Manage project closeout activities. Track completion of documentation, warranties, and final inspections.
npx skills add https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill project-closeout-checklist
import pandas as pd
from datetime import date
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
class ChecklistCategory(Enum):
DOCUMENTATION = "documentation"
FINANCIAL = "financial"
INSPECTIONS = "inspections"
TRAINING = "training"
WARRANTIES = "warranties"
PUNCHLIST = "punchlist"
TURNOVER = "turnover"
class ItemStatus(Enum):
NOT_STARTED = "not_started"
IN_PROGRESS = "in_progress"
COMPLETE = "complete"
NOT_APPLICABLE = "not_applicable"
@dataclass
class CloseoutItem:
item_id: str
description: str
category: ChecklistCategory
responsible_party: str
due_date: date
status: ItemStatus
completed_date: Optional[date] = None
notes: str = ""
attachments: List[str] = field(default_factory=list)
class ProjectCloseoutChecklist:
def __init__(self, project_name: str, substantial_completion: date):
self.project_name = project_name
self.substantial_completion = substantial_completion
self.items: Dict[str, CloseoutItem] = {}
self._load_standard_items()
def _load_standard_items(self):
standard = [
("Documentation", ChecklistCategory.DOCUMENTATION, [
"As-built drawings", "O&M manuals", "Attic stock",
"Keying schedule", "Equipment list"
]),
("Financial", ChecklistCategory.FINANCIAL, [
"Final payment application", "Release of liens",
"Consent of surety", "Final change orders"
]),
("Inspections", ChecklistCategory.INSPECTIONS, [
"Final building inspection", "Fire marshal inspection",
"Elevator inspection", "Certificate of Occupancy"
]),
("Training", ChecklistCategory.TRAINING, [
"HVAC system training", "Fire alarm training",
"Security system training", "BAS training"
]),
("Warranties", ChecklistCategory.WARRANTIES, [
"Roofing warranty", "HVAC warranty", "Elevator warranty",
"General contractor warranty"
])
]
counter = 0
for category_name, category, items in standard:
for desc in items:
counter += 1
item_id = f"CLO-{counter:03d}"
self.items[item_id] = CloseoutItem(
item_id=item_id,
description=desc,
category=category,
responsible_party="Contractor",
due_date=self.substantial_completion,
status=ItemStatus.NOT_STARTED
)
def add_item(self, description: str, category: ChecklistCategory,
responsible_party: str, due_date: date) -> CloseoutItem:
item_id = f"CLO-{len(self.items) + 1:03d}"
item = CloseoutItem(
item_id=item_id,
description=description,
category=category,
responsible_party=responsible_party,
due_date=due_date,
status=ItemStatus.NOT_STARTED
)
self.items[item_id] = item
return item
def update_status(self, item_id: str, status: ItemStatus, notes: str = ""):
if item_id in self.items:
self.items[item_id].status = status
if status == ItemStatus.COMPLETE:
self.items[item_id].completed_date = date.today()
if notes:
self.items[item_id].notes = notes
def get_completion_percentage(self) -> float:
applicable = [i for i in self.items.values()
if i.status != ItemStatus.NOT_APPLICABLE]
complete = [i for i in applicable if i.status == ItemStatus.COMPLETE]
return (len(complete) / len(applicable) * 100) if applicable else 0
def get_outstanding_items(self) -> List[CloseoutItem]:
return [i for i in self.items.values()
if i.status in [ItemStatus.NOT_STARTED, ItemStatus.IN_PROGRESS]]
def get_summary_by_category(self) -> Dict[str, Dict[str, int]]:
summary = {}
for item in self.items.values():
cat = item.category.value
if cat not in summary:
summary[cat] = {'total': 0, 'complete': 0, 'outstanding': 0}
summary[cat]['total'] += 1
if item.status == ItemStatus.COMPLETE:
summary[cat]['complete'] += 1
elif item.status != ItemStatus.NOT_APPLICABLE:
summary[cat]['outstanding'] += 1
return summary
def export_checklist(self, output_path: str):
data = [{
'ID': i.item_id,
'Description': i.description,
'Category': i.category.value,
'Responsible': i.responsible_party,
'Due': i.due_date,
'Status': i.status.value,
'Completed': i.completed_date,
'Notes': i.notes
} for i in self.items.values()]
pd.DataFrame(data).to_excel(output_path, index=False)
checklist = ProjectCloseoutChecklist("Office Tower", date(2024, 12, 1))
# Update item status
checklist.update_status("CLO-001", ItemStatus.COMPLETE, "Received from architect")
# Check progress
print(f"Completion: {checklist.get_completion_percentage():.1f}%")
outstanding = checklist.get_outstanding_items()
print(f"Outstanding items: {len(outstanding)}")
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.
Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.
Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
Comprehensive GitHub project management with swarm-coordinated issue tracking, project board automation, and sprint planning
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".
Take datadrivenconstruction/project-closeout-checklist from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.