Transform EMDX into a Claude Code Control Center - a unified interface where developers can seamlessly blend human planning with AI execution, creating a new paradigm for AI-assisted development.
Imagine opening EMDX and seeing not just documents, but a living, breathing development ecosystem:
┌─ Claude Control Center ─────────────────────────────────────────┐
│ 🎯 Gameplans 🔍 Analyses 📊 Dashboards 🧠 AI Sessions │
└─────────────────────────────────────────────────────────────────┘
┌─ Active Context ────────────────┬─ Live Execution ─────────────┐
│ 📄 Auth System Redesign │ ● Claude: Implementing auth │
│ Tags: 🎯 🚀 🔧 │ ├─ Created user.py │
│ │ ├─ Updated routes.py │
│ ## Goals │ └─ Running tests... │
│ 1. Replace JWT with sessions │ │
│ 2. Add OAuth providers │ 🟡 Git: Creating PR │
│ 3. Implement 2FA │ └─ Branch: feat/auth-v2 │
│ │ │
│ ## Context Docs │ ✅ Tests: 42/42 passing │
│ → API Spec (#234) │ │
│ → Security Audit (#189) │ ⏸️ Monitor: API Health │
│ → User Feedback (#445) │ └─ Paused by user │
└─────────────────────────────────┴─────────────────────────────┘
The system understands relationships between documents:
class ContextGraph:
"""Build a knowledge graph of related documents."""
def get_context_for_execution(self, doc_id: int) -> dict:
"""Gather all relevant context for AI execution."""
return {
'primary_doc': get_document(doc_id),
'related_docs': self.get_related_documents(doc_id),
'previous_attempts': self.get_execution_history(doc_id),
'project_context': self.get_project_info(doc_id),
'relevant_code': self.scan_codebase_context(doc_id),
'test_results': self.get_recent_test_results()
}Different document types trigger different AI behaviors:
- Breaks down into subtasks automatically
- Tracks progress across multiple executions
- Can pause/resume complex implementations
- Maintains state between sessions
- Gathers data before execution
- Runs investigative commands
- Produces structured reports
- Feeds findings back into knowledge base
- Captures error context automatically
- Suggests fixes based on patterns
- Can run diagnostic commands
- Links to similar resolved issues
Not just Claude, but a full orchestration system:
class ExecutionOrchestrator:
"""Orchestrate multiple AI and tool executions."""
def execute_plan(self, plan: Document) -> ExecutionResult:
steps = self.parse_plan_steps(plan)
for step in steps:
if step.requires_ai:
# Route to Claude
result = self.claude_executor.run(step)
elif step.is_git_operation:
# Handle with git automation
result = self.git_executor.run(step)
elif step.is_test:
# Run test suite
result = self.test_executor.run(step)
elif step.is_deployment:
# Handle deployment
result = self.deploy_executor.run(step)
# Checkpoint after each step
self.checkpoint(plan.id, step, result)Beyond simple execution - true collaboration:
┌─ AI Session: Refactoring User Service ──────────────────────────┐
│ Claude > I'll help refactor the user service. First, let me │
│ analyze the current structure... │
│ │
│ You > Focus on the authentication flow first │
│ │
│ Claude > Understood. Looking at auth.py now... │
│ Found 3 areas for improvement: │
│ 1. Password hashing is using MD5 │
│ 2. No rate limiting on login attempts │
│ 3. Session tokens never expire │
│ │
│ You > Let's fix #1 first, use bcrypt │
│ │
│ Claude > Implementing bcrypt hashing... │
│ ✓ Updated user.py │
│ ✓ Created migration │
│ ✓ Updated tests │
│ Running test suite... ✅ All passing! │
│ │
│ [Continue] [Pause] [Save Session] [Export Changes] │
└──────────────────────────────────────────────────────────────────┘
The system learns from every execution:
- "This looks like the auth refactor from project X"
- "Similar error encountered in execution #234"
- "This gameplan matches successful pattern Y"
- Auto-saves progress at logical breakpoints
- Can resume failed executions from last good state
- Maintains execution context between sessions
- Automatic rollback capabilities
- Suggests fixes for common failures
- Can retry with modified approach
See your entire development history:
Timeline View
═══════════════════════════════════════════════════════════════
Today
├─ 09:00 ● Gameplan: API Redesign → Claude execution started
├─ 09:15 ✓ Created new route structure
├─ 09:30 ✓ Implemented middleware
├─ 09:45 ✗ Tests failed (17/45)
├─ 10:00 ● Debug session started
├─ 10:15 ✓ Fixed test issues
├─ 10:30 ✓ All tests passing
├─ 10:45 ● Creating PR...
└─ 11:00 ✓ PR #234 created and ready for review
Yesterday
├─ 14:00 ● Analysis: Database Performance
├─ 14:30 ✓ Identified slow queries
└─ 15:00 ✓ Created optimization plan (#567)
Every execution enriches your knowledge base:
class KnowledgeAmplifier:
"""Learn from every AI execution."""
def post_execution_analysis(self, execution: ExecutionResult):
# Extract learnings
patterns = self.extract_patterns(execution)
solutions = self.extract_solutions(execution)
errors = self.extract_error_patterns(execution)
# Create new documents automatically
if patterns:
create_document(
title=f"Pattern: {patterns.summary}",
content=patterns.details,
tags=['📚', '🔍', 'pattern']
)
# Link related executions
similar = self.find_similar_executions(execution)
for sim in similar:
link_documents(execution.doc_id, sim.doc_id)
# Update success metrics
self.update_pattern_success_rates(execution)- Share execution contexts with team
- Collaborative AI sessions
- Knowledge base merging
- Execution handoffs
# After execution, create review request
review = ReviewRequest(
execution_id=execution.id,
changes=execution.get_changes(),
context=execution.get_context(),
ai_explanation=execution.get_ai_summary()
)┌─ Planning Mode ─────┬─ Execution Mode ─────┬─ Review Mode ────┐
│ Document editor │ Live logs │ Diff viewer │
│ Tag management │ Progress bars │ Test results │
│ Related docs │ Resource usage │ PR preview │
└─────────────────────┴─────────────────────┴──────────────────┘
Floating UI elements that show relevant information:
- Hover over a function → see its implementation
- Mention a document → preview appears
- Reference an error → see previous solutions
Visualize parallel executions:
Layer 1: Claude implementing feature ████████▒▒▒▒ 70%
Layer 2: Tests running continuously ████████████ 100% ✓
Layer 3: Git operations ████▒▒▒▒▒▒▒▒ 33%
Layer 4: Monitoring API health ████████████ Active
Support for multiple AI models with different strengths:
class AIModelRouter:
"""Route tasks to appropriate AI models."""
models = {
'claude-3-opus': {'strengths': ['complex_reasoning', 'architecture']},
'claude-3-sonnet': {'strengths': ['quick_tasks', 'refactoring']},
'gpt-4': {'strengths': ['creative_solutions', 'debugging']},
'local-llm': {'strengths': ['privacy_sensitive', 'quick_completion']}
}
def route_task(self, task: Task) -> AIModel:
# Intelligent routing based on task characteristics
if task.requires_privacy:
return self.models['local-llm']
elif task.complexity > 0.8:
return self.models['claude-3-opus']
# ... etcPre-built execution patterns:
recipes:
feature_implementation:
steps:
- analyze: "Review requirements and existing code"
- plan: "Create implementation plan"
- implement: "Write code with Claude"
- test: "Run test suite"
- document: "Update documentation"
- pr: "Create pull request"
bug_investigation:
steps:
- reproduce: "Confirm bug exists"
- analyze: "Find root cause"
- fix: "Implement solution"
- test: "Verify fix works"
- regression: "Ensure no new issues"Automatic execution based on events:
class ExecutionTriggers:
"""Automatically trigger executions based on events."""
@on_event('git_push')
def on_push(self, event):
# Find related gameplans
plans = find_documents_by_tag('🎯', project=event.project)
for plan in plans:
if self.should_execute(plan, event):
execute_plan(plan, context={'trigger': 'git_push'})
@on_event('test_failure')
def on_test_failure(self, event):
# Create debug session automatically
create_debug_session(
error=event.error,
context=event.test_context
)- Execution Engine: Manages all AI/tool executions
- Context Service: Builds and maintains execution contexts
- Knowledge Graph: Tracks relationships and learnings
- Event Bus: Coordinates between components
- State Manager: Handles checkpointing and recovery
- Multi-pane Editor: For viewing plans/code/results
- Real-time Log Viewer: With filtering and search
- Execution Timeline: Visual history browser
- Context Browser: Navigate related documents
- AI Chat Interface: For interactive sessions
- Document Store: Enhanced EMDX database
- Execution History: All past executions
- Context Cache: Fast context retrieval
- Checkpoint Store: Execution state snapshots
- Learning Database: Patterns and solutions
Morning workflow with Claude Control Center:
- Open EMDX → See dashboard of active work
- Select gameplan → Context automatically loaded
- Hit Execute → Claude starts implementing
- Monitor progress → Real-time logs and updates
- Intervene if needed → Pause, modify, resume
- Review changes → Integrated diff viewer
- Approve & merge → One-click deployment
- Replay any execution step-by-step
- Modify and re-run from any point
- Compare different execution paths
- Real-time collaborative editing with Claude
- Claude suggests next steps as you type
- Seamless handoff between human and AI
- Share successful execution patterns
- Import recipes from other developers
- Rate and review execution strategies
- Track your AI collaboration metrics
- See which patterns work best
- Optimize your workflow over time
The Claude Code Control Center transforms EMDX from a knowledge base into a complete AI-powered development environment. It's not just about storing documents or running commands - it's about creating a new paradigm where human creativity and AI capability blend seamlessly.
Key innovations:
- Context-aware execution - AI understands your full project
- Multi-modal orchestration - Not just Claude, but all tools
- Interactive sessions - True collaboration, not just commands
- Knowledge amplification - Every execution makes you smarter
- Visual timeline - See your development story unfold
- Smart checkpointing - Never lose progress
- Team collaboration - AI-assisted development at scale
This is the future of AI-assisted development: A unified control center where your ideas transform into reality through intelligent orchestration of AI and traditional tools, all while building an ever-growing knowledge base that makes you more effective over time.