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BytesAgain β€Ί Skills β€Ί Meta Workflow Discoverer
πŸ¦€ ClawHub

Meta Workflow Discoverer

by @jason-aka-chen

AI-powered workflow automation discoverer that observes user patterns, identifies repetitive tasks, and automatically generates executable automation workflo...

Versionv1.0.0
Downloads300
Installs1
#legal#automation#productivity
TERMINAL
clawhub install meta-workflow-discoverer

πŸ“– About This Skill

name: meta-workflow-discoverer description: AI-powered workflow automation discoverer that observes user patterns, identifies repetitive tasks, and automatically generates executable automation workflows. Learns from history to create time-saving automations. tags: - meta - workflow - automation - discovery - pattern-recognition - efficiency version: 1.0.0 author: chenq

Meta Workflow Discoverer

Automatically discover and create workflows from patterns.

Features

1. Pattern Mining

  • Task Similarity: Find similar recurring tasks
  • Sequence Patterns: Identify common task sequences
  • Time Patterns: Detect time-based patterns
  • Context Patterns: Learn contextual triggers

    • 2. Workflow Generation

  • Auto-Create: Generate workflow from patterns
  • Step Optimization: Optimize workflow steps
  • Error Handling: Add robust error handling
  • Parallelization: Identify parallelizable steps

    • 3. Automation

  • Scheduled Triggers: Time-based execution
  • Event Triggers: Event-based execution
  • Conditional Logic: Branching workflows
  • Looping: Repeat workflows as needed

    • 4. Learning

  • Success Tracking: Monitor workflow success
  • Auto-Improve: Refine based on results
  • User Feedback: Incorporate user corrections
  • Cross-User Learning: Share across users

    • Installation

    pip install numpy pandas scikit-learn

    Usage

    Initialize Discoverer

    from workflow_discoverer import WorkflowDiscoverer
    discoverer = WorkflowDiscoverer(
    user_id="user123",
    min_occurrences=3
)

    Record Task History

    # Record task execution
discoverer.record_task(
    task="send daily report",
    steps=["fetch_data", "generate_chart", "send_email"],
    context={"time": "morning", "recipients": ["team"]},
    result="success"
)
    Record multiple similar tasks
    
for i in range(5):
    discoverer.record_task(
        task="weekly summary",
        steps=["collect_stats", "format_report", "post_to_slack"],
        context={"day": "friday"},
        result="success"
    )

    Discover Workflows

    # Discover potential workflows
workflows = discoverer.discover_workflows()
    for wf in workflows:
    print(f"Workflow: {wf['name']}")
    print(f"Pattern: {wf['pattern']}")
    print(f"Confidence: {wf['confidence']:.0%}")
    print(f"Time saved: {wf['time_saved_minutes']} min")

    Create Automation

    # Create automated workflow
automation = discoverer.create_automation(
    workflow_id="weekly_summary",
    trigger={"type": "schedule", "time": "friday 09:00"},
    enabled=True
)
    print(f"Automation created: {automation['id']}")

    API Reference

    Recording

    | Method | Description | |--------|-------------| | record_task(...) | Record task execution | | record_sequence(...) | Record task sequence | | import_history(...) | Import from external source |

    Discovery

    | Method | Description | |--------|-------------| | discover_workflows() | Find workflow patterns | | analyze_sequences() | Analyze task sequences | | detect_triggers() | Detect trigger patterns |

    Automation

    | Method | Description | |--------|-------------| | create_automation(...) | Create automation | | enable_automation(id) | Enable workflow | | disable_automation(id) | Disable workflow | | run_automation(id) | Run manually |

    Learning

    | Method | Description | |--------|-------------| | track_results() | Track automation results | | improve_workflow() | Improve based on results | | merge_patterns() | Merge similar patterns |

    Workflow Templates

    Common Discovered Workflows

    # Data Analysis Workflow
{
    "name": "daily_data_review",
    "steps": [
        "fetch_yesterday_data",
        "run_analysis",
        "generate_report",
        "send_to_stakeholders"
    ],
    "trigger": "schedule: 09:00 daily",
    "time_saved": 30  # minutes
}
    Content Publishing Workflow
    
{
    "name": "cross_platform_post",
    "steps": [
        "create_content",
        "adapt_for_twitter",
        "adapt_for_linkedin",
        "schedule_posts"
    ],
    "trigger": "manual",
    "time_saved": 45
}
    Research Workflow
    
{
    "name": "topic_research",
    "steps": [
        "search_web",
        "filter_sources",
        "extract_key_info",
        "generate_summary"
    ],
    "trigger": "event: new_topic",
    "time_saved": 60
}

    Pattern Detection

    Task Similarity

    Task: "send report to john"
Task: "send report to team"  
Similarity: 0.85
β†’ Potential workflow: "send_report"

    Sequence Patterns

    [A, B, C] β†’ D
[A, B, C] β†’ D
[A, B, C] β†’ D
Pattern: Auto-create [A,B,C] β†’ D

    Time Patterns

    Task: "morning standup" at 09:00 daily
Task: "morning standup" at 09:05 daily
β†’ Suggest: Scheduled automation at 09:00

    Example: Full Workflow

    # 1. Record user's recurring tasks
discoverer = WorkflowDiscoverer("user123")
    Over time, user does similar tasks
    
discoverer.record_task(
    task="analyze stock 600519",
    steps=["fetch_data", "compute_indicators", "generate_signal"],
    context={"stock": "600519", "type": "analysis"}
)
    discoverer.record_task(
    task="analyze stock 000858",
    steps=["fetch_data", "compute_indicators", "generate_signal"],
    context={"stock": "000858", "type": "analysis"}
)
    2. Discover patterns
    
workflows = discoverer.discover_workflows()
    3. Create automation
    
if workflows:
    wf = workflows[0]
    
    automation = discoverer.create_automation(
        workflow_id=wf['id'],
        trigger={"type": "schedule", "cron": "0 9 * * 1-5"},
        params={"stocks": ["600519", "000858", "600036"]}
    )
    
    print(f"Created: {automation['name']}")

    Use Cases

  • Report Generation: Auto-create scheduled reports
  • Data Processing: Pipeline repetitive analysis
  • Communication: Automate routine messages
  • Research: Streamline information gathering
  • Trading: Systematic trading routines

    • Metrics

    Discovered Patterns

  • Task frequency
  • Sequence consistency
  • Time regularity
  • Context similarity

    • Workflow Value

  • Time saved per execution
  • Error reduction
  • Consistency improvement

    • Integration

    With OpenClaw

    # Auto-discover from conversation
@hookimpl
def after_message(message, response):
    discoverer.record_task(
        task=extract_intent(message),
        steps=extract_tools_used(response),
        result="success"
    )

    With Skills

    # Learn from skill usage
for skill in used_skills:
    discoverer.record_task(
        task=skill.name,
        steps=skill.execution_steps,
        context=skill.context,
        result=skill.result
    )

    Best Practices

    1. More Data = Better Patterns: Record more tasks for accuracy 2. Verify Before Automating: Review discovered workflows 3. Start Simple: Begin with 2-3 step workflows 4. Monitor Results: Track automation success 5. Iterate: Continuously improve workflows

    Future Capabilities

  • Natural language workflow creation
  • Cross-user pattern sharing
  • AI-generated workflow optimization
  • Self-healing workflows

    • ⚑ When to Use

    TriggerAction
    - **Data Processing**: Pipeline repetitive analysis
    - **Communication**: Automate routine messages
    - **Research**: Streamline information gathering
    - **Trading**: Systematic trading routines

    πŸ’‘ Examples

    Initialize Discoverer

    from workflow_discoverer import WorkflowDiscoverer
    discoverer = WorkflowDiscoverer(
    user_id="user123",
    min_occurrences=3
)

    Record Task History

    # Record task execution
discoverer.record_task(
    task="send daily report",
    steps=["fetch_data", "generate_chart", "send_email"],
    context={"time": "morning", "recipients": ["team"]},
    result="success"
)
    Record multiple similar tasks
    
for i in range(5):
    discoverer.record_task(
        task="weekly summary",
        steps=["collect_stats", "format_report", "post_to_slack"],
        context={"day": "friday"},
        result="success"
    )

    Discover Workflows

    # Discover potential workflows
workflows = discoverer.discover_workflows()
    for wf in workflows:
    print(f"Workflow: {wf['name']}")
    print(f"Pattern: {wf['pattern']}")
    print(f"Confidence: {wf['confidence']:.0%}")
    print(f"Time saved: {wf['time_saved_minutes']} min")

    Create Automation

    # Create automated workflow
automation = discoverer.create_automation(
    workflow_id="weekly_summary",
    trigger={"type": "schedule", "time": "friday 09:00"},
    enabled=True
)
    print(f"Automation created: {automation['id']}")

    πŸ“‹ Tips & Best Practices

    1. More Data = Better Patterns: Record more tasks for accuracy 2. Verify Before Automating: Review discovered workflows 3. Start Simple: Begin with 2-3 step workflows 4. Monitor Results: Track automation success 5. Iterate: Continuously improve workflows