本文へ移動
cccskills
無料GitHub で公開

submittal-tracker

Track construction submittals through the review process. Manage approvals, revisions, and compliance.

インストール方法を見る

含まれるファイル(3)

  • SKILL.md4.9 KB
  • claw.json464 B
  • instructions.md1.1 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Submittal Tracker

Business Case

Submittals require careful tracking to avoid delays. This skill manages the entire submittal workflow from creation to approval.

Technical Implementation

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


class SubmittalStatus(Enum):
    DRAFT = "draft"
    SUBMITTED = "submitted"
    UNDER_REVIEW = "under_review"
    APPROVED = "approved"
    APPROVED_AS_NOTED = "approved_as_noted"
    REVISE_RESUBMIT = "revise_resubmit"
    REJECTED = "rejected"


class SubmittalType(Enum):
    PRODUCT_DATA = "product_data"
    SHOP_DRAWING = "shop_drawing"
    SAMPLE = "sample"
    MOCK_UP = "mock_up"
    CERTIFICATE = "certificate"
    TEST_REPORT = "test_report"


@dataclass
class ReviewComment:
    reviewer: str
    comment: str
    comment_date: date
    resolved: bool = False


@dataclass
class Submittal:
    submittal_id: str
    spec_section: str
    title: str
    submittal_type: SubmittalType
    status: SubmittalStatus
    contractor: str
    revision: int
    submitted_date: Optional[date]
    required_date: date
    approved_date: Optional[date] = None
    comments: List[ReviewComment] = field(default_factory=list)
    files: List[str] = field(default_factory=list)


class SubmittalTracker:
    def __init__(self, project_name: str):
        self.project_name = project_name
        self.submittals: Dict[str, Submittal] = {}
        self._counter = 0

    def create_submittal(self, spec_section: str, title: str,
                        submittal_type: SubmittalType, contractor: str,
                        required_date: date) -> Submittal:
        self._counter += 1
        sub_id = f"SUB-{self._counter:04d}"

        submittal = Submittal(
            submittal_id=sub_id,
            spec_section=spec_section,
            title=title,
            submittal_type=submittal_type,
            status=SubmittalStatus.DRAFT,
            contractor=contractor,
            revision=0,
            submitted_date=None,
            required_date=required_date
        )
        self.submittals[sub_id] = submittal
        return submittal

    def submit(self, sub_id: str, files: List[str]):
        if sub_id in self.submittals:
            self.submittals[sub_id].status = SubmittalStatus.SUBMITTED
            self.submittals[sub_id].submitted_date = date.today()
            self.submittals[sub_id].files = files

    def review(self, sub_id: str, status: SubmittalStatus, reviewer: str, comment: str = ""):
        if sub_id in self.submittals:
            sub = self.submittals[sub_id]
            sub.status = status
            if comment:
                sub.comments.append(ReviewComment(reviewer, comment, date.today()))
            if status in [SubmittalStatus.APPROVED, SubmittalStatus.APPROVED_AS_NOTED]:
                sub.approved_date = date.today()

    def resubmit(self, sub_id: str, files: List[str]):
        if sub_id in self.submittals:
            sub = self.submittals[sub_id]
            sub.revision += 1
            sub.status = SubmittalStatus.SUBMITTED
            sub.submitted_date = date.today()
            sub.files = files

    def get_pending(self) -> List[Submittal]:
        return [s for s in self.submittals.values()
                if s.status in [SubmittalStatus.SUBMITTED, SubmittalStatus.UNDER_REVIEW]]

    def get_overdue(self) -> List[Submittal]:
        today = date.today()
        return [s for s in self.submittals.values()
                if s.status not in [SubmittalStatus.APPROVED, SubmittalStatus.APPROVED_AS_NOTED]
                and s.required_date < today]

    def export_log(self, output_path: str):
        data = [{
            'ID': s.submittal_id,
            'Spec': s.spec_section,
            'Title': s.title,
            'Type': s.submittal_type.value,
            'Status': s.status.value,
            'Rev': s.revision,
            'Submitted': s.submitted_date,
            'Required': s.required_date,
            'Approved': s.approved_date
        } for s in self.submittals.values()]
        pd.DataFrame(data).to_excel(output_path, index=False)

Quick Start

tracker = SubmittalTracker("Office Tower")

sub = tracker.create_submittal("09 29 00", "Gypsum Board",
                               SubmittalType.PRODUCT_DATA, "ABC Drywall",
                               date.today() + timedelta(days=14))
tracker.submit(sub.submittal_id, ["spec_sheet.pdf"])
tracker.review(sub.submittal_id, SubmittalStatus.APPROVED, "Architect")

Resources

  • DDC Book: Chapter 4 - Document Control

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Create 4D construction simulations by linking BIM elements to project schedules. Generate time-based visualizations, sequence analysis, and construction phasing with Gantt integration.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3492026年8月22日 更新

Analyze 5000+ IFC and Revit projects at scale for patterns, benchmarks, and insights. Big data analysis for construction.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3492026年8月22日 更新

Check construction AI systems against the EU AI Act (Regulation 2024/1689): classify risk of estimation, scheduling, CV and agent tools, document transparency, keep human oversight. Use when deploying or auditing AI in construction.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3492026年8月22日 更新

Orchestrate multiple AI agents for construction workflows: estimator, scheduler, document, QA and safety agents coordinated by a supervisor agent, with human checkpoints. Use when automating end-to-end project processes with agentic AI.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3492026年8月22日 更新

Create Apache Airflow DAGs for construction data pipelines. Orchestrate ETL, validation, and reporting workflows.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3492026年8月22日 更新

Automate as-built documentation and digital handover for construction. Compile project records, generate O&M manuals, create asset databases, and ensure complete project closeout.

日本語の概要は準備中です。原文の説明を表示しています。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3492026年8月22日 更新

datadrivenconstruction のスキルをすべて見る

このスキルの問題を報告する