docs: Add interactive demo for AI-powered exploration workflow (#540)

* docs: Add interactive demo for AI-powered exploration workflow

Add demo.py script that demonstrates AI task decomposition:
- Shows how Claude intelligently breaks down exploration goals
- Fast demo (~15 seconds) using CLIExecutor and AI split operation
- Easy to customize with different goals and focus areas
- Uses relative paths (assumes run from git root)
- Replaces previous longer demo scripts with single concise example

The demo showcases the core AI split functionality from PR #539,
making it easy for users to see the AI-powered task decomposition in action.

Usage:
  uv run python -m plugins.automation.workflows.examples.demo

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

* fix: Make Windows ctags installation non-blocking in CI

The Windows CI jobs were failing due to gateway timeouts when downloading
universal-ctags from the Chocolatey repository. This is a transient
infrastructure issue unrelated to code changes.

Changes:
- Add continue-on-error: true to Windows ctags installation step
- Add 60-second timeout to choco install command
- Update comment to indicate ctags is optional

This allows CI to proceed even if ctags installation fails, as tests
requiring ctags will be skipped gracefully.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

---------

Co-authored-by: Claude <[email protected]>

Author: plusplusoneplusplus

.github/workflows/tests.yml

@@ -61,9 +61,10 @@ jobs:
 
       - name: Install system dependencies (Windows)
         if: runner.os == 'Windows'
+        continue-on-error: true
         run: |
-          # Install ctags using chocolatey
-          choco install universal-ctags -y
+          # Install ctags using chocolatey (optional, some tests may be skipped if unavailable)
+          choco install universal-ctags -y --timeout=60
 
       - name: Install uv (Unix)
         if: runner.os != 'Windows'

plugins/automation/workflows/examples/demo.py

@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+"""
+Quick demo of AI-powered task decomposition.
+
+Shows how Claude intelligently breaks down exploration goals.
+
+Usage:
+    uv run python -m plugins.automation.workflows.examples.demo
+"""
+
+import asyncio
+import sys
+from pathlib import Path
+
+# Add project root to path
+project_root = Path(__file__).parent.parent.parent.parent.parent
+sys.path.insert(0, str(project_root))
+
+from plugins.automation.workflows.steps.operations import AISplitOperation
+
+
+async def main():
+    print("\n" + "=" * 80)
+    print("AI-POWERED TASK DECOMPOSITION DEMO")
+    print("=" * 80 + "\n")
+
+    # Configure the AI split operation
+    config = {
+        "model": "haiku",      # Fast model for task decomposition
+        "max_tasks": 6,        # Allow up to 6 parallel tasks
+        "min_tasks": 3,        # Require at least 3 tasks
+    }
+
+    inputs = {
+        "goal": "Understand how the MCP workflow system orchestrates AI agents",
+        "codebase_path": ".",  # Current directory (git root)
+        "focus_areas": ["architecture", "execution flow", "error handling"],
+        "constraints": "Focus on the core workflow engine and step execution"
+    }
+
+    print("🎯 Goal:")
+    print(f"   {inputs['goal']}\n")
+
+    print("📍 Focus Areas:")
+    for area in inputs['focus_areas']:
+        print(f"   • {area}")
+    print()
+
+    print("⏳ Asking Claude to decompose this into focused exploration tasks...\n")
+    print("-" * 80 + "\n")
+
+    # Create and execute operation
+    operation = AISplitOperation(config, inputs)
+
+    # Validate
+    error = operation.validate()
+    if error:
+        print(f"❌ Validation error: {error}")
+        return
+
+    try:
+        # Execute AI split
+        result = await operation.execute()
+
+        # Display results
+        print("✅ AI Task Decomposition Complete!\n")
+        print("=" * 80)
+        print(f"AI REASONING:")
+        print("=" * 80)
+        print(result['reasoning'])
+        print()
+
+        print("=" * 80)
+        print(f"GENERATED TASKS ({result['task_count']} tasks)")
+        print("=" * 80 + "\n")
+
+        for task in result['tasks']:
+            print(f"📋 Task {task['index'] + 1}: {task['title']}")
+            print(f"   Type: {task['type']}")
+            print(f"   Priority: {task.get('priority', 'N/A')}")
+            print(f"   Complexity: {task.get('estimated_complexity', 'N/A')}")
+            print(f"   Query: {task['query']}")
+            print()
+
+        print("=" * 80)
+        print("METADATA")
+        print("=" * 80)
+        print(f"Goal: {result['metadata']['goal']}")
+        print(f"Codebase: {result['metadata'].get('codebase_path', 'N/A')}")
+        print(f"Model: {result['metadata'].get('model', 'haiku')}")
+        print(f"Max Tasks: {result['metadata'].get('max_tasks', 'N/A')}")
+        print(f"Min Tasks: {result['metadata'].get('min_tasks', 'N/A')}")
+        print()
+
+        print("💡 Next Steps:")
+        print("   • These tasks can now be executed in parallel by ExploreAgent")
+        print("   • Each task will store findings in session files")
+        print("   • A summarize operation will aggregate all results")
+        print("   • See README.md for full workflow examples")
+        print()
+
+    except Exception as e:
+        print(f"❌ Error during execution: {e}")
+        import traceback
+        traceback.print_exc()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())