Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill as-built-documentationgit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-documentation)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-documentation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-documentation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-documentation"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-documentation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.01835 |
| Opus 5 | $0.00013 | $0.00918 |
| Sonnet 5 | $0.00005 | $0.00367 |
| Haiku 4.5 | $0.00003 | $0.00184 |
Grade A, and why
as-built-documentation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- as-built-documentation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 257 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 257 lines · 26 tokens per session scan A ea9ce89d2d98
as-built-documentation is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 20d ago), licensed MIT. It adds 26 tokens to every session and 1,835 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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