awesome-openclaw-usecases-discovery

# awesome-openclaw-usecases-discovery > Skill by [ara.so](https://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. ## What It Covers 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. ## Installation This is a reference repository, not a package. Access it via: ```bash # Clone the repository git clone https://github.com/hesamsheikh/awesome-openclaw-usecases.git cd awesome-openclaw-usecases # Browse use cases ls usecases/ ``` Or view online at: https://github.com/hesamsheikh/awesome-openclaw-usecases ## Repository Structure ``` 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 ## Key Use Case Categories ### Social Media Automation ```markdown # Daily Reddit Digest Summarize curated subreddits based on preferences Skills: reddit-skill, summarization # X/Twitter Automation Post, reply, like, DM, search via TweetClaw plugin Skills: tweetclaw-plugin, scheduling # Multi-Source Tech News Aggregate from 109+ sources (RSS, X, GitHub, web) Skills: rss-reader, web-search, content-scoring ``` ### Creative Workflows ```markdown # Goal-Driven Autonomous Tasks Brain dump → auto-generate tasks → build mini-apps overnight Skills: task-generation, autonomous-execution, git-integration # YouTube Content Pipeline Automate idea scouting, research, tracking Skills: youtube-api, notion-integration, scheduling # Multi-Agent Content Factory Research + writing + thumbnail agents in Discord Skills: multi-agent, discord-integration, image-generation ``` ### Infrastructure & DevOps ```markdown # n8n Workflow Orchestration Delegate API calls to n8n via webhooks (agent never touches creds) Skills: webhook-trigger, n8n-integration # Self-Healing Home Server Always-on infra agent with SSH, cron, self-healing Skills: ssh-access, cron-management, monitoring ``` ### Productivity ```markdown # Autonomous Project Management Multi-agent coordination using STATE.yaml pattern Skills: state-management, multi-agent, file-system # Multi-Channel Customer Service Unified inbox: WhatsApp, Instagram, Email, Google Reviews Skills: whatsapp-api, instagram-api, email-integration # Personal CRM Auto-discover contacts from email/calendar + NL queries Skills: email-parsing, calendar-integration, database ``` ### Research & Learning ```markdown # Personal Knowledge Base (RAG) Drop URLs/tweets/articles → searchable knowledge base Skills: rag, vector-search, content-extraction # arXiv Paper Reader Fetch, analyze, compare papers conversationally Skills: arxiv-api, pdf-parsing, summarization # Semantic Memory Search Vector-powered search over markdown memory files Skills: embeddings, hybrid-retrieval, file-watching ``` ## Common Implementation Patterns ### Pattern 1: Scheduled Digest ```yaml # Daily Reddit Digest implementation 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 ``` ### Pattern 2: Multi-Agent Coordination ```yaml # Content Factory pattern 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 ``` ### Pattern 3: Webhook Orchestration ```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 ``` ### Pattern 4: RAG Knowledge Base ```python # Personal Knowledge Base pattern from openclaw_skills import rag_skill # Add content to knowledge base rag_skill.add_document( content=article_text, metadata={ "source": "https://example.com/article", "date": "2026-05-16", "tags": ["ai", "research"] } ) # Query conversationally results = rag_skill.search( query="What did I save about RAG implementations?", top_k=5 ) ``` ### Pattern 5: State Management (Multi-Agent) ```yaml # STATE.yaml pattern for autonomous coordination 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 ``` ## Accessing Use Case Details ```bash # Read a specific use case cat usecases/daily-reddit-digest.md # Search for keywords grep -r "telegram" usecases/ # List all use cases by category grep "^|" README.md | grep -A 1 "Social Media" ``` ## Configuration Examples ### Environment Variables (Common Across Use Cases) ```bash # Social Media 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 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 N8N_WEBHOOK_BASE=https://n8n.yourdomain.com/webhook/ SSH_PRIVATE_KEY_PATH=/path/to/ssh/key # AI/LLM OPENAI_API_KEY=your_openai_key ANTHROPIC_API_KEY=your_anthropic_key # Databases POSTGRES_URL=postgresql://user:pass@localhost/db VECTOR_DB_URL=http://localhost:6333 # Qdrant/Weaviate/etc ``` ### Skill Requirements Mapping ```markdown # Example: 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 # Example: Autonomous Project Management Required Skills: - state-management (file-system based) - multi-agent (orchestration) - git-integration (commits, PRs) Installation: openclaw install state-management-skill openclaw install git-skill ``` ## Real-World Implementation Example ```python # Implementing "Custom Morning Brief" use case # 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 # Schedule: Every day at 7 AM # openclaw schedule add "0 7 * * *" generate_morning_brief ``` ## Discovering Use Cases via Natural Language When a user asks "show me openclaw use cases for X", reference this mapping: ```python # Intent → Use Case Category mapping 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 def recommend_use_case(user_intent): """ User: "I want to automate my morning routine" Agent: Recommends custom-morning-brief, family-calendar-household-assistant """ pass ``` ## Troubleshooting Common Issues ### Issue: Use Case References Missing Skills ```bash # Check if skill exists in OpenClaw ecosystem openclaw search reddit-skill # If not found, check use case documentation for custom skill link # Many use cases link to community GitHub repos ``` ### Issue: API Rate Limits ```python # Most use cases should implement rate limiting 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 ``` ### Issue: Security Concerns ```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 ``` ### Issue: Multi-Agent Coordination Failures ```yaml # Use STATE.yaml pattern from autonomous-project-management # Each agent checks state file before acting 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) ``` ## Contributing New Use Cases ```markdown # From CONTRIBUTING.md 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 --- # Use Case Title ## Overview What it does and why ## Skills Required - skill-name-1 - skill-name-2 ## Implementation Step-by-step setup ## Configuration Environment variables, API keys ## Example Prompts Real user interactions ## Tips & Troubleshooting Common issues --- ``` ## Integration with Other Tools ```bash # n8n Workflow Orchestration # Use case: usecases/n8n-workflow-orchestration.md # Benefit: Agent never touches credentials # AIONui Desktop Cowork # Use case: usecases/aionui-cowork-desktop.md # Benefit: Multi-agent unified UI # DenchClaw Local CRM # Use case: usecases/local-crm-framework.md npx denchclaw # Benefit: Fully local CRM with browser automation ``` ## Quick Reference: Top 10 Use Cases by Popularity 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