Automator

Create and manage complex automation workflows using OpenClaw. Orchestrate multi-step tasks, parallel processing, conditional logic, and scheduled automation...

Security Scan

The skill's requirements and instructions are internally consistent with an automation/workflow tool: it is instruction-only, requires the openclaw CLI, and does not request unrelated credentials or installs.

Purpose & Capability

Name/description (automation workflows) align with the declared requirement: the SKILL.md instructs use of the openclaw CLI and YAML workflows. The single required binary (openclaw) is appropriate for this purpose.

Instruction Scope

Instructions stay within workflow creation and openclaw CLI usage. However, example prompts and notifier/agent roles reference external URLs, emails, Slack, PagerDuty, and human-in-the-loop steps — which will cause agents or connectors to access external services when used. The skill itself does not instruct reading unrelated local files, but workflows can embed arbitrary prompts/URLs which could transmit data if users include sensitive information.

Install Mechanism

No install spec and no code files (instruction-only). No downloads or archive extraction are performed by the skill itself, which minimizes install-time risk.

Credentials

The skill declares no environment variables or credentials, which matches being instruction-only. Be aware examples reference sending emails, Slack, PagerDuty, etc.; using those will require configuring separate connectors/credentials outside this skill. The skill does not request unrelated secrets, but you will need to provide service credentials elsewhere for notifications/connectors to work.

Persistence & Privilege

always is false and there is no install-time configuration or persistent modification described. The skill does not request elevated platform privileges or modify other skills/configs.

Assessment

This skill appears coherent for creating workflows, but before installing: ensure you trust the openclaw CLI binary and its source; review any workflows or agent prompts for embedded secrets or sensitive URLs; understand that notifications (email/Slack/PagerDuty) require separate connector credentials you must provide and manage securely; test workflows in a sandbox environment and restrict which agents can access sensitive data or external networks.

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

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

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

failure_notifications:
  - email: "[e-mail]"
  - slack: "#alerts"

Quick Start

1. Define Your Workflow

Create a YAML file my-workflow.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 [e-mail]
        Subject: Price Alert!
        Body: Product is now ${{Check Amazon Price.output}}
    depends_on: [Check Amazon Price, Compare to Threshold]

2. Load and Start

# 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

# 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

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

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

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

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

Conditional Branching

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:

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

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

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

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?

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

Next Steps

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

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💡 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

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Automate the boring stuff. Focus on what matters. 🚀