# RForge Auto-Delegation MVP - Week 1 Implementation Plan

> **STATUS: COMPLETED**
> **Date:** 2025-12-27
> This MVP plan was successfully executed. The RForge MCP server is live.

> **See instead:** `RFORGE-AUTO-DELEGATION-MCP-PLAN.md` (current implementation plan)

---

**Goal:** Working auto-delegation system with pattern recognition and background agents
**Timeline:** 8 days (Dec 22 - Dec 30, 2025)
**Success Metric:** User types one command → system auto-delegates → results in <3 min

---

## 🎯 MVP Scope

### What We're Building

**5 Core Features:**
1. ✅ Pattern Recognition - Auto-detect task type, delegate appropriate agents
2. ✅ Agent Orchestration - Run multiple agents in parallel
3. ✅ Progress Dashboard - Live TUI showing agent progress
4. ✅ Incremental Results - Stream results as agents complete
5. ✅ State Management - Save/resume on interruption

### What We're NOT Building (Yet)

❌ Adaptive learning (Phase 2)
❌ Confidence scoring (Phase 2)
❌ Speculative execution (Phase 3)
❌ Cross-agent learning (Phase 3)
❌ Delegation modes (Phase 2)

---

## 🏗️ Architecture Overview

```
┌─────────────────────────────────────────────────────────┐
│                  rforge:plan (CLI Entry)                │
└──────────────────────┬──────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────┐
│              Pattern Recognizer                         │
│  • Analyze user request                                 │
│  • Match to pattern (5 patterns)                        │
│  • Return agent configuration                           │
└──────────────────────┬──────────────────────────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────┐
│              Agent Orchestrator                         │
│  • Spawn agents in parallel                             │
│  • Monitor progress                                     │
│  • Collect results                                      │
│  • Handle interruptions                                 │
└──────────────────────┬──────────────────────────────────┘
                       │
         ┌─────────────┼─────────────┬─────────────┐
         ▼             ▼             ▼             ▼
    ┌────────┐    ┌────────┐    ┌────────┐    ┌────────┐
    │Agent 1 │    │Agent 2 │    │Agent 3 │    │Agent 4 │
    │Impact  │    │Tests   │    │Docs    │    │CRAN    │
    └───┬────┘    └───┬────┘    └───┬────┘    └───┬────┘
        │             │             │             │
        └─────────────┴─────────────┴─────────────┘
                       │
                       ▼
┌─────────────────────────────────────────────────────────┐
│              Results Synthesizer                        │
│  • Combine agent outputs                                │
│  • Generate summary                                     │
│  • Provide next steps                                   │
└─────────────────────────────────────────────────────────┘
                       │
                       ▼
                  User Decision
```

---

## 📁 File Structure

```
src/aiterm/rforge/
├── __init__.py
├── cli.py                      # CLI commands (rforge:plan, rforge:resume)
├── patterns/
│   ├── __init__.py
│   ├── recognizer.py           # Pattern matching logic
│   ├── library.py              # Pattern definitions
│   └── types.py                # Pattern data classes
├── agents/
│   ├── __init__.py
│   ├── base.py                 # BaseAgent abstract class
│   ├── orchestrator.py         # Agent orchestration
│   ├── impact_analysis.py      # Impact analysis agent
│   ├── test_coverage.py        # Test coverage agent
│   ├── docs_check.py           # Documentation check agent
│   ├── cran_status.py          # CRAN status agent
│   └── types.py                # Agent data classes
├── ui/
│   ├── __init__.py
│   ├── progress.py             # Progress dashboard (Rich TUI)
│   └── synthesis.py            # Results synthesis UI
└── state/
    ├── __init__.py
    ├── manager.py              # State save/resume
    └── storage.py              # State persistence

tests/aiterm/rforge/
├── test_patterns.py
├── test_agents.py
├── test_orchestrator.py
├── test_state.py
└── integration/
    └── test_end_to_end.py
```

---

## 📅 Day-by-Day Implementation Plan

### Day 1: Foundation & Data Models (Dec 22)

**Goal:** Define interfaces, data classes, and base abstractions

#### Tasks:
1. **Create data models** (2 hours)
   ```python
   # src/aiterm/rforge/patterns/types.py
   @dataclass
   class Pattern:
       name: str
       triggers: list[str]
       agents: list[AgentConfig]
       confidence: float
       description: str

   @dataclass
   class AgentConfig:
       agent_type: str
       priority: Priority  # CRITICAL, HIGH, MEDIUM, LOW
       timeout: int  # seconds
       required: bool

   # src/aiterm/rforge/agents/types.py
   @dataclass
   class AgentResult:
       agent_id: str
       agent_type: str
       status: Status  # SUCCESS, FAILED, TIMEOUT
       output: dict
       duration: float
       errors: list[str]
   ```

2. **Define BaseAgent abstract class** (2 hours)
   ```python
   # src/aiterm/rforge/agents/base.py
   from abc import ABC, abstractmethod

   class BaseAgent(ABC):
       def __init__(self, config: AgentConfig):
           self.config = config
           self.status = Status.PENDING
           self.progress = 0
           self.result = None

       @abstractmethod
       async def execute(self) -> AgentResult:
           """Execute agent logic, return result"""
           pass

       @abstractmethod
       def get_progress(self) -> float:
           """Return progress 0-100"""
           pass

       def cancel(self):
           """Cancel agent execution"""
           self.status = Status.CANCELLED
   ```

3. **Create pattern library structure** (1 hour)
   ```python
   # src/aiterm/rforge/patterns/library.py
   PATTERNS = {
       "code_change": Pattern(
           name="code_change",
           triggers=["update", "modify", "change", "improve"],
           agents=[
               AgentConfig("impact_analysis", Priority.CRITICAL, 120, required=True),
               AgentConfig("test_coverage", Priority.HIGH, 60, required=True),
               AgentConfig("docs_check", Priority.MEDIUM, 30, required=False),
           ],
           confidence=0.95,
           description="Code modification requiring cascade analysis"
       ),
       # ... more patterns
   }
   ```

**Deliverable:** Core data models and interfaces defined
**Test:** Can instantiate patterns and agents

---

### Day 2: Pattern Recognition (Dec 23)

**Goal:** Implement pattern matching from user input

#### Tasks:
1. **Build pattern recognizer** (3 hours)
   ```python
   # src/aiterm/rforge/patterns/recognizer.py
   class PatternRecognizer:
       def __init__(self):
           self.patterns = PATTERNS

       def recognize(self, user_input: str) -> tuple[Pattern, float]:
           """
           Analyze user input, return best matching pattern + confidence
           """
           # Tokenize input
           tokens = self._tokenize(user_input)

           # Score each pattern
           scores = {}
           for pattern_name, pattern in self.patterns.items():
               score = self._score_pattern(tokens, pattern)
               scores[pattern_name] = score

           # Return best match
           best_pattern = max(scores, key=scores.get)
           confidence = scores[best_pattern]

           return self.patterns[best_pattern], confidence

       def _score_pattern(self, tokens: list[str], pattern: Pattern) -> float:
           """Score how well tokens match pattern triggers"""
           matches = sum(1 for token in tokens if token in pattern.triggers)
           return matches / len(pattern.triggers) if pattern.triggers else 0.0
   ```

2. **Add context detection** (2 hours)
   ```python
   def detect_context(self) -> dict:
       """Detect git status, current package, modified files"""
       context = {}

       # Git status
       if self._in_git_repo():
           context['git'] = {
               'branch': self._git_branch(),
               'modified_files': self._git_modified_files(),
               'package': self._detect_package()
           }

       return context
   ```

3. **Write tests** (1 hour)
   ```python
   # tests/aiterm/rforge/test_patterns.py
   def test_recognize_code_change():
       recognizer = PatternRecognizer()
       pattern, confidence = recognizer.recognize(
           "Update RMediation bootstrap algorithm"
       )
       assert pattern.name == "code_change"
       assert confidence > 0.8
   ```

**Deliverable:** Pattern recognition working with 5 patterns
**Test:** Can match user inputs to patterns correctly

---

### Day 3: Agent Base Implementation (Dec 24)

**Goal:** Implement first 2 agents (Impact Analysis, Test Coverage)

#### Tasks:
1. **Implement Impact Analysis Agent** (3 hours)
   ```python
   # src/aiterm/rforge/agents/impact_analysis.py
   class ImpactAnalysisAgent(BaseAgent):
       async def execute(self) -> AgentResult:
           start_time = time.time()

           try:
               # 1. Scan dependencies
               self.progress = 25
               deps = await self._scan_dependencies()

               # 2. Find affected packages
               self.progress = 50
               affected = await self._find_affected_packages(deps)

               # 3. Calculate cascade
               self.progress = 75
               cascade = await self._calculate_cascade(affected)

               # 4. Estimate effort
               self.progress = 90
               effort = self._estimate_effort(cascade)

               self.progress = 100
               return AgentResult(
                   agent_id=self.id,
                   agent_type="impact_analysis",
                   status=Status.SUCCESS,
                   output={
                       'affected_packages': affected,
                       'cascade_steps': cascade,
                       'estimated_hours': effort
                   },
                   duration=time.time() - start_time,
                   errors=[]
               )
           except Exception as e:
               return AgentResult(
                   agent_id=self.id,
                   agent_type="impact_analysis",
                   status=Status.FAILED,
                   output={},
                   duration=time.time() - start_time,
                   errors=[str(e)]
               )

       async def _scan_dependencies(self) -> dict:
           """Read DESCRIPTION, find Depends/Imports"""
           # Implementation
           pass
   ```

2. **Implement Test Coverage Agent** (2 hours)
   ```python
   # src/aiterm/rforge/agents/test_coverage.py
   class TestCoverageAgent(BaseAgent):
       async def execute(self) -> AgentResult:
           # Run R CMD check
           # Parse test results
           # Calculate coverage
           # Return structured result
           pass
   ```

3. **Write agent tests** (1 hour)
   ```python
   # tests/aiterm/rforge/test_agents.py
   @pytest.mark.asyncio
   async def test_impact_analysis_agent():
       config = AgentConfig("impact_analysis", Priority.HIGH, 60, True)
       agent = ImpactAnalysisAgent(config)
       result = await agent.execute()

       assert result.status == Status.SUCCESS
       assert 'affected_packages' in result.output
   ```

**Deliverable:** 2 working agents (Impact, Tests)
**Test:** Agents execute and return structured results

---

### Day 4: Agent Orchestrator (Dec 25)

**Goal:** Implement parallel agent execution with monitoring

#### Tasks:
1. **Build orchestrator core** (4 hours)
   ```python
   # src/aiterm/rforge/agents/orchestrator.py
   class AgentOrchestrator:
       def __init__(self, pattern: Pattern):
           self.pattern = pattern
           self.agents: list[BaseAgent] = []
           self.results: dict[str, AgentResult] = {}
           self.status = OrchestratorStatus.READY

       def spawn_agents(self):
           """Create agent instances from pattern config"""
           for agent_config in self.pattern.agents:
               agent_class = self._get_agent_class(agent_config.agent_type)
               agent = agent_class(agent_config)
               self.agents.append(agent)

       async def execute_all(self) -> dict[str, AgentResult]:
           """Execute all agents in parallel"""
           self.status = OrchestratorStatus.RUNNING

           # Create tasks for all agents
           tasks = [agent.execute() for agent in self.agents]

           # Execute in parallel with timeout
           try:
               results = await asyncio.gather(*tasks, return_exceptions=True)

               # Store results
               for agent, result in zip(self.agents, results):
                   if isinstance(result, Exception):
                       result = self._create_error_result(agent, result)
                   self.results[agent.id] = result

               self.status = OrchestratorStatus.COMPLETE
               return self.results

           except asyncio.CancelledError:
               self.status = OrchestratorStatus.CANCELLED
               return self.results

       def get_progress(self) -> dict:
           """Get current progress of all agents"""
           return {
               agent.id: {
                   'type': agent.config.agent_type,
                   'progress': agent.get_progress(),
                   'status': agent.status
               }
               for agent in self.agents
           }
   ```

2. **Add cancellation handling** (1 hour)
   ```python
   async def cancel_all(self):
       """Cancel all running agents"""
       for agent in self.agents:
           agent.cancel()

       # Save partial results
       await self._save_partial_state()
   ```

3. **Write orchestrator tests** (1 hour)
   ```python
   @pytest.mark.asyncio
   async def test_orchestrator_parallel_execution():
       pattern = PATTERNS['code_change']
       orchestrator = AgentOrchestrator(pattern)
       orchestrator.spawn_agents()

       results = await orchestrator.execute_all()

       assert len(results) == len(pattern.agents)
       assert all(r.status in [Status.SUCCESS, Status.FAILED] for r in results.values())
   ```

**Deliverable:** Working orchestrator that runs agents in parallel
**Test:** Can execute multiple agents concurrently

---

### Day 5: Progress Dashboard (Dec 26)

**Goal:** Real-time TUI showing agent progress

#### Tasks:
1. **Build progress dashboard** (4 hours)
   ```python
   # src/aiterm/rforge/ui/progress.py
   from rich.live import Live
   from rich.table import Table
   from rich.progress import Progress, BarColumn, TextColumn

   class ProgressDashboard:
       def __init__(self, orchestrator: AgentOrchestrator):
           self.orchestrator = orchestrator
           self.start_time = time.time()

       def create_table(self) -> Table:
           """Create progress table"""
           table = Table(title="RForge Analysis")
           table.add_column("Agent", style="cyan")
           table.add_column("Progress", style="green")
           table.add_column("Status", style="yellow")
           table.add_column("Time", style="magenta")

           progress_data = self.orchestrator.get_progress()

           for agent_id, data in progress_data.items():
               # Progress bar
               bar = self._create_progress_bar(data['progress'])

               # Status emoji
               status_emoji = self._status_emoji(data['status'])

               # Elapsed time
               elapsed = self._elapsed_time(agent_id)

               table.add_row(
                   data['type'],
                   bar,
                   f"{status_emoji} {data['status']}",
                   elapsed
               )

           return table

       async def monitor(self):
           """Live monitoring loop"""
           with Live(self.create_table(), refresh_per_second=4) as live:
               while self.orchestrator.status == OrchestratorStatus.RUNNING:
                   live.update(self.create_table())
                   await asyncio.sleep(0.25)

       def _create_progress_bar(self, progress: float) -> str:
           """Create progress bar string"""
           filled = int(progress / 10)
           empty = 10 - filled
           return f"[{'█' * filled}{'░' * empty}] {progress:.0f}%"
   ```

2. **Add incremental results display** (2 hours)
   ```python
   def display_result(self, agent_type: str, result: AgentResult):
       """Display result as it completes"""
       console = Console()

       if result.status == Status.SUCCESS:
           console.print(f"\n[green]✓ {agent_type} complete[/green]")
           console.print(Panel(
               self._format_result(result.output),
               title=f"{agent_type} Results",
               border_style="green"
           ))
       else:
           console.print(f"\n[red]✗ {agent_type} failed[/red]")
           console.print(Panel(
               "\n".join(result.errors),
               title="Errors",
               border_style="red"
           ))
   ```

**Deliverable:** Live progress dashboard with Rich TUI
**Test:** Dashboard updates in real-time, shows accurate progress

---

### Day 6: State Management (Dec 27)

**Goal:** Save/resume capability for interrupted sessions

#### Tasks:
1. **Implement state manager** (3 hours)
   ```python
   # src/aiterm/rforge/state/manager.py
   class StateManager:
       def __init__(self, state_dir: Path = Path.home() / ".rforge"):
           self.state_dir = state_dir
           self.state_dir.mkdir(exist_ok=True)

       def save_state(self,
                      task_id: str,
                      pattern: Pattern,
                      orchestrator: AgentOrchestrator) -> str:
           """Save current state to disk"""
           state = {
               'task_id': task_id,
               'timestamp': datetime.now().isoformat(),
               'pattern': asdict(pattern),
               'agents': [
                   {
                       'id': agent.id,
                       'type': agent.config.agent_type,
                       'status': agent.status.value,
                       'progress': agent.get_progress(),
                       'result': asdict(agent.result) if agent.result else None
                   }
                   for agent in orchestrator.agents
               ],
               'results': {
                   agent_id: asdict(result)
                   for agent_id, result in orchestrator.results.items()
               }
           }

           state_file = self.state_dir / f"{task_id}.json"
           with open(state_file, 'w') as f:
               json.dump(state, f, indent=2)

           return str(state_file)

       def load_state(self, task_id: str) -> dict:
           """Load state from disk"""
           state_file = self.state_dir / f"{task_id}.json"

           if not state_file.exists():
               raise FileNotFoundError(f"State {task_id} not found")

           with open(state_file, 'r') as f:
               return json.load(f)

       def resume_orchestrator(self, state: dict) -> AgentOrchestrator:
           """Recreate orchestrator from saved state"""
           pattern = Pattern(**state['pattern'])
           orchestrator = AgentOrchestrator(pattern)

           # Restore completed results
           for agent_id, result_dict in state['results'].items():
               orchestrator.results[agent_id] = AgentResult(**result_dict)

           # Spawn only incomplete agents
           for agent_data in state['agents']:
               if agent_data['status'] != Status.SUCCESS.value:
                   orchestrator.spawn_agents()  # Spawn remaining
                   break

           return orchestrator
   ```

2. **Add signal handlers** (1 hour)
   ```python
   # src/aiterm/rforge/cli.py
   import signal

   class InterruptHandler:
       def __init__(self, state_manager: StateManager):
           self.state_manager = state_manager
           self.task_id = None
           self.orchestrator = None

       def setup(self, task_id: str, orchestrator: AgentOrchestrator):
           self.task_id = task_id
           self.orchestrator = orchestrator
           signal.signal(signal.SIGINT, self._handle_interrupt)

       def _handle_interrupt(self, sig, frame):
           """Handle Ctrl+C"""
           console = Console()
           console.print("\n[yellow]⏸  Pausing analysis...[/yellow]")

           # Save state
           state_file = self.state_manager.save_state(
               self.task_id,
               self.orchestrator.pattern,
               self.orchestrator
           )

           console.print(f"[green]State saved: {self.task_id}[/green]")
           console.print(f"\nResume anytime with:")
           console.print(f"  [cyan]rforge:resume {self.task_id}[/cyan]")

           sys.exit(0)
   ```

3. **Implement resume command** (1 hour)
   ```python
   @app.command()
   def resume(task_id: str):
       """Resume a paused analysis"""
       state_manager = StateManager()

       try:
           # Load state
           state = state_manager.load_state(task_id)

           console.print(f"[cyan]Resuming analysis: {task_id}[/cyan]")
           console.print(f"Original task: {state['pattern']['description']}")

           # Recreate orchestrator
           orchestrator = state_manager.resume_orchestrator(state)

           # Continue execution
           asyncio.run(execute_with_dashboard(orchestrator))

       except FileNotFoundError:
           console.print(f"[red]State {task_id} not found[/red]")
   ```

**Deliverable:** Save/resume working with Ctrl+C handling
**Test:** Can interrupt and resume analysis

---

### Day 7: Results Synthesis (Dec 28)

**Goal:** Combine agent outputs into coherent summary

#### Tasks:
1. **Build results synthesizer** (4 hours)
   ```python
   # src/aiterm/rforge/ui/synthesis.py
   class ResultsSynthesizer:
       def __init__(self, results: dict[str, AgentResult]):
           self.results = results

       def synthesize(self) -> Panel:
           """Create synthesis summary"""

           # Extract key data
           impact = self._extract_impact()
           quality = self._extract_quality()
           maintenance = self._extract_maintenance()
           next_steps = self._generate_next_steps()

           # Build summary text
           summary = self._build_summary_text(
               impact, quality, maintenance, next_steps
           )

           return Panel(
               summary,
               title="[bold]ANALYSIS COMPLETE[/bold]",
               border_style="green",
               padding=(1, 2)
           )

       def _extract_impact(self) -> dict:
           """Extract impact data from impact_analysis agent"""
           impact_result = self.results.get('impact_analysis')
           if not impact_result:
               return {'level': 'UNKNOWN', 'details': []}

           affected = len(impact_result.output.get('affected_packages', []))
           hours = impact_result.output.get('estimated_hours', 0)

           if affected == 0:
               level = 'LOW'
           elif affected <= 2:
               level = 'MEDIUM'
           else:
               level = 'HIGH'

           return {
               'level': level,
               'affected_packages': affected,
               'estimated_hours': hours
           }

       def _build_summary_text(self, impact, quality, maintenance, next_steps) -> str:
           """Build formatted summary text"""
           text = []

           # Impact section
           text.append(f"🎯 [bold]IMPACT: {impact['level']}[/bold]")
           text.append(f"  • {impact['affected_packages']} packages affected")
           text.append(f"  • Estimated: {impact['estimated_hours']} hours")
           text.append("")

           # Quality section
           text.append(f"✅ [bold]QUALITY: {quality['level']}[/bold]")
           text.append(f"  • Tests: {quality['test_summary']}")
           text.append(f"  • Coverage: {quality['coverage']}%")
           text.append("")

           # Next steps
           text.append("📋 [bold]RECOMMENDED NEXT STEPS:[/bold]")
           for i, step in enumerate(next_steps, 1):
               text.append(f"  {i}. {step}")

           return "\n".join(text)
   ```

2. **Add options menu** (1 hour)
   ```python
   def show_options(self) -> str:
       """Show options menu, return user choice"""
       console = Console()

       console.print("\n[bold]Options:[/bold]")
       console.print("  [1] Generate detailed cascade plan")
       console.print("  [2] Auto-fix documentation and start coding")
       console.print("  [3] Save analysis and decide later")
       console.print("  [4] View detailed agent outputs")

       choice = Prompt.ask("Choose option [1-4]", choices=["1", "2", "3", "4"])
       return choice
   ```

3. **Write synthesis tests** (1 hour)

**Deliverable:** Working results synthesis with options menu
**Test:** Synthesis combines multiple agent outputs correctly

---

### Day 8: Integration & Testing (Dec 29-30)

**Goal:** End-to-end integration and comprehensive testing

#### Tasks:
1. **Build main CLI command** (2 hours)
   ```python
   # src/aiterm/rforge/cli.py
   @app.command()
   def plan(
       task_description: str,
       auto: bool = False  # For future: auto-execute without prompts
   ):
       """Plan an R package development task"""
       console = Console()

       # 1. Generate task ID
       task_id = generate_task_id()

       # 2. Recognize pattern
       recognizer = PatternRecognizer()
       pattern, confidence = recognizer.recognize(task_description)

       console.print(f"\n[cyan]Analyzing request...[/cyan]")
       console.print(f"Pattern: {pattern.name} ({confidence:.0%} confident)")

       # 3. Create orchestrator
       orchestrator = AgentOrchestrator(pattern)
       orchestrator.spawn_agents()

       console.print(f"Delegating to {len(orchestrator.agents)} agents:\n")
       for agent in orchestrator.agents:
           console.print(f"  • {agent.config.agent_type} ({agent.config.priority.name})")

       # 4. Setup interrupt handler
       state_manager = StateManager()
       interrupt_handler = InterruptHandler(state_manager)
       interrupt_handler.setup(task_id, orchestrator)

       # 5. Execute with progress dashboard
       asyncio.run(execute_with_dashboard(task_id, orchestrator, state_manager))

   async def execute_with_dashboard(
       task_id: str,
       orchestrator: AgentOrchestrator,
       state_manager: StateManager
   ):
       """Execute orchestrator with live dashboard"""
       # Create progress dashboard
       dashboard = ProgressDashboard(orchestrator)

       # Start monitoring task
       monitor_task = asyncio.create_task(dashboard.monitor())

       # Execute agents
       execute_task = asyncio.create_task(orchestrator.execute_all())

       # Wait for completion
       results = await execute_task

       # Stop monitoring
       monitor_task.cancel()

       # Show incremental results
       for agent_id, result in results.items():
           dashboard.display_result(agent_id, result)

       # Synthesize results
       synthesizer = ResultsSynthesizer(results)
       summary = synthesizer.synthesize()

       console = Console()
       console.print(summary)

       # Show options
       choice = synthesizer.show_options()

       # Handle choice
       handle_user_choice(choice, results, state_manager)
   ```

2. **Write integration tests** (3 hours)
   ```python
   # tests/aiterm/rforge/integration/test_end_to_end.py
   @pytest.mark.asyncio
   async def test_full_workflow():
       """Test complete workflow from input to synthesis"""

       # 1. Pattern recognition
       recognizer = PatternRecognizer()
       pattern, conf = recognizer.recognize("Update RMediation bootstrap")
       assert pattern.name == "code_change"

       # 2. Orchestration
       orchestrator = AgentOrchestrator(pattern)
       orchestrator.spawn_agents()
       results = await orchestrator.execute_all()

       # 3. All agents completed
       assert len(results) > 0
       assert all(r.status in [Status.SUCCESS, Status.FAILED] for r in results.values())

       # 4. Synthesis
       synthesizer = ResultsSynthesizer(results)
       summary = synthesizer.synthesize()
       assert summary is not None

   def test_save_and_resume():
       """Test state save/resume"""
       task_id = "test_123"
       state_manager = StateManager()

       # Create initial state
       pattern = PATTERNS['code_change']
       orchestrator = AgentOrchestrator(pattern)

       # Save
       state_file = state_manager.save_state(task_id, pattern, orchestrator)
       assert Path(state_file).exists()

       # Resume
       restored = state_manager.resume_orchestrator(
           state_manager.load_state(task_id)
       )
       assert restored.pattern.name == pattern.name
   ```

3. **Documentation** (1 hour)
   - Write README for rforge module
   - Add docstrings to all classes
   - Create usage examples

4. **Bug fixes and polish** (2 hours)
   - Fix any issues found in testing
   - Improve error messages
   - Add logging

**Deliverable:** Complete working MVP
**Test:** Can run full workflow end-to-end

---

## 🧪 Testing Strategy

### Unit Tests (Run Daily)
```bash
# Test individual components
pytest tests/aiterm/rforge/test_patterns.py
pytest tests/aiterm/rforge/test_agents.py
pytest tests/aiterm/rforge/test_orchestrator.py
pytest tests/aiterm/rforge/test_state.py
```

### Integration Tests (Run on Day 8)
```bash
# Test full workflow
pytest tests/aiterm/rforge/integration/ -v
```

### Manual Testing Checklist

**Day 8 Manual Tests:**
- [ ] Run `rforge:plan "Update RMediation bootstrap"`
- [ ] Verify pattern recognition correct
- [ ] Check agents spawn in parallel
- [ ] Confirm progress dashboard updates
- [ ] Press Ctrl+C mid-execution
- [ ] Verify state saved
- [ ] Run `rforge:resume <task_id>`
- [ ] Confirm execution resumes
- [ ] Check results synthesis quality
- [ ] Test all 5 patterns work

---

## 🎯 Success Criteria

### Functional Requirements
✅ User types one command → auto-delegation happens
✅ 3-5 agents run in parallel
✅ Live progress dashboard with accurate progress
✅ Results stream as they complete
✅ Can interrupt with Ctrl+C and resume later
✅ Final synthesis with actionable next steps
✅ Works for all 5 patterns

### Performance Requirements
✅ Pattern recognition: < 1 second
✅ Agent spawning: < 2 seconds
✅ Total analysis time: < 3 minutes for typical case
✅ Dashboard refresh rate: 4 times/second
✅ State save: < 1 second
✅ Resume: < 2 seconds

### ADHD-Friendly Requirements
✅ Minimal user decisions (just approve pattern or not)
✅ Visible progress (no black box)
✅ Incremental feedback (stream results)
✅ Interrupt-friendly (Ctrl+C works gracefully)
✅ Clear next steps (actionable guidance)

---

## 📦 Dependencies

### New Python Packages
```toml
# pyproject.toml additions
[project.dependencies]
rich = "^13.7.0"          # TUI, progress bars, formatting
asyncio = "*"             # Async agent execution
aiofiles = "^23.2.1"      # Async file I/O
```

### R Packages (for agents)
```r
# Required R packages
devtools     # R CMD check
testthat     # Test execution
covr         # Coverage calculation
pkgdown      # Documentation checks
```

---

## 🐛 Known Risks & Mitigation

### Risk 1: Agent Timeouts
**Problem:** Agents might take longer than expected
**Mitigation:**
- Set generous timeouts (2-5 min)
- Implement soft/hard timeout (warn at 80%, kill at 100%)
- Allow partial results

### Risk 2: Async Complexity
**Problem:** Async code harder to debug
**Mitigation:**
- Extensive logging
- Unit test each agent separately
- Use asyncio debugging tools

### Risk 3: State Corruption
**Problem:** Interrupted state might be invalid
**Mitigation:**
- Atomic file writes (write to .tmp, rename)
- Validate state on load
- Keep previous states (state-001.json, state-002.json)

### Risk 4: Progress Accuracy
**Problem:** Agent progress might be inaccurate
**Mitigation:**
- Start with simple progress (0% → 100%)
- Improve accuracy in Phase 2
- Use time-based estimates if needed

---

## 🚀 Launch Checklist (Day 8)

**Pre-Launch:**
- [ ] All unit tests passing
- [ ] Integration tests passing
- [ ] Manual testing complete
- [ ] Documentation written
- [ ] Code reviewed

**Launch:**
- [ ] Merge to `dev` branch
- [ ] Tag as `v0.2.0-alpha.1`
- [ ] Test on real R package work
- [ ] Gather feedback
- [ ] Create issues for Phase 2

**Post-Launch:**
- [ ] Document learnings
- [ ] Identify quick wins for Phase 2
- [ ] Plan Phase 2 priorities

---

## 📈 Metrics to Track

**During Development:**
- Lines of code written
- Test coverage %
- Bugs found/fixed
- Time spent per component

**After Launch:**
- Average analysis time
- Agent success rate
- Interrupt/resume usage
- Pattern recognition accuracy
- User satisfaction (qualitative)

---

## 🎓 Learning Opportunities

**For You (Implementation Skills):**
- Async Python programming
- Rich TUI development
- Agent orchestration patterns
- State management in CLI tools
- ADHD-friendly UX design

**For RForge (System Improvements):**
- Which patterns are most used?
- Which agents are most valuable?
- Where do users interrupt?
- What results do they value most?
- How accurate are time estimates?

---

## 📚 References

**Python Async:**
- [Real Python: Async IO](https://realpython.com/async-io-python/)
- [Python asyncio docs](https://docs.python.org/3/library/asyncio.html)

**Rich TUI:**
- [Rich documentation](https://rich.readthedocs.io/)
- [Rich progress examples](https://rich.readthedocs.io/en/latest/progress.html)

**Design Patterns:**
- Agent pattern for parallel execution
- Observer pattern for progress monitoring
- State pattern for save/resume

---

## 💬 Daily Standup Template

**What I did yesterday:**
**What I'm doing today:**
**Blockers:**
**Questions:**

---

## ✅ Definition of Done

A feature is done when:
1. ✅ Code written and committed
2. ✅ Unit tests written and passing
3. ✅ Manually tested
4. ✅ Documented (docstrings + README)
5. ✅ Reviewed (self-review checklist)
6. ✅ No known bugs

---

**Generated:** 2025-12-21
**Timeline:** Dec 22-30, 2025 (8 days)
**Goal:** Working auto-delegation MVP with 5 features
**Success:** User types one command → gets analysis in <3 min

---

## 🔜 Next: Day 1 Implementation

Ready to start Day 1 (Foundation & Data Models)?

**Day 1 Tasks:**
1. Create data models (patterns/types.py, agents/types.py)
2. Define BaseAgent abstract class
3. Create pattern library with 5 patterns
4. Write basic tests

**Estimated time:** 5 hours
**Deliverable:** Core interfaces defined, patterns ready

Let's go! 🚀
