as-built-tracker

as-built-tracker is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 27 tokens per session (2,909 once invoked), scanned A, original, MIT.

A tracker for as-built documents: the final drawings and records showing what was actually built. It records document types, revisions, and review status through handover.

In plain words
What is it for?
Use it to monitor submissions, reviews, approvals, rejected documents, resubmissions, and handover completeness across architectural, structural, mechanical, electrical, plumbing, civil, and other project records.
Why use it?
It reduces the risk of losing track of hundreds of documents, using outdated revisions, or handing over an incomplete package late.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to monitor submissions, reviews, approvals, rejected documents, resubmissions, and handover completeness across architectural, structural, mechanical, electrical, plumbing, civil, and other project records.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-tracker
Install

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.

Any agent
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill as-built-tracker
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for as-built-tracker

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-tracker/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-tracker)
Your own site
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-tracker"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-tracker/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.

agentmods 80×15 button for as-built-tracker

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-tracker"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/as-built-tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,909 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00027 $0.02909
Opus 5 $0.00014 $0.01455
Sonnet 5 $0.00005 $0.00582
Haiku 4.5 $0.00003 $0.00291

Measured 11d ago against content hash 7566a219b1ac, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

as-built-tracker 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

1_DDC_Toolkit/Closeout/as-built-tracker/SKILL.md · 431 lines

How it starts

The opening of the file, as written. The whole thing — 431 lines — stays where its author put it; the contents beside it link to each section on GitHub.

As-Built Documentation Tracker

Business Case

Problem Statement

As-built documentation challenges:

  • Tracking hundreds of drawings
  • Managing revisions
  • Ensuring completeness
  • Meeting handover deadlines

Solution

Systematic tracking of as-built documentation submissions, revisions, and approval status.

Technical Implementation

import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import date, timedelta
from enum import Enum


class DocumentStatus(Enum):
    NOT_STARTED = "not_started"
    IN_PROGRESS = "in_progress"
    SUBMITTED = "submitted"
    UNDER_REVIEW = "under_review"
    APPROVED = "approved"
    REJECTED = "rejected"
    RESUBMIT = "resubmit"


class DocumentType(Enum):
    ARCHITECTURAL = "architectural"
    STRUCTURAL = "structural"
    MECHANICAL = "mechanical"
    ELECTRICAL = "electrical"
    PLUMBING = "plumbing"
    FIRE_PROTECTION = "fire_protection"
    CIVIL = "civil"
    LANDSCAPE = "landscape"
    SPECIFICATIONS = "specifications"
    O_AND_M = "o_and_m"


@dataclass
class AsBuiltDocument:
    document_id: str
    document_number: str
    title: str
    doc_type: DocumentType
    discipline: str
    contractor: str
    status: DocumentStatus
    current_revision: str
    required_date: date
    submitted_date: Optional[date] = None
    approved_date: Optional[date] = None
    reviewer: str = ""
    comments: str = ""
    file_path: str = ""


@dataclass
class DocumentSubmission:
    submission_id: str
    document_id: str
    revision: str
    submission_date: date
    submitted_by: str
    file_path: str
    status: DocumentStatus
    review_comments: str = ""


class AsBuiltTracker:
    """Track as-built documentation."""

    def __init__(self, project_name: str, handover_date: date):
        self.project_name = project_name
        self.handover_date = handover_date
        self.documents: Dict[str, AsBuiltDocument] = {}
        self.submissions: List[DocumentSubmission] = []
        self._next_id = 1

    def add_document(self,
                     document_number: str,
                     title: str,
                     doc_type: DocumentType,
                     discipline: str,
                     contractor: str,
                     required_date: date = None) -> AsBuiltDocument:
        """Add document to tracking."""

        doc_id = f"DOC-{self._next_id:04d}"
        self._next_id += 1

        if required_date is None:
            required_date = self.handover_date - timedelta(days=14)

        doc = AsBuiltDocument(
            document_id=doc_id,
            document_number=document_number,
            title=title,
            doc_type=doc_type,
            discipline=discipline,
            contractor=contractor,
            status=DocumentStatus.NOT_STARTED,
            current_revision="0",
            required_date=required_date
        )

        self.documents[doc_id] = doc
        return doc

    def import_document_list(self, df: pd.DataFrame):
        """Import document list from DataFrame."""

        for _, row in df.iterrows():
            doc_type = DocumentType(row.get('type', 'architectural').lower())
            req_date = pd.to_datetime(row.get('required_date', self.handover_date)).date() if 'required_date' in row else None

            self.add_document(
                document_number=str(row['document_number']),
                title=row['title'],
                doc_type=doc_type,
                discipline=row.get('discipline', ''),
                contractor=row.get('contractor', ''),
                required_date=req_date
            )

    def record_submission(self,
                          document_id: str,
                          revision: str,
                          submitted_by: str,
                          file_path: str = "") -> Optional[DocumentSubmission]:
        """Record document submission."""

        if document_id not in self.documents:
            return None

        doc = self.documents[document_id]

        submission = DocumentSubmission(
            submission_id=f"SUB-{len(self.submissions)+1:04d}",
            document_id=document_id,
            revision=revision,
            submission_date=date.today(),
            submitted_by=submitted_by,
            file_path=file_path,
            status=DocumentStatus.SUBMITTED
        )

        self.submissions.append(submission)

        # Update document
        doc.status = DocumentStatus.SUBMITTED
        doc.current_revision = revision
        doc.submitted_date = date.today()

        return submission

    def review_submission(self,
                          document_id: str,
                          approved: bool,
                          reviewer: str,
                          comments: str = ""):
        """Review submitted document."""

        if document_id not in self.documents:
            return

        doc = self.documents[document_id]

        if approved:
            doc.status = DocumentStatus.APPROVED
            doc.approved_date = date.today()
        else:
            doc.status = DocumentStatus.REJECTED

        doc.reviewer = reviewer
        doc.comments = comments

        # Update latest submission
        for sub in reversed(self.submissions):
            if sub.document_id == document_id:
                sub.status = DocumentStatus.APPROVED if approved else DocumentStatus.REJECTED
                sub.review_comments = comments
                break

    def get_summary(self) -> Dict[str, Any]:
        """Get documentation status summary."""

        docs = list(self.documents.values())
        today = date.today()

        # Status counts
        status_counts = {}
        for status in DocumentStatus:
            status_counts[status.value] = sum(1 for d in docs if d.status == status)

        # By type
        by_type = {}
        for doc_type in DocumentType:
            pending = sum(1 for d in docs if d.doc_type == doc_type and d.status != DocumentStatus.APPROVED)
            if pending > 0:
                by_type[doc_type.value] = pending

        # Overdue
        overdue = sum(
            1 for d in docs
            if d.required_date < today and d.status != DocumentStatus.APPROVED
        )

        # Completion rate
        approved = sum(1 for d in docs if d.status == DocumentStatus.APPROVED)
        completion = (approved / len(docs) * 100) if docs else 0

        return {
            'total_documents': len(docs),
            'approved': approved,
            'completion_rate': round(completion, 1),
            'by_status': status_counts,
            'by_type': by_type,
            'overdue': overdue,
            'days_to_handover': (self.handover_date - today).days
        }

    def get_contractor_status(self, contractor: str) -> Dict[str, Any]:
        """Get status for specific contractor."""

        docs = [d for d in self.documents.values() if d.contractor == contractor]

        approved = sum(1 for d in docs if d.status == DocumentStatus.APPROVED)
        pending = len(docs) - approved

        return {
            'contractor': contractor,
            'total': len(docs),
            'approved': approved,
            'pending': pending,
            'completion_rate': round(approved / len(docs) * 100, 1) if docs else 0
        }

    def get_overdue_documents(self) -> List[Dict[str, Any]]:
        """Get overdue documents."""

        today = date.today()
        overdue = []

        for doc in self.documents.values():
            if doc.required_date < today and doc.status != DocumentStatus.APPROVED:
                overdue.append({
                    'document_id': doc.document_id,
                    'document_number': doc.document_number,
                    'title': doc.title,
                    'contractor': doc.contractor,
                    'required_date': doc.required_date,
                    'days_overdue': (today - doc.required_date).days,
                    'status': doc.status.value
                })

        return sorted(overdue, key=lambda x: x['days_overdue'], reverse=True)

    def forecast_completion(self) -> Dict[str, Any]:
        """Forecast documentation completion."""

        summary = self.get_summary()
        pending = summary['total_documents'] - summary['approved']

        # Calculate submission rate
        recent_approvals = sum(
            1 for d in self.documents.values()
            if d.approved_date and d.approved_date >= date.today() - timedelta(days=14)
        )
        weekly_rate = recent_approvals / 2 if recent_approvals > 0 else 1

        weeks_needed = pending / weekly_rate if weekly_rate > 0 else pending
        projected_completion = date.today() + timedelta(weeks=weeks_needed)

        return {
            'pending_documents': pending,
            'approval_rate_per_week': round(weekly_rate, 1),
            'weeks_needed': round(weeks_needed, 1),
            'projected_completion': projected_completion,
            'handover_date': self.handover_date,
            'on_track': projected_completion <= self.handover_date
        }

    def generate_transmittal(self,
                              document_ids: List[str],
                              to: str,
                              subject: str) -> Dict[str, Any]:
        """Generate transmittal for documents."""

        docs = [self.documents[d] for d in document_ids if d in self.documents]

        return {
            'transmittal_number': f"TR-{date.today().strftime('%Y%m%d')}-001",
            'date': date.today(),
            'from': self.project_name,
            'to': to,
            'subject': subject,
            'documents': [
                {
                    'number': d.document_number,
                    'title': d.title,
                    'revision': d.current_revision
                }
                for d in docs
            ],
            'document_count': len(docs)
        }

    def export_to_excel(self, output_path: str) -> str:
        """Export tracking to Excel."""

        summary = self.get_summary()

        with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
            # Summary
            summary_df = pd.DataFrame([{
                'Project': self.project_name,
                'Handover Date': self.handover_date,
                'Total Documents': summary['total_documents'],
                'Approved': summary['approved'],
                'Completion %': summary['completion_rate'],
                'Overdue': summary['overdue'],
                'Days to Handover': summary['days_to_handover']
            }])
            summary_df.to_excel(writer, sheet_name='Summary', index=False)

            # All Documents
            docs_df = pd.DataFrame([
                {
                    'ID': d.document_id,
                    'Number': d.document_number,
                    'Title': d.title,
                    'Type': d.doc_type.value,
                    'Discipline': d.discipline,
                    'Contractor': d.contractor,
                    'Status': d.status.value,
                    'Revision': d.current_revision,
                    'Required': d.required_date,
                    'Submitted': d.submitted_date,
                    'Approved': d.approved_date
                }
                for d in self.documents.values()
            ])
            docs_df.to_excel(writer, sheet_name='Documents', index=False)

            # Overdue
            overdue = self.get_overdue_documents()
            if overdue:
                overdue_df = pd.DataFrame(overdue)
                overdue_df.to_excel(writer, sheet_name='Overdue', index=False)

            # By Contractor
            contractors = set(d.contractor for d in self.documents.values())
            contractor_data = [self.get_contractor_status(c) for c in contractors]
            if contractor_data:
                contractor_df = pd.DataFrame(contractor_data)
                contractor_df.to_excel(writer, sheet_name='By Contractor', index=False)

        return output_path

Read the full file on GitHub · 431 lines

Files

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.

Changes

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.

  1. 11d ago First seen · 431 lines · 27 tokens per session scan A 7566a219b1ac

Subscribe to this mod's changes

as-built-tracker 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 27 tokens to every session and 2,909 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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