、追踪流程 Skills: youtube-api, notion-integration, scheduling

Multi-Agent Content Factory

多Agent内容工厂

Research + writing + thumbnail agents in Discord Skills: multi-agent, discord-integration, image-generation
undefined
Discord中的调研+写作+缩略图生成Agent协作 Skills: multi-agent, discord-integration, image-generation
undefined

Infrastructure & DevOps

基础设施与DevOps

markdown
undefined
markdown
undefined

n8n Workflow Orchestration

n8n工作流编排

Delegate API calls to n8n via webhooks (agent never touches creds) Skills: webhook-trigger, n8n-integration
通过Webhook将API调用委托给n8n（Agent无需接触凭证） Skills: webhook-trigger, n8n-integration

Self-Healing Home Server

自修复家庭服务器

Always-on infra agent with SSH, cron, self-healing Skills: ssh-access, cron-management, monitoring
undefined
具备SSH、定时任务、自修复能力的全天候基础设施Agent Skills: ssh-access, cron-management, monitoring
undefined

Productivity

生产力工具

markdown
undefined
markdown
undefined

Autonomous Project Management

自主项目管理

Multi-agent coordination using STATE.yaml pattern Skills: state-management, multi-agent, file-system
采用STATE.yaml模式实现多Agent协作 Skills: state-management, multi-agent, file-system

Multi-Channel Customer Service

多渠道客户服务

Unified inbox: WhatsApp, Instagram, Email, Google Reviews Skills: whatsapp-api, instagram-api, email-integration
统一收件箱：WhatsApp、Instagram、邮件、谷歌评论 Skills: whatsapp-api, instagram-api, email-integration

Personal CRM

个人CRM

Auto-discover contacts from email/calendar + NL queries Skills: email-parsing, calendar-integration, database
undefined
从邮件/日历自动识别联系人并支持自然语言查询 Skills: email-parsing, calendar-integration, database
undefined

Research & Learning

研究与学习

markdown
undefined
markdown
undefined

Personal Knowledge Base (RAG)

个人知识库（RAG）

Drop URLs/tweets/articles → searchable knowledge base Skills: rag, vector-search, content-extraction
导入URL/推文/文章 → 生成可搜索知识库 Skills: rag, vector-search, content-extraction

arXiv Paper Reader

arXiv论文阅读器

Fetch, analyze, compare papers conversationally Skills: arxiv-api, pdf-parsing, summarization
对话式获取、分析、对比论文 Skills: arxiv-api, pdf-parsing, summarization

Semantic Memory Search

语义记忆搜索

Vector-powered search over markdown memory files Skills: embeddings, hybrid-retrieval, file-watching
undefined
基于向量的Markdown记忆文件搜索 Skills: embeddings, hybrid-retrieval, file-watching
undefined

Common Implementation Patterns

通用实现模式

Pattern 1: Scheduled Digest

模式1：定时摘要

yaml
undefined
yaml
undefined

Daily Reddit Digest implementation

Daily Reddit Digest实现配置

schedule: "0 8 * * *" # Every day at 8 AM skills:
  • reddit-skill
  • summarization
  • notification
workflow:
  1. Fetch top posts from configured subreddits
  2. Filter by upvotes/engagement threshold
  3. Summarize using LLM
  4. Format digest
  5. Send via Telegram/Email/Discord
undefined
schedule: "0 8 * * *" # 每天早上8点执行 skills:
  • reddit-skill
  • summarization
  • notification
workflow:
  1. 从配置的子版块获取热门帖子
  2. 根据点赞/互动阈值筛选内容
  3. 使用LLM生成摘要
  4. 格式化摘要内容
  5. 通过Telegram/邮件/Discord发送
undefined

Pattern 2: Multi-Agent Coordination

模式2：多Agent协作

yaml
undefined
yaml
undefined

Content Factory pattern

内容工厂模式配置

agents:
  • name: researcher channel: "#research" skills: [web-search, note-taking]
  • name: writer channel: "#writing" skills: [content-generation, editing]
  • name: designer channel: "#design" skills: [image-generation, thumbnail-creation]
coordination: type: state-file # STATE.yaml handoff: automatic
undefined
agents:
  • name: researcher channel: "#research" skills: [web-search, note-taking]
  • name: writer channel: "#writing" skills: [content-generation, editing]
  • name: designer channel: "#design" skills: [image-generation, thumbnail-creation]
coordination: type: state-file # 基于STATE.yaml handoff: automatic
undefined

Pattern 3: Webhook Orchestration

模式3：Webhook编排

javascript
// n8n Workflow Orchestration pattern
// Agent sends request to n8n webhook
const triggerN8nWorkflow = async (workflowName, payload) => {
  const webhookUrl = process.env.N8N_WEBHOOK_BASE + workflowName;
  
  const response = await fetch(webhookUrl, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(payload)
  });
  
  return response.json();
};

// Agent never touches API credentials
// All integrations managed visually in n8n
javascript
// n8n工作流编排模式
// Agent向n8n Webhook发送请求
const triggerN8nWorkflow = async (workflowName, payload) => {
  const webhookUrl = process.env.N8N_WEBHOOK_BASE + workflowName;
  
  const response = await fetch(webhookUrl, {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify(payload)
  });
  
  return response.json();
};

// Agent无需接触API凭证
// 所有集成通过n8n可视化管理

Pattern 4: RAG Knowledge Base

模式4：RAG知识库

python
undefined
python
undefined

Personal Knowledge Base pattern

个人知识库实现模式

from openclaw_skills import rag_skill
from openclaw_skills import rag_skill

Add content to knowledge base

向知识库添加内容

rag_skill.add_document( content=article_text, metadata={ "source": "https://example.com/article", "date": "2026-05-16", "tags": ["ai", "research"] } )
rag_skill.add_document( content=article_text, metadata={ "source": "https://example.com/article", "date": "2026-05-16", "tags": ["ai", "research"] } )

Query conversationally

对话式查询

results = rag_skill.search( query="What did I save about RAG implementations?", top_k=5 )
undefined
results = rag_skill.search( query="我保存过哪些关于RAG实现的内容？", top_k=5 )
undefined

Pattern 5: State Management (Multi-Agent)

模式5：多Agent状态管理

yaml
undefined
yaml
undefined

STATE.yaml pattern for autonomous coordination

用于自主协作的STATE.yaml模式

project: youtube-content-pipeline state: research
completed:
  • idea-scouting
  • keyword-research
current_task: agent: researcher action: competitor-analysis started_at: 2026-05-16T10:30:00Z
next_tasks:
  • script-outline (writer)
  • thumbnail-concepts (designer)
context: channel: tech-tutorials target_keywords: ["AI agents", "automation"] deadline: 2026-05-20
undefined
project: youtube-content-pipeline state: research
completed:
  • idea-scouting
  • keyword-research
current_task: agent: researcher action: competitor-analysis started_at: 2026-05-16T10:30:00Z
next_tasks:
  • script-outline (writer)
  • thumbnail-concepts (designer)
context: channel: tech-tutorials target_keywords: ["AI agents", "automation"] deadline: 2026-05-20
undefined

Accessing Use Case Details

访问用例详情

bash
undefined
bash
undefined

Read a specific use case

查看特定用例

cat usecases/daily-reddit-digest.md
cat usecases/daily-reddit-digest.md

Search for keywords

关键词搜索

grep -r "telegram" usecases/
grep -r "telegram" usecases/

List all use cases by category

按类别列出所有用例

grep "^|" README.md | grep -A 1 "Social Media"
undefined
grep "^|" README.md | grep -A 1 "Social Media"
undefined

Configuration Examples

配置示例

Environment Variables (Common Across Use Cases)

通用环境变量（适用于多数用例）

bash
undefined
bash
undefined

Social Media

社交媒体

REDDIT_CLIENT_ID=your_reddit_client_id REDDIT_CLIENT_SECRET=your_reddit_secret TWITTER_API_KEY=your_twitter_key YOUTUBE_API_KEY=your_youtube_key
REDDIT_CLIENT_ID=your_reddit_client_id REDDIT_CLIENT_SECRET=your_reddit_secret TWITTER_API_KEY=your_twitter_key YOUTUBE_API_KEY=your_youtube_key

Communication

通讯工具

TELEGRAM_BOT_TOKEN=your_telegram_token DISCORD_WEBHOOK_URL=https://discord.com/api/webhooks/... TWILIO_ACCOUNT_SID=your_twilio_sid TWILIO_AUTH_TOKEN=your_twilio_token
TELEGRAM_BOT_TOKEN=your_telegram_token DISCORD_WEBHOOK_URL=https://discord.com/api/webhooks/... TWILIO_ACCOUNT_SID=your_twilio_sid TWILIO_AUTH_TOKEN=your_twilio_token

Infrastructure

基础设施

N8N_WEBHOOK_BASE=https://n8n.yourdomain.com/webhook/ SSH_PRIVATE_KEY_PATH=/path/to/ssh/key
N8N_WEBHOOK_BASE=https://n8n.yourdomain.com/webhook/ SSH_PRIVATE_KEY_PATH=/path/to/ssh/key

AI/LLM

AI/LLM

OPENAI_API_KEY=your_openai_key ANTHROPIC_API_KEY=your_anthropic_key
OPENAI_API_KEY=your_openai_key ANTHROPIC_API_KEY=your_anthropic_key

Databases

数据库

POSTGRES_URL=postgresql://user:pass@localhost/db VECTOR_DB_URL=http://localhost:6333 # Qdrant/Weaviate/etc
undefined
POSTGRES_URL=postgresql://user:pass@localhost/db VECTOR_DB_URL=http://localhost:6333 # Qdrant/Weaviate等
undefined

Skill Requirements Mapping

Skill需求映射

markdown
undefined
markdown
undefined

Example: Daily Reddit Digest

示例：Daily Reddit Digest

Required Skills:
  • reddit-skill (community or custom)
  • summarization (built-in LLM)
  • notification (telegram/discord/email)
Installation: openclaw install reddit-skill openclaw install notification-skill
所需Skills:
  • reddit-skill（社区版或自定义版）
  • summarization（内置LLM）
  • notification（telegram/discord/email）
安装方式: openclaw install reddit-skill openclaw install notification-skill

Example: Autonomous Project Management

示例：自主项目管理

Required Skills:
  • state-management (file-system based)
  • multi-agent (orchestration)
  • git-integration (commits, PRs)
Installation: openclaw install state-management-skill openclaw install git-skill
undefined
所需Skills:
  • state-management（基于文件系统）
  • multi-agent（编排功能）
  • git-integration（提交、PR管理）
安装方式: openclaw install state-management-skill openclaw install git-skill
undefined

Real-World Implementation Example

真实场景实现示例

python
undefined
python
undefined

Implementing "Custom Morning Brief" use case

实现「自定义晨间简报」用例

usecases/custom-morning-brief.md

usecases/custom-morning-brief.md

import os from datetime import datetime from openclaw_skills import calendar, todoist, news_api, llm, notification
async def generate_morning_brief(): """ Aggregate daily briefing from multiple sources """ # Fetch calendar events events = await calendar.get_today_events()
# Fetch tasks
tasks = await todoist.get_today_tasks()

# Fetch news
news = await news_api.get_top_headlines(
    topics=["AI", "technology", "startups"]
)

# Generate briefing
briefing = await llm.generate({
    "prompt": f"""
    Create a concise morning briefing:
    
    Calendar: {events}
    Tasks: {tasks}
    News: {news}
    
    Include:
    - Today's schedule highlights
    - Top 3 priority tasks
    - 2-3 relevant news items
    - AI-recommended actions
    
    Keep it under 300 words, friendly tone.
    """
})

# Send via SMS/Telegram
await notification.send(
    channel="sms",
    recipient=os.getenv("PHONE_NUMBER"),
    message=briefing
)

return briefing
import os from datetime import datetime from openclaw_skills import calendar, todoist, news_api, llm, notification
async def generate_morning_brief(): """ 从多源聚合每日简报 """ # 获取日历事件 events = await calendar.get_today_events()
# 获取任务
tasks = await todoist.get_today_tasks()

# 获取新闻
news = await news_api.get_top_headlines(
    topics=["AI", "technology", "startups"]
)

# 生成简报
briefing = await llm.generate({
    "prompt": f"""
    创建一份简洁的晨间简报：
    
    日历安排：{events}
    今日任务：{tasks}
    热点新闻：{news}
    
    内容需包含：
    - 今日日程重点
    - 3项核心优先级任务
    - 2-3条相关新闻
    - AI推荐行动建议
    
    字数控制在300字以内，语气友好。
    """
})

# 通过短信/Telegram发送
await notification.send(
    channel="sms",
    recipient=os.getenv("PHONE_NUMBER"),
    message=briefing
)

return briefing

Schedule: Every day at 7 AM

定时任务：每天早上7点执行

openclaw schedule add "0 7 * * *" generate_morning_brief

openclaw schedule add "0 7 * * *" generate_morning_brief

undefined
undefined

Discovering Use Cases via Natural Language

通过自然语言探索用例

When a user asks "show me openclaw use cases for X", reference this mapping:
python
undefined
当用户询问「展示适用于X场景的OpenClaw用例」时，可参考以下映射关系：
python
undefined

Intent → Use Case Category mapping

用户意图 → 用例类别映射

category_mapping = { "social media": ["daily-reddit-digest", "x-twitter-automation", "multi-source-tech-news"], "content creation": ["youtube-content-pipeline", "content-factory", "podcast-production"], "productivity": ["custom-morning-brief", "todoist-task-manager", "personal-crm"], "automation": ["n8n-workflow-orchestration", "self-healing-home-server"], "research": ["knowledge-base-rag", "arxiv-paper-reader", "semantic-memory-search"], "devops": ["n8n-workflow-orchestration", "self-healing-home-server"], "customer service": ["multi-channel-customer-service"], "health": ["health-symptom-tracker"], "finance": ["polymarket-autopilot", "earnings-tracker"] }
category_mapping = { "social media": ["daily-reddit-digest", "x-twitter-automation", "multi-source-tech-news"], "content creation": ["youtube-content-pipeline", "content-factory", "podcast-production"], "productivity": ["custom-morning-brief", "todoist-task-manager", "personal-crm"], "automation": ["n8n-workflow-orchestration", "self-healing-home-server"], "research": ["knowledge-base-rag", "arxiv-paper-reader", "semantic-memory-search"], "devops": ["n8n-workflow-orchestration", "self-healing-home-server"], "customer service": ["multi-channel-customer-service"], "health": ["health-symptom-tracker"], "finance": ["polymarket-autopilot", "earnings-tracker"] }

Example agent response

示例Agent响应

def recommend_use_case(user_intent): """ User: "I want to automate my morning routine" Agent: Recommends custom-morning-brief, family-calendar-household-assistant """ pass
undefined
def recommend_use_case(user_intent): """ 用户：「我想自动化我的晨间流程」 Agent：推荐custom-morning-brief、family-calendar-household-assistant用例 """ pass
undefined

Troubleshooting Common Issues

常见问题故障排除

Issue: Use Case References Missing Skills

问题：用例引用的Skill缺失

bash
undefined
bash
undefined

Check if skill exists in OpenClaw ecosystem

检查OpenClaw生态中是否存在该Skill

openclaw search reddit-skill
openclaw search reddit-skill

If not found, check use case documentation for custom skill link

如果未找到，查看用例文档中的自定义Skill链接

Many use cases link to community GitHub repos

多数用例会链接到社区GitHub仓库

undefined
undefined

Issue: API Rate Limits

问题：API调用频率限制

python
undefined
python
undefined

Most use cases should implement rate limiting

多数用例应实现频率限制

import time from functools import wraps
def rate_limit(calls_per_minute=10): min_interval = 60.0 / calls_per_minute last_called = [0.0]
def decorator(func):
    @wraps(func)
    async def wrapper(*args, **kwargs):
        elapsed = time.time() - last_called[0]
        wait_time = min_interval - elapsed
        if wait_time > 0:
            time.sleep(wait_time)
        result = await func(*args, **kwargs)
        last_called[0] = time.time()
        return result
    return wrapper
return decorator
undefined
import time from functools import wraps
def rate_limit(calls_per_minute=10): min_interval = 60.0 / calls_per_minute last_called = [0.0]
def decorator(func):
    @wraps(func)
    async def wrapper(*args, **kwargs):
        elapsed = time.time() - last_called[0]
        wait_time = min_interval - elapsed
        if wait_time > 0:
            time.sleep(wait_time)
        result = await func(*args, **kwargs)
        last_called[0] = time.time()
        return result
    return wrapper
return decorator
undefined

Issue: Security Concerns

问题：安全顾虑

markdown
⚠️ SECURITY WARNING from repository README:

> OpenClaw skills and third-party dependencies may have critical 
> security vulnerabilities. Many use cases link to community-built 
> skills that have NOT been audited.

Best Practices:
1. Review all skill source code before installation
2. Use environment variables for credentials (never hardcode)
3. Limit agent permissions (no sudo, restricted file access)
4. Audit third-party skills regularly
5. Use webhook patterns (n8n) to isolate credentials
markdown
⚠️ 仓库README中的安全警告：

> OpenClaw Skills和第三方依赖可能存在严重安全漏洞。许多用例链接的社区开发Skill未经过安全审计。

最佳实践：
1. 安装前审查所有Skill源代码
2. 使用环境变量存储凭证（切勿硬编码）
3. 限制Agent权限（无sudo权限、受限文件访问）
4. 定期审计第三方Skill
5. 使用Webhook模式（如n8n）隔离凭证

Issue: Multi-Agent Coordination Failures

问题：多Agent协作失败

yaml
undefined
yaml
undefined

Use STATE.yaml pattern from autonomous-project-management

采用autonomous-project-management中的STATE.yaml模式

Each agent checks state file before acting

每个Agent执行前先检查状态文件

state_file: STATE.yaml lock_file: STATE.lock
read_state:
  1. Acquire lock
  2. Read STATE.yaml
  3. Check current_task.agent == self.name
  4. Release lock
write_state:
  1. Acquire lock
  2. Update STATE.yaml
  3. Commit changes
  4. Release lock
  5. Notify next agent (optional)
undefined
state_file: STATE.yaml lock_file: STATE.lock
read_state:
  1. 获取锁
  2. 读取STATE.yaml
  3. 检查current_task.agent是否为自身名称
  4. 释放锁
write_state:
  1. 获取锁
  2. 更新STATE.yaml
  3. 提交更改
  4. 释放锁
  5. 通知下一个Agent（可选）
undefined

Contributing New Use Cases

贡献新用例

markdown
undefined
markdown
undefined

From CONTRIBUTING.md

来自CONTRIBUTING.md的要求

Requirements:
  1. Must be production-tested (at least 1 day)
  2. Include real implementation details
  3. List all required skills/dependencies
  4. Provide configuration examples
  5. No crypto-related use cases
Template: usecases/your-use-case.md
提交要求：
  1. 必须经过生产环境测试（至少运行1天）
  2. 包含真实实现细节
  3. 列出所有所需Skill/依赖
  4. 提供配置示例
  5. 禁止提交加密货币相关用例
模板： usecases/your-use-case.md

Use Case Title

用例标题

Overview

概述

What it does and why
功能与应用价值

Skills Required

所需Skill

  • skill-name-1
  • skill-name-2
  • skill-name-1
  • skill-name-2

Implementation

实现步骤

Step-by-step setup
分步搭建指南

Configuration

配置说明

Environment variables, API keys
环境变量、API密钥

Example Prompts

示例提示词

Real user interactions
真实用户交互场景

Tips & Troubleshooting

技巧与故障排除

Common issues

undefined

常见问题解决方法

undefined

Integration with Other Tools

与其他工具集成

bash
undefined
bash
undefined

n8n Workflow Orchestration

n8n工作流编排

Use case: usecases/n8n-workflow-orchestration.md

用例：usecases/n8n-workflow-orchestration.md

Benefit: Agent never touches credentials

优势：Agent无需接触凭证

AIONui Desktop Cowork

AIONui桌面协作工具

Use case: usecases/aionui-cowork-desktop.md

用例：usecases/aionui-cowork-desktop.md

Benefit: Multi-agent unified UI

优势：多Agent统一UI

DenchClaw Local CRM

DenchClaw本地CRM

Use case: usecases/local-crm-framework.md

用例：usecases/local-crm-framework.md

npx denchclaw
npx denchclaw

Benefit: Fully local CRM with browser automation

优势：具备浏览器自动化功能的全本地CRM

undefined
undefined

Quick Reference: Top 10 Use Cases by Popularity

快速参考：Top10热门用例

Based on repository structure (ordered by category appearance):
  1. Daily Reddit Digest - Automated subreddit summaries
  2. YouTube Content Pipeline - End-to-end video production automation
  3. n8n Workflow Orchestration - Credential-free API delegation
  4. Autonomous Project Management - STATE.yaml multi-agent coordination
  5. Multi-Channel Customer Service - Unified AI inbox
  6. Custom Morning Brief - Personalized daily briefing via SMS
  7. Personal Knowledge Base (RAG) - Conversational document search
  8. Self-Healing Home Server - Always-on infrastructure agent
  9. X/Twitter Automation - Full social media automation via TweetClaw
  10. arXiv Paper Reader - Conversational research paper analysis
基于仓库结构（按类别出现顺序排序）：
  1. Daily Reddit Digest - 自动化子版块内容摘要
  2. YouTube内容流水线 - 端到端视频生产自动化
  3. n8n工作流编排 - 无凭证API委托
  4. 自主项目管理 - STATE.yaml多Agent协作
  5. 多渠道客户服务 - AI统一收件箱
  6. 自定义晨间简报 - 通过SMS发送个性化每日简报
  7. 个人知识库（RAG） - 对话式文档搜索
  8. 自修复家庭服务器 - 全天候基础设施Agent
  9. X/Twitter自动化 - 通过TweetClaw实现全社交媒体自动化
  10. arXiv论文阅读器 - 对话式研究论文分析

Links & Resources

链接与资源

Agent Usage Recommendations

Agent使用建议

When helping users implement use cases:
  1. Ask about their goals first - Match use case to actual need
  2. Check skill availability - Verify required skills exist or link to custom implementations
  3. Warn about security - Reference repository security disclaimer
  4. Provide real examples - Use code from this skill, not placeholders
  5. Suggest combinations - Many use cases work well together (e.g., morning brief + personal CRM)
  6. Start simple - Recommend single-agent use cases before multi-agent orchestration
帮助用户实现用例时：
  1. 先询问用户目标 - 将用例与实际需求匹配
  2. 检查Skill可用性 - 确认所需Skill存在或提供自定义实现链接
  3. 提醒安全事项 - 参考仓库安全声明
  4. 提供真实示例 - 使用本Skill中的代码，而非占位符
  5. 建议组合使用 - 许多用例可协同工作（如晨间简报+个人CRM）
  6. 从简单开始 - 先推荐单Agent用例，再尝试多Agent编排