feat: add multi-feature batching for coding agents

Enable the orchestrator to assign 1-3 features per coding agent subprocess,
selected via dependency chain extension + same-category fill. This reduces
cold-start overhead and leverages shared context across related features.

Orchestrator (parallel_orchestrator.py):
- Add batch tracking: _batch_features and _feature_to_primary data structures
- Add build_feature_batches() with dependency chain + category fill algorithm
- Add start_feature_batch() and _spawn_coding_agent_batch() methods
- Update _on_agent_complete() for batch cleanup across all features
- Update stop_feature() with _feature_to_primary lookup
- Update get_ready_features() to exclude all batch feature IDs
- Update main loop to build batches then spawn per available slot

CLI and agent layer:
- Add --feature-ids (comma-separated) and --batch-size CLI args
- Add feature_ids parameter to run_autonomous_agent() with batch prompt selection
- Add get_batch_feature_prompt() with sequential workflow instructions

WebSocket layer (server/websocket.py):
- Add BATCH_CODING_AGENT_START_PATTERN and BATCH_FEATURES_COMPLETE_PATTERN
- Add _handle_batch_agent_start() and _handle_batch_agent_complete() methods
- Add featureIds field to all agent_update messages
- Track current_feature_id updates as agent moves through batch

Frontend (React UI):
- Add featureIds to ActiveAgent and WSAgentUpdateMessage types
- Update KanbanColumn and DependencyGraph agent-feature maps for batch
- Update AgentCard to show "Batch: #X, #Y, #Z" with active feature highlight
- Add "Features per Agent" segmented control (1-3) in SettingsModal

Settings integration (full stack):
- Add batch_size to schemas, settings router, agent router, process manager
- Default batch_size=3, user-configurable 1-3 via settings UI
- batch_size=1 is functionally identical to pre-batching behavior

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

server/schemas.py

@@ -399,6 +399,7 @@ class SettingsResponse(BaseModel):
     ollama_mode: bool = False  # True if Ollama API is configured via .env
     testing_agent_ratio: int = 1  # Regression testing agents (0-3)
     playwright_headless: bool = True
+    batch_size: int = 3  # Features per coding agent batch (1-3)
 
 
 class ModelsResponse(BaseModel):
@@ -413,6 +414,7 @@ class SettingsUpdate(BaseModel):
     model: str | None = None
     testing_agent_ratio: int | None = None  # 0-3
     playwright_headless: bool | None = None
+    batch_size: int | None = None  # Features per agent batch (1-3)
 
     @field_validator('model')
     @classmethod
@@ -428,6 +430,13 @@ class SettingsUpdate(BaseModel):
             raise ValueError("testing_agent_ratio must be between 0 and 3")
         return v
 
+    @field_validator('batch_size')
+    @classmethod
+    def validate_batch_size(cls, v: int | None) -> int | None:
+        if v is not None and (v < 1 or v > 3):
+            raise ValueError("batch_size must be between 1 and 3")
+        return v
+
 
 # ============================================================================
 # Dev Server Schemas