Skill: jason-aka-chen/meta-workflow-discoverer (version 1.0.0)

--- 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 ```bash pip install numpy pandas scikit-learn ``` ## Usage ### Initialize Discoverer ```python from workflow_discoverer import WorkflowDiscoverer discoverer = WorkflowDiscoverer( user_id="user123", min_occurrences=3 ) ``` ### Record Task History ```python # 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 ```python # 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 ```python # 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 ```python # 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 → Potential workflow: "send_report" ``` ### Sequence Patterns ``` [A, B, C] → D [A, B, C] → D [A, B, C] → D Pattern: Auto-create [A,B,C] → D ``` ### Time Patterns ``` Task: "morning standup" at 09:00 daily Task: "morning standup" at 09:05 daily → Suggest: Scheduled automation at 09:00 ``` ## Example: Full Workflow ```python # 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 ```python # 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 ```python # 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


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)