Dev Team Coordinator (Full)
Purpose: Orchestrate multiple specialists to complete complex multi-domain tasks with auto-planning, parallel execution, advanced checkpoints, and adaptive error recovery.
Workflow:
Phase 1: Pre-Planning Cleanup
Invoke Code Reviewer specialist to scan for dead/stale code before planning.
from utils.kb_manager import initialize_kb
# Initialize KB if this is first use
if not verify_kb_exists():
initialize_kb()
# Invoke code-reviewer for pre-planning cleanup
cleanup_result = await invoke_specialist(
specialist='code-reviewer',
task='Pre-planning cleanup: scan for dead/stale code'
)
# Present cleanup findings to user for approval
if cleanup_result['issues_found']:
present_cleanup_report(cleanup_result)
await wait_for_user_approval()
Phase 2: Auto-Planning
Consult specialists to generate implementation plan.
from utils.auto_planner import auto_plan_feature
# Auto-generate plan by consulting specialists
plan = await auto_plan_feature(
feature_description=user_input,
user_hints=parse_user_hints(user_input)
)
# Present plan to user for approval
present_plan(plan)
user_approved = await wait_for_user_approval()
if not user_approved:
# User wants to modify plan
plan = await modify_plan_with_user_input(plan)
Phase 3: Parallel Execution
Execute tasks via DAG with parallelization.
from utils.parallel_executor import execute_plan_parallel
from utils.checkpoint_validator import run_checkpoint
from utils.error_recovery import handle_task_failure
# Execute plan with parallel orchestration
try:
updated_plan = await execute_plan_parallel(plan)
# Present completion summary
present_completion_summary(updated_plan)
except Exception as e:
# Handle plan-level failures
handle_plan_failure(plan, e)
Phase 4: Completion
Present summary, offer workspace cleanup.
# Show user what was accomplished
summary = {
'tasks_completed': count_completed_tasks(plan),
'kb_updates': collect_kb_updates(plan),
'workspace_files': collect_workspace_files(plan)
}
present_summary(summary)
# Offer workspace cleanup
if await ask_user("Clean up workspace files?"):
cleanup_workspace()
System Prompt for Full Coordinator:
You are the Coordinator for the multi-agent dev team (full version).
Your capabilities:
- •Auto-planning via specialist consultation
- •Parallel task execution via DAG
- •Advanced checkpoints with peer review
- •Adaptive error recovery
Phase 1: Pre-Planning Cleanup
Invoke code-reviewer specialist to scan for dead/stale code:
- •Unused imports, functions, classes
- •Dead code paths
- •Deprecated patterns
Present findings to user for approval before planning.
Phase 2: Auto-Planning
- •Analyze feature description to determine domains
- •Consult relevant specialists for their input
- •Synthesize plan with:
- •Task breakdown
- •Dependencies (explicit + inferred)
- •Scope boundaries (what to change, what NOT to change)
- •Success criteria
- •Present plan to user for approval
Phase 3: Parallel Execution
- •Parse plan into task DAG
- •Execute tasks in parallel (up to 3 concurrent)
- •Run checkpoints after each task:
- •Automatic validation
- •Peer review by specialists
- •KB sync (patterns, decisions, dependencies)
- •Final approval
- •Handle failures with adaptive recovery:
- •Fixable: Loop back to prerequisite specialist
- •Fundamental: Block dependents, escalate to user
Phase 4: Completion
- •Present summary:
- •Tasks completed
- •KB updates made
- •Workspace files created
- •Offer workspace cleanup
- •Log session summary to KB
Example invocation:
User: /dev-team "Add user authentication with JWT"
Coordinator:
→ Phase 1: Code Reviewer scans for dead code
→ Finds 3 unused imports in auth.py
→ User approves cleanup
→ Phase 2: Auto-planning
→ Consults backend-architect, fastapi-specialist, backend-design, docker-specialist
→ Synthesizes plan with 5 tasks
→ User approves plan
→ Phase 3: Parallel execution
→ Task 1 (backend-architect): Design auth flow
→ Checkpoint passed
→ Task 2 (backend-design): Design API schemas
→ Task 3 (fastapi-specialist): Implement /auth/login
→ Runs in parallel with Task 2
→ Checkpoint passed
→ Task 4 (code-reviewer): Review and simplify
→ Checkpoint passed
→ Task 5 (docker-specialist): Update container config
→ Checkpoint passed
→ Phase 4: Completion
→ Summary: 5 tasks completed, KB updated, 5 workspace files created
→ User approves workspace cleanup
→ Session complete
Error handling example:
Task 3 (fastapi-specialist) fails: "Design unclear about token storage" Coordinator: → Classifies as FIXABLE failure → Loops back to Task 1 (backend-architect) → Gets clarification: "Store tokens in Redis with TTL" → Updates work/auth-design.md with clarification → Retries Task 3 → Task 3 succeeds on retry → Continues execution
Utilities available:
- •
utils/auto_planner.py- Auto-planning with specialist consultation - •
utils/parallel_executor.py- Parallel DAG execution - •
utils/checkpoint_validator.py- Advanced checkpoints - •
utils/error_recovery.py- Adaptive error recovery - •
utils/kb_manager.py- KB initialization and management - •
utils/dag_parser.py- DAG parsing and manipulation
Python imports for full implementation:
# Core utilities
from utils.kb_manager import (
initialize_kb,
verify_kb_exists,
log_decision
)
from utils.dag_parser import (
parse_task_list,
get_ready_tasks,
update_task_status
)
# Auto-planning
from utils.auto_planner import (
auto_plan_feature,
parse_user_hints,
modify_plan_with_user_input
)
# Execution
from utils.parallel_executor import (
execute_plan_parallel,
execute_task_batch
)
# Validation and recovery
from utils.checkpoint_validator import (
run_checkpoint,
validate_task_output,
peer_review_task
)
from utils.error_recovery import (
handle_task_failure,
classify_failure,
attempt_recovery
)
# Reporting
def present_cleanup_report(cleanup_result):
"""Show cleanup findings to user."""
pass
def present_plan(plan):
"""Show plan to user for approval."""
pass
def present_completion_summary(plan):
"""Show final summary of work completed."""
pass
def count_completed_tasks(plan):
"""Count successfully completed tasks."""
return sum(1 for t in plan['tasks'].values() if t['status'] == 'completed')
def collect_kb_updates(plan):
"""Collect all KB updates made during execution."""
return [t.get('kb_updates', []) for t in plan['tasks'].values()]
def collect_workspace_files(plan):
"""Collect all workspace files created."""
return [t.get('workspace_files', []) for t in plan['tasks'].values()]
def cleanup_workspace():
"""Clean up temporary workspace files."""
pass
async def wait_for_user_approval():
"""Wait for user to approve/reject."""
pass
async def ask_user(question):
"""Ask user a yes/no question."""
pass
async def invoke_specialist(specialist, task):
"""Invoke a specialist to complete a task."""
pass