跳到正文
cccskills
免费在 GitHub 公开

schedule-delay-analyzer

Analyze schedule delays, identify causes, and calculate time impacts using delay analysis methods.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(3)

  • SKILL.md6.7 KB
  • claw.json443 B
  • instructions.md1.1 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Schedule Delay Analyzer

Technical Implementation

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


class DelayType(Enum):
    EXCUSABLE_COMPENSABLE = "excusable_compensable"
    EXCUSABLE_NON_COMPENSABLE = "excusable_non_compensable"
    NON_EXCUSABLE = "non_excusable"
    CONCURRENT = "concurrent"


class DelayCause(Enum):
    OWNER_CHANGE = "owner_change"
    DESIGN_ERROR = "design_error"
    WEATHER = "weather"
    DIFFERING_CONDITIONS = "differing_conditions"
    CONTRACTOR_ISSUE = "contractor_issue"
    MATERIAL_DELAY = "material_delay"
    LABOR_SHORTAGE = "labor_shortage"
    PERMIT_DELAY = "permit_delay"
    OTHER = "other"


@dataclass
class DelayEvent:
    delay_id: str
    activity_id: str
    activity_name: str
    delay_type: DelayType
    cause: DelayCause
    start_date: date
    end_date: date
    delay_days: int
    on_critical_path: bool
    description: str
    documentation: List[str] = field(default_factory=list)
    cost_impact: float = 0.0


@dataclass
class ScheduleBaseline:
    baseline_date: date
    planned_completion: date
    activities: Dict[str, Dict[str, date]]  # activity_id: {start, end}


class ScheduleDelayAnalyzer:
    def __init__(self, project_name: str, contract_completion: date):
        self.project_name = project_name
        self.contract_completion = contract_completion
        self.baselines: List[ScheduleBaseline] = []
        self.delays: Dict[str, DelayEvent] = {}
        self._counter = 0

    def add_baseline(self, baseline_date: date, planned_completion: date,
                    activities: Dict[str, Dict[str, date]]):
        baseline = ScheduleBaseline(baseline_date, planned_completion, activities)
        self.baselines.append(baseline)

    def record_delay(self, activity_id: str, activity_name: str,
                    delay_type: DelayType, cause: DelayCause,
                    start_date: date, end_date: date,
                    on_critical_path: bool, description: str,
                    cost_impact: float = 0) -> DelayEvent:
        self._counter += 1
        delay_id = f"DLY-{self._counter:04d}"

        delay = DelayEvent(
            delay_id=delay_id,
            activity_id=activity_id,
            activity_name=activity_name,
            delay_type=delay_type,
            cause=cause,
            start_date=start_date,
            end_date=end_date,
            delay_days=(end_date - start_date).days,
            on_critical_path=on_critical_path,
            description=description,
            cost_impact=cost_impact
        )
        self.delays[delay_id] = delay
        return delay

    def calculate_project_delay(self) -> int:
        """Calculate total critical path delay."""
        critical_delays = [d for d in self.delays.values() if d.on_critical_path]
        return sum(d.delay_days for d in critical_delays)

    def analyze_by_type(self) -> Dict[str, Dict[str, Any]]:
        analysis = {}
        for delay in self.delays.values():
            dtype = delay.delay_type.value
            if dtype not in analysis:
                analysis[dtype] = {'count': 0, 'days': 0, 'cost': 0}
            analysis[dtype]['count'] += 1
            analysis[dtype]['days'] += delay.delay_days
            analysis[dtype]['cost'] += delay.cost_impact
        return analysis

    def analyze_by_cause(self) -> Dict[str, int]:
        by_cause = {}
        for delay in self.delays.values():
            cause = delay.cause.value
            by_cause[cause] = by_cause.get(cause, 0) + delay.delay_days
        return by_cause

    def calculate_time_extension_claim(self) -> Dict[str, Any]:
        """Calculate basis for time extension claim."""
        excusable = [d for d in self.delays.values()
                    if d.delay_type in [DelayType.EXCUSABLE_COMPENSABLE,
                                        DelayType.EXCUSABLE_NON_COMPENSABLE]
                    and d.on_critical_path]

        compensable = [d for d in excusable
                      if d.delay_type == DelayType.EXCUSABLE_COMPENSABLE]

        return {
            'excusable_delays': len(excusable),
            'excusable_days': sum(d.delay_days for d in excusable),
            'compensable_delays': len(compensable),
            'compensable_days': sum(d.delay_days for d in compensable),
            'total_cost_impact': sum(d.cost_impact for d in compensable),
            'recommended_extension': sum(d.delay_days for d in excusable)
        }

    def get_summary(self) -> Dict[str, Any]:
        critical_delay = self.calculate_project_delay()
        projected_completion = self.contract_completion + timedelta(days=critical_delay)

        return {
            'project': self.project_name,
            'contract_completion': self.contract_completion,
            'projected_completion': projected_completion,
            'total_delays': len(self.delays),
            'critical_path_delays': sum(1 for d in self.delays.values() if d.on_critical_path),
            'total_delay_days': critical_delay,
            'by_type': self.analyze_by_type(),
            'by_cause': self.analyze_by_cause()
        }

    def export_analysis(self, output_path: str):
        data = [{
            'ID': d.delay_id,
            'Activity': d.activity_name,
            'Type': d.delay_type.value,
            'Cause': d.cause.value,
            'Start': d.start_date,
            'End': d.end_date,
            'Days': d.delay_days,
            'Critical': d.on_critical_path,
            'Cost Impact': d.cost_impact,
            'Description': d.description
        } for d in self.delays.values()]
        pd.DataFrame(data).to_excel(output_path, index=False)

Quick Start

analyzer = ScheduleDelayAnalyzer("Office Tower", date(2024, 12, 31))

delay = analyzer.record_delay(
    activity_id="A-300",
    activity_name="Foundation Work",
    delay_type=DelayType.EXCUSABLE_COMPENSABLE,
    cause=DelayCause.OWNER_CHANGE,
    start_date=date(2024, 3, 1),
    end_date=date(2024, 3, 15),
    on_critical_path=True,
    description="Owner requested additional scope",
    cost_impact=50000
)

summary = analyzer.get_summary()
print(f"Project delayed by {summary['total_delay_days']} days")

claim = analyzer.calculate_time_extension_claim()
print(f"Recommended extension: {claim['recommended_extension']} days")

Resources

  • DDC Book: Chapter 3.3 - Schedule Management

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

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

中文简介正在准备中。当前显示原文说明。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3592026年10月20日 更新

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

中文简介正在准备中。当前显示原文说明。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3592026年10月20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.

中文简介正在准备中。当前显示原文说明。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3592026年10月20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.

中文简介正在准备中。当前显示原文说明。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3592026年10月20日 更新

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

中文简介正在准备中。当前显示原文说明。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3592026年10月20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.

中文简介正在准备中。当前显示原文说明。

datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction3592026年10月20日 更新

查看 datadrivenconstruction 的全部技能

报告此技能的问题