Automator

Create and manage complex automation workflows using OpenClaw. Orchestrate multi-step tasks, parallel processing, conditional logic, and scheduled automation. Perfect for repetitive business processes, data pipelines, and cross-platform integrations.

Fuhaolin@fuczy

Install

openclaw skills install @fuczy/clawd-automator

Automator Skill

Build profitable automation workflows that save hours every week

When to Use

✅ USE this skill when:

"Automate my daily report generation"
"Create a workflow that monitors prices and alerts me"
"Set up a multi-step data processing pipeline"
"I need to schedule recurring tasks with dependencies"
"Automate my social media posting across platforms"
"Create an approval workflow for my team"
"Set up automated backups with notifications"

When NOT to Use

❌ DON'T use this skill when:

Single simple command needed (use direct command instead)
One-off manual task (no automation needed)
Tasks requiring human judgment/creativity
Real-time interactive work (workflow adds latency)

💰 Value Proposition

What you get:

⏰ Save 10+ hours/week on repetitive tasks
🎯 Reliability - workflows run on schedule, even when you forget
🔄 Scalability - same workflow works at any volume
📊 Visibility - track execution history and failures
🛡️ Error handling - retries, fallbacks, alerts

ROI Example:

Simple workflow (data fetch + email): 1 hour setup = 5 hours/month saved
Complex workflow (multi-source aggregation + reports): 4 hours setup = 20+ hours/month saved
Break-even: 1-2 weeks for most workflows

Core Concepts

Workflow Structure

yaml

workflow:
  name: "Daily Report Generator"
  schedule: "0 8 * * *"  # Every day at 8 AM
  steps:
    - id: fetch_data
      task: "Fetch sales data from API"
      agent: "data-fetcher"
      timeout: 300

    - id: process
      task: "Process data into report format"
      agent: "data-processor"
      depends_on: [fetch_data]
      timeout: 600

    - id: notify
      task: "Send report via email"
      agent: "notifier"
      depends_on: [process]
      timeout: 120

Agent Roles

Each step can run on a specialized agent:

data-fetcher: API calls, data extraction
data-processor: Transformations, analysis, calculations
notifier: Email, Slack, Telegram, notifications
approver: Human-in-the-loop decisions
archiver: Storage, backups, cleanup

Error Handling & Retries

yaml

retry_policy:
  max_attempts: 3
  backoff: "exponential"  # 1s, 2s, 4s
  on_failure: "notify_admin"  # or "continue", "abort"

failure_notifications:
  - email: "admin@company.com"
  - slack: "#alerts"

Quick Start

1. Define Your Workflow

Create a YAML file my-workflow.yaml:

yaml

workflow:
  name: "Price Monitor"
  description: "Check product prices hourly and alert if below threshold"
  schedule:
    type: "interval"
    every: "1h"

steps:
  - name: "Check Amazon Price"
    agent: "price-checker"
    prompt: |
      Check price of product https://amazon.com/dp/B08XYZ
      Return price and availability

  - name: "Compare to Threshold"
    agent: "decision-maker"
    prompt: |
      Threshold: $50
      Current price: {{Check Amazon Price.output}}
      Is price below threshold? Return yes/no

  - name: "Send Alert if Cheap"
    agent: "notifier"
    prompt: |
      If {{Compare to Threshold.output}} == "yes":
        Send email to user@example.com
        Subject: Price Alert!
        Body: Product is now ${{Check Amazon Price.output}}
    depends_on: [Check Amazon Price, Compare to Threshold]

2. Load and Start

bash

# Load workflow definition
openclaw workflow load my-workflow.yaml

# Start the scheduled workflow
openclaw workflow start Price Monitor

# Check status
openclaw workflow status

3. Monitor Execution

bash

# View recent runs
openclaw workflow runs Price Monitor --limit 10

# Get execution details
openclaw workflow run <run-id>

# Stop workflow
openclaw workflow stop Price Monitor

Common Workflow Patterns

Pattern 1: Data Pipeline

yaml

workflow:
  name: "Daily Analytics Pipeline"
  schedule: "0 6 * * *"  # 6 AM daily

steps:
  - fetch: "Extract data from 3 sources"
    agent: "extractor"
    parallel: true  # Run multiple sources in parallel

  - transform: "Clean and normalize data"
    agent: "transformer"
    depends_on: [fetch]

  - analyze: "Generate insights"
    agent: "analyst"
    depends_on: [transform]

  - report: "Create PDF report"
    agent: "reporter"
    depends_on: [analyze]

  - distribute: "Email and Slack"
    agent: "distributor"
    depends_on: [report]

Benefit: 30-minute manual process → fully automated

Pattern 2: Approval Workflow

yaml

workflow:
  name: "Document Approval"
  trigger: "manual"  # Start on demand

steps:
  - draft: "Generate initial document"
    agent: "writer"

  - review: "Human review"
    agent: "approver"
    type: "human_input"  # Waits for manual approval

  - finalize: "Apply final changes"
    agent: "editor"
    depends_on: [review]

  - publish: "Deploy to production"
    agent: "publisher"
    depends_on: [finalize]

Benefit: Track approvals, no lost emails

Pattern 3: Alert & Escalation

yaml

workflow:
  name: "System Monitor"
  schedule: "*/5 * * * *"  # Every 5 minutes
  alert_levels:
    - warning: "System load > 80%"
    - critical: "System load > 95%"

steps:
  - check: "Monitor system metrics"
    agent: "monitor"

  - classify: "Determine severity"
    agent: "classifier"

  - alert:
      agent: "alerter"
      escalation:
        warning: "log_only"
        critical: ["slack", "pagerduty", "sms"]

Benefit: 24/7 monitoring without human attention

Advanced Features

Parallel Execution

yaml

steps:
  - name: "Parallel Fetch"
    agent: "fetcher"
    task: "Fetch data from multiple sources"
    parallel: true
    max_concurrent: 5

Conditional Branching

yaml

steps:
  - validate: "Check data quality"
    agent: "validator"

  - if_good:
      agent: "loader"
      depends_on: [validate]
      condition: "{{validate.output}} == 'valid'"

  - if_bad:
      agent: "alerter"
      depends_on: [validate]
      condition: "{{validate.output}} != 'valid'"

Output Passing

Use {{step-name.output}} to reference previous step results:

yaml

steps:
  - fetch_users:
      agent: "query-db"
      output: "user_ids"

  - fetch_data:
      agent: "api-client"
      prompt: "Fetch records for users: {{fetch_users.output}}"

Pro Tips

1. Start Simple, Then Complex

Begin with 2-3 step workflows
Add error handling after basic flow works
Use templates (see below)

2. Use Specialized Agents

Create dedicated agents for common tasks
Save as reusable agent profiles
Example: data-analyst, email-composer, code-reviewer

3. Implement Checkpoints

yaml

steps:
  - step1: ...
  - checkpoint: "Save progress to DB"
    agent: "checkpointer"
  - step2:
      depends_on: [checkpoint]
      # Will resume from checkpoint if failed

4. Set Up Alerts

Always configure failure notifications
Use different channels for different severity levels
Include run ID in alerts for quick debugging

5. Monitor Costs

Track token usage per workflow run
Set budget alerts
Optimize prompts to reduce token consumption

Templates

Copy these templates to get started:

Template: Daily Summary

yaml

workflow:
  name: "Daily Digest"
  schedule: "0 7 * * *"

steps:
  - news: "Fetch latest news"
    agent: "news-fetcher"

  - weather: "Get weather forecast"
    agent: "weather-checker"

  - calendar: "Today's meetings"
    agent: "calendar-agent"

  - compile: "Compile into digest"
    agent: "compiler"

  - send: "Email digest"
    agent: "emailer"

Template: E-commerce Monitor

yaml

workflow:
  name: "Store Monitor"
  schedule: "*/15 * * * *"

steps:
  - check_inventory:
      agent: "inventory-checker"
      prompt: "List products below reorder threshold"

  - check_orders:
      agent: "order-checker"
      prompt: "Find pending orders > 24 hours"

  - generate_report:
      agent: "reporter"
      depends_on: [check_inventory, check_orders]

  - notify_manager:
      agent: "slack-notifier"
      depends_on: [generate_report]

Troubleshooting

Workflow Not Running?

Check schedule format (cron expression)
Verify agent exists: openclaw agents list
View logs: openclaw logs --follow

Steps Timing Out?

Increase timeout in step definition
Break large tasks into smaller steps
Use parallelization

No Output Available?

Check agent responded correctly
Use openclaw workflow run <id> to inspect
Agents must use output field

Want to Pause?

bash

openclaw workflow pause <workflow-name>
openclaw workflow resume <workflow-name>

Next Steps

📖 Read examples: ~/.openclaw/workspace/skills/automator/examples/
🧪 Test in sandbox: Use non-production agents first
📈 Monitor usage: Check token costs daily
🔄 Iterate: Refine prompts based on results
📤 Share: Publish your workflows to ClawHub (coming soon!)

💡 Need Help?

Join OpenClaw Discord: https://discord.com/invite/clawd
Report issues: https://github.com/openclaw/openclaw/issues
Read full docs: https://docs.openclaw.ai/workflows

Automate the boring stuff. Focus on what matters. 🚀

Automator

Install

Automator Skill

When to Use

When NOT to Use

💰 Value Proposition

Core Concepts

Workflow Structure

Agent Roles

Error Handling & Retries

Quick Start

1. Define Your Workflow

2. Load and Start

3. Monitor Execution

Common Workflow Patterns

Pattern 1: Data Pipeline

Pattern 2: Approval Workflow

Pattern 3: Alert & Escalation

Advanced Features

Parallel Execution

Conditional Branching

Output Passing

Pro Tips

1. Start Simple, Then Complex

2. Use Specialized Agents

3. Implement Checkpoints

4. Set Up Alerts

5. Monitor Costs

Templates

Template: Daily Summary

Template: E-commerce Monitor

Troubleshooting

Workflow Not Running?

Steps Timing Out?

No Output Available?

Want to Pause?

Next Steps

💡 Need Help?

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