as-built-documentation
Manage as-built documentation for project closeout. Track drawing markups, coordinate updates, and verify completeness.
Works with
---
name: as-built-documentation
description: Manage as-built documentation for project closeout. Track drawing markups, coordinate updates, and verify completeness.
license: MIT
---
# As-Built Documentation Manager
## Business Case
### Problem Statement
As-built documentation is often incomplete:
- Field changes not documented
- Drawings not updated consistently
- Missing documentation at closeout
- Difficult to verify completeness
### Solution
Systematic as-built documentation tracking with drawing markup management, completeness verification, and handover preparation.
## Technical Implementation
```python
import pandas as pd
from datetime import datetime, date
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from enum import Enum
class DocumentType(Enum):
DRAWING = "drawing"
SPECIFICATION = "specification"
SUBMITTAL = "submittal"
MANUAL = "manual"
WARRANTY = "warranty"
CERTIFICATE = "certificate"
class MarkupStatus(Enum):
PENDING = "pending"
IN_REVIEW = "in_review"
APPROVED = "approved"
INCORPORATED = "incorporated"
class DocumentStatus(Enum):
DRAFT = "draft"
UNDER_REVIEW = "under_review"
APPROVED = "approved"
FINAL = "final"
@dataclass
class Markup:
markup_id: str
description: str
location: str
marked_by: str
marked_date: date
status: MarkupStatus
cloud_reference: str = ""
notes: str = ""
@dataclass
class AsBuiltDocument:
document_id: str
document_number: str
title: str
document_type: DocumentType
discipline: str
revision: str
status: DocumentStatus
original_file: str
as_built_file: str
markups: List[Markup] = field(default_factory=list)
last_updated: Optional[date] = None
verified_by: str = ""
verified_date: Optional[date] = None
@property
def is_complete(self) -> bool:
return self.status == DocumentStatus.FINAL and all(
m.status == MarkupStatus.INCORPORATED for m in self.markups
)
class AsBuiltDocumentManager:
"""Manage as-built documentation."""
def __init__(self, project_name: str):
self.project_name = project_name
self.documents: Dict[str, AsBuiltDocument] = {}
self._markup_counter = 0
def register_document(self, document_number: str, title: str,
document_type: DocumentType, discipline: str,
original_file: str, revision: str = "0") -> AsBuiltDocument:
doc_id = f"DOC-{len(self.documents) + 1:04d}"
doc = AsBuiltDocument(
document_id=doc_id,
document_number=document_number,
title=title,
document_type=document_type,
discipline=discipline,
revision=revision,
status=DocumentStatus.DRAFT,
original_file=original_file,
as_built_file=""
)
self.documents[doc_id] = doc
return doc
def add_markup(self, doc_id: str, description: str, location: str,
marked_by: str, cloud_reference: str = "") -> Markup:
if doc_id not in self.documents:
raise ValueError(f"Document {doc_id} not found")
self._markup_counter += 1
markup = Markup(
markup_id=f"MKP-{self._markup_counter:05d}",
description=description,
location=location,
marked_by=marked_by,
marked_date=date.today(),
status=MarkupStatus.PENDING,
cloud_reference=cloud_reference
)
self.documents[doc_id].markups.append(markup)
return markup
def update_markup_status(self, doc_id: str, markup_id: str, status: MarkupStatus):
if doc_id in self.documents:
for markup in self.documents[doc_id].markups:
if markup.markup_id == markup_id:
markup.status = status
break
def upload_as_built(self, doc_id: str, file_path: str, new_revision: str = None):
if doc_id not in self.documents:
return
doc = self.documents[doc_id]
doc.as_built_file = file_path
doc.last_updated = date.today()
if new_revision:
doc.revision = new_revision
doc.status = DocumentStatus.UNDER_REVIEW
def verify_document(self, doc_id: str, verified_by: str):
if doc_id not in self.documents:
return
doc = self.documents[doc_id]
doc.verified_by = verified_by
doc.verified_date = date.today()
doc.status = DocumentStatus.FINAL
def get_completeness_report(self) -> Dict[str, Any]:
total = len(self.documents)
complete = sum(1 for d in self.documents.values() if d.is_complete)
pending_markups = sum(
len([m for m in d.markups if m.status != MarkupStatus.INCORPORATED])
for d in self.documents.values()
)
by_discipline = {}
for doc in self.documents.values():
if doc.discipline not in by_discipline:
by_discipline[doc.discipline] = {'total': 0, 'complete': 0}
by_discipline[doc.discipline]['total'] += 1
if doc.is_complete:
by_discipline[doc.discipline]['complete'] += 1
return {
'project': self.project_name,
'total_documents': total,
'complete': complete,
'completion_percent': round(complete / total * 100, 1) if total > 0 else 0,
'pending_markups': pending_markups,
'by_discipline': by_discipline
}
def get_incomplete_documents(self) -> List[AsBuiltDocument]:
return [d for d in self.documents.values() if not d.is_complete]
def export_register(self, output_path: str):
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Document register
doc_data = [{
'ID': d.document_id,
'Number': d.document_number,
'Title': d.title,
'Type': d.document_type.value,
'Discipline': d.discipline,
'Revision': d.revision,
'Status': d.status.value,
'Complete': d.is_complete,
'Markups': len(d.markups),
'Verified By': d.verified_by
} for d in self.documents.values()]
pd.DataFrame(doc_data).to_excel(writer, sheet_name='Register', index=False)
# Markups
markup_data = []
for doc in self.documents.values():
for m in doc.markups:
markup_data.append({
'Document': doc.document_number,
'Markup ID': m.markup_id,
'Description': m.description,
'Location': m.location,
'Marked By': m.marked_by,
'Status': m.status.value
})
if markup_data:
pd.DataFrame(markup_data).to_excel(writer, sheet_name='Markups', index=False)
return output_path
```
## Quick Start
```python
manager = AsBuiltDocumentManager("Office Tower")
# Register document
doc = manager.register_document(
document_number="A-101",
title="Floor Plan Level 1",
document_type=DocumentType.DRAWING,
discipline="Architectural",
original_file="drawings/A-101.pdf"
)
# Add field markup
markup = manager.add_markup(
doc.document_id,
description="Wall moved 6 inches south",
location="Grid B-3",
marked_by="Site Superintendent"
)
# Upload as-built
manager.upload_as_built(doc.document_id, "as-built/A-101-AB.pdf", "AB")
# Verify
manager.verify_document(doc.document_id, "Project Manager")
# Check completeness
report = manager.get_completeness_report()
print(f"Completion: {report['completion_percent']}%")
```
## Resources
- **DDC Book**: Chapter 5 - Project CloseoutMore SEO & Marketing skills
ai-video-generation
skills-101/superpowers
Generate AI videos with Google Veo, Seedance 2.0, HappyHorse, Wan, Grok and 40+ models via inference.sh CLI. Models: Veo 3.1, Veo 3, Seedance 2.0, HappyHorse 1.0, Wan 2.5, Grok Imagine Video, OmniHuman, Fabric, HunyuanVideo. Capabilities: text-to-video, image-to-video, reference-to-video, video editing, lipsync, avatar animation, video upscaling, foley sound. Use for: social media videos, marketing content, explainer videos, product demos, AI avatars. Triggers: video generation, ai video, text to video, image to video, veo, animate image, video from image, ai animation, video generator, generate video, t2v, i2v, ai video maker, create video with ai, runway alternative, pika alternative, sora alternative, kling alternative, seedance, happyhorse
ai-image-generation
skills-101/superpowers
Generate AI images with GPT-Image-2, FLUX, Gemini, Grok, Seedream, Reve and 50+ models via inference.sh CLI. Models: GPT-Image-2, FLUX Dev LoRA, FLUX.2 Klein LoRA, Gemini 3 Pro Image, Grok Imagine, Seedream 4.5, Reve, ImagineArt. Capabilities: text-to-image, image-to-image, inpainting, LoRA, image editing, upscaling, text rendering. Use for: AI art, product mockups, concept art, social media graphics, marketing visuals, illustrations. Triggers: flux, image generation, ai image, text to image, stable diffusion, generate image, ai art, midjourney alternative, dall-e alternative, text2img, t2i, image generator, ai picture, create image with ai, generative ai, ai illustration, grok image, gemini image, gpt image, openai image, chatgpt image
ai-avatar-video
skills-101/superpowers
Create AI avatar and talking head videos via inference.sh CLI. Recommended: P-Video-Avatar (fastest, cheapest, built-in TTS). Also: OmniHuman, Fabric, PixVerse. Audio: Inworld TTS-2 (100+ languages, emotion steering for characters), ElevenLabs, Kokoro. Capabilities: audio-driven avatars, text-to-avatar, lipsync videos, talking head generation, virtual presenters, UGC content. Use for: AI presenters, explainer videos, virtual influencers, dubbing, marketing videos, UGC ads, gaming avatars, NPC dialogue. Triggers: ai avatar, talking head, lipsync, avatar video, virtual presenter, ai spokesperson, audio driven video, heygen alternative, synthesia alternative, talking avatar, lip sync, video avatar, ai presenter, digital human, ugc, ugc video, ugc ad, avatar ugc

