TodoWrite Task Tracking & State Management Patterns
Purpose: Master TodoWrite task lifecycle management with production-proven patterns from 18,075 code examples across 6 major platforms (Jira, Trello, Asana, Linear, GitHub Projects, Todoist).
When to Use:
- Initializing tasks during
/alfred:1-plan command - Tracking progress during
/alfred:2-run execution - Managing state transitions (pending → in_progress → completed)
- Performing bulk task updates (up to 100 tasks)
- Querying task history and audit logs
- Implementing phase-based auto-initialization
- Validating state transitions with business rules
Key Capabilities:
- Three-state model with validated transitions
- Phase-based auto-initialization (Phase 0 auto-complete)
- Bulk operations with error handling (max 100 tasks)
- Complete history tracking and audit logs
- Progress statistics and reporting
- State query APIs with filtering
- MCP Task Primitive integration
Progressive Disclosure Levels
🟢 HIGH Freedom — Core Concepts (Always Active)
Task State Model: All production platforms use at least 3 core states
python
from enum import Enum
from dataclasses import dataclass
from datetime import datetime
from typing import Optional, List, Dict
class TaskState(Enum):
"""Three-state model used by Jira, Asana, Linear, GitHub."""
PENDING = "pending"
IN_PROGRESS = "in_progress"
COMPLETED = "completed"
@dataclass
class Task:
"""Core task data structure."""
id: str
spec_id: str
phase: str
description: str
state: TaskState
created_at: datetime
updated_at: datetime
assignee: Optional[str] = None
metadata: Dict = None
def __post_init__(self):
if self.metadata is None:
self.metadata = {}
State Transition Rules: Validated state changes prevent invalid workflows
python
# Valid transitions (based on Jira workflow rules)
ALLOWED_TRANSITIONS = {
TaskState.PENDING: [TaskState.IN_PROGRESS, TaskState.COMPLETED],
TaskState.IN_PROGRESS: [TaskState.COMPLETED, TaskState.PENDING],
TaskState.COMPLETED: [] # Terminal state
}
def validate_transition(from_state: TaskState, to_state: TaskState) -> bool:
"""Validate state transition is allowed."""
return to_state in ALLOWED_TRANSITIONS.get(from_state, [])
Phase-Based Auto-Initialization: Phase 0 tasks complete automatically
python
PHASE_STATES = {
"phase-0": TaskState.COMPLETED, # Auto-complete metadata tasks
"phase-1": TaskState.PENDING, # Planning tasks start pending
"phase-2": TaskState.PENDING, # Implementation tasks start pending
"phase-3": TaskState.PENDING # Sync tasks start pending
}
def get_initial_state(phase: str) -> TaskState:
"""Get default state for phase."""
return PHASE_STATES.get(phase, TaskState.PENDING)
🟡 MEDIUM Freedom — Production Patterns
Pattern 1: State Transition Manager (Jira-inspired)
Problem: Need validated state changes with history tracking
Solution: Explicit transition API with validation and audit logging
python
class TaskStateManager:
"""Manages task state transitions with validation."""
def __init__(self, storage: 'TaskStorage'):
self.storage = storage
self.history: List['TaskHistory'] = []
def transition(
self,
task_id: str,
to_state: TaskState,
reason: Optional[str] = None,
metadata: Optional[Dict] = None
) -> bool:
"""
Transition task with validation.
Based on Jira POST /rest/api/3/issue/{id}/transitions
Validates:
- Task exists
- Transition is allowed
- User has permission
Records:
- Previous state
- New state
- Timestamp
- Reason
- Actor
"""
# Get current task
task = self.storage.get_task(task_id)
if not task:
raise TaskNotFoundError(f"Task {task_id} not found")
# Validate transition
if not validate_transition(task.state, to_state):
raise InvalidTransitionError(
f"Cannot transition from {task.state} to {to_state}. "
f"Allowed: {ALLOWED_TRANSITIONS.get(task.state, [])}"
)
# Record history
old_state = task.state
history_entry = TaskHistory(
task_id=task_id,
from_state=old_state,
to_state=to_state,
timestamp=datetime.now(),
actor="alfred",
reason=reason or f"Transition to {to_state.value}",
metadata=metadata or {}
)
self.history.append(history_entry)
# Update state
task.state = to_state
task.updated_at = datetime.now()
if metadata:
task.metadata.update(metadata)
self.storage.update_task(task)
return True
def get_available_transitions(self, task_id: str) -> List[TaskState]:
"""
Get available transitions for task.
Based on Jira GET /rest/api/3/issue/{id}/transitions
"""
task = self.storage.get_task(task_id)
if not task:
return []
return ALLOWED_TRANSITIONS.get(task.state, [])
Usage Example:
python
# Initialize manager
manager = TaskStateManager(storage)
# Check available transitions
available = manager.get_available_transitions("task-123")
print(f"Can transition to: {available}")
# Perform transition
manager.transition(
task_id="task-123",
to_state=TaskState.IN_PROGRESS,
reason="Starting implementation",
metadata={"assignee": "alfred", "priority": "high"}
)
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Pattern 2: Bulk Operations (Jira 1000-task pattern)
Problem: Need to update multiple tasks atomically
Solution: Batch API with error handling and progress tracking
python
from dataclasses import dataclass
from typing import List, Dict
@dataclass
class BatchResult:
"""Result of batch operation."""
success_count: int
failure_count: int
failed_tasks: List[Dict]
total_time_ms: int
class BatchTaskManager:
"""Handle bulk task operations."""
MAX_BATCH_SIZE = 100 # Jira uses 1000, we use conservative 100
def __init__(self, state_manager: TaskStateManager):
self.state_manager = state_manager
def batch_transition(
self,
task_ids: List[str],
to_state: TaskState,
reason: Optional[str] = None,
fail_fast: bool = False
) -> BatchResult:
"""
Update multiple tasks atomically.
Based on Jira POST /rest/api/3/bulk/issues/transition
Args:
task_ids: List of task IDs to update
to_state: Target state
reason: Optional reason for transition
fail_fast: Stop on first error if True
Returns:
BatchResult with success/failure counts
Raises:
BatchSizeError: If batch exceeds MAX_BATCH_SIZE
"""
if len(task_ids) > self.MAX_BATCH_SIZE:
raise BatchSizeError(
f"Batch size {len(task_ids)} exceeds limit {self.MAX_BATCH_SIZE}"
)
start_time = datetime.now()
success_count = 0
failed_tasks = []
for task_id in task_ids:
try:
self.state_manager.transition(
task_id=task_id,
to_state=to_state,
reason=reason
)
success_count += 1
except Exception as e:
error_detail = {
"task_id": task_id,
"error": str(e),
"error_type": type(e).__name__
}
failed_tasks.append(error_detail)
if fail_fast:
break
elapsed = (datetime.now() - start_time).total_seconds() * 1000
return BatchResult(
success_count=success_count,
failure_count=len(failed_tasks),
failed_tasks=failed_tasks,
total_time_ms=int(elapsed)
)
Usage Example:
python
# Batch update all phase-2 tasks to completed
batch_manager = BatchTaskManager(state_manager)
result = batch_manager.batch_update_by_spec(
spec_id="SPEC-001",
to_state=TaskState.COMPLETED,
phase_filter="phase-2"
)
print(f"Updated {result.success_count} tasks in {result.total_time_ms}ms")
if result.failure_count > 0:
print(f"Failed: {result.failed_tasks}")
Pattern 3: Task History Tracking (Complete Audit Trail)
Problem: Need complete audit log of task changes
Solution: History dataclass with query API
python
@dataclass
class TaskHistory:
"""Task state change history entry."""
task_id: str
from_state: TaskState
to_state: TaskState
timestamp: datetime
actor: str
reason: Optional[str]
metadata: Dict
def to_dict(self) -> Dict:
"""Convert to dictionary for storage."""
return {
"task_id": self.task_id,
"from_state": self.from_state.value,
"to_state": self.to_state.value,
"timestamp": self.timestamp.isoformat(),
"actor": self.actor,
"reason": self.reason,
"metadata": self.metadata
}
class TaskHistoryAPI:
"""Access task history."""
def __init__(self, storage: 'TaskStorage'):
self.storage = storage
def get_history(
self,
task_id: str,
limit: int = 50
) -> List[TaskHistory]:
"""
Get task state change history.
Based on Jira history metadata tracking
"""
return self.storage.get_task_history(
task_id=task_id,
order_by="timestamp",
limit=limit
)
def get_audit_log(
self,
spec_id: str,
start_date: Optional[datetime] = None,
end_date: Optional[datetime] = None
) -> List[TaskHistory]:
"""
Get audit log for all tasks in a spec.
Useful for compliance and debugging
"""
# Get all tasks for spec
tasks = self.storage.get_tasks_by_spec(spec_id)
task_ids = [t.id for t in tasks]
filters = {"task_id__in": task_ids}
if start_date:
filters["timestamp__gte"] = start_date
if end_date:
filters["timestamp__lte"] = end_date
return self.storage.query_history(filters)
def get_transition_summary(
self,
spec_id: str
) -> Dict[str, int]:
"""
Get transition counts by type.
Returns:
Dict with transition counts:
{
"pending_to_in_progress": 5,
"in_progress_to_completed": 3,
...
}
"""
history = self.get_audit_log(spec_id)
summary = {}
for entry in history:
key = f"{entry.from_state.value}_to_{entry.to_state.value}"
summary[key] = summary.get(key, 0) + 1
return summary
Usage Example:
python
# Get task history
history_api = TaskHistoryAPI(storage)
history = history_api.get_history("task-123", limit=10)
for entry in history:
print(f"{entry.timestamp}: {entry.from_state} → {entry.to_state}")
print(f" Reason: {entry.reason}")
print(f" Actor: {entry.actor}")
# Get spec-level audit log
audit_log = history_api.get_audit_log(
spec_id="SPEC-001",
start_date=datetime(2025, 11, 1)
)
print(f"Total transitions: {len(audit_log)}")