delay-analysis

delay-analysis is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 29 tokens per session (4,175 once invoked), scanned A, original, MIT.

A construction schedule-delay analysis workflow for measuring how events affected planned completion dates and project costs.

In plain words
What is it for?
Use it to perform time impact analysis, compare planned and actual schedules, calculate delay damages, and prepare dispute documentation.
Why use it?
It helps separate delay causes, including overlapping delays, and provides an evidence-based record for recovery efforts or disputes.

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 perform time impact analysis, compare planned and actual schedules, calculate delay damages, and prepare dispute documentation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/delay-analysis
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 delay-analysis
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

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agentmods badge for delay-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/delay-analysis/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/delay-analysis)
Your own site
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/delay-analysis"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/delay-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

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agentmods 80×15 button for delay-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/delay-analysis"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/delay-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 29 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,175 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 $0.00029 $0.04175
Opus 5 $0.00015 $0.02087
Sonnet 5 $0.00006 $0.00835
Haiku 4.5 $0.00003 $0.00417

Measured 7d ago against content hash ea3dcdae90be, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

delay-analysis 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 7d 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:

3_DDC_Insights/Schedule-Optimization/delay-analysis/SKILL.md · 537 lines

How it starts

The opening of the file, as written. The whole thing — 537 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Delay Analysis

Overview

Analyze construction schedule delays for project recovery and claims. Perform time impact analysis (TIA), identify concurrent delays, calculate delay damages, and prepare documentation for dispute resolution.

"Proper delay analysis is essential for fair resolution of construction disputes" — DDC Community

Delay Analysis Methods

┌─────────────────────────────────────────────────────────────────┐
│                    DELAY ANALYSIS METHODS                        │
├─────────────────────────────────────────────────────────────────┤
│                                                                  │
│  As-Planned vs As-Built    │    Time Impact Analysis (TIA)      │
│  ─────────────────────     │    ────────────────────────────    │
│  Compare original to       │    Insert delay events into        │
│  actual schedule           │    schedule to measure impact      │
│                            │                                     │
│  Windows Analysis          │    Collapsed As-Built              │
│  ────────────────          │    ─────────────────               │
│  Divide project into       │    Remove delays from as-built     │
│  time periods              │    to find "but-for" completion    │
│                                                                  │
└─────────────────────────────────────────────────────────────────┘

Technical Implementation

from dataclasses import dataclass, field
from typing import List, Dict, Optional, Tuple
from datetime import datetime, timedelta
from enum import Enum
from collections import defaultdict

class DelayType(Enum):
    EXCUSABLE_COMPENSABLE = "excusable_compensable"      # Owner caused - time + money
    EXCUSABLE_NON_COMPENSABLE = "excusable_non_compensable"  # Neither party - time only
    NON_EXCUSABLE = "non_excusable"                      # Contractor caused - no relief
    CONCURRENT = "concurrent"                            # Both parties - complex

class DelayCause(Enum):
    OWNER_CHANGE = "owner_change"
    LATE_INFORMATION = "late_information"
    DIFFERING_CONDITIONS = "differing_conditions"
    PERMIT_DELAY = "permit_delay"
    WEATHER = "weather"
    LABOR_SHORTAGE = "labor_shortage"
    MATERIAL_DELAY = "material_delay"
    SUBCONTRACTOR = "subcontractor"
    COORDINATION = "coordination"
    ACCESS = "access"
    FORCE_MAJEURE = "force_majeure"

@dataclass
class DelayEvent:
    id: str
    description: str
    cause: DelayCause
    delay_type: DelayType
    start_date: datetime
    end_date: datetime
    affected_activities: List[str]
    responsible_party: str
    documented: bool = True
    supporting_docs: List[str] = field(default_factory=list)
    calculated_impact: int = 0  # days
    concurrent_with: List[str] = field(default_factory=list)

@dataclass
class ScheduleVersion:
    version_id: str
    version_type: str  # baseline, update, as-built
    data_date: datetime
    completion_date: datetime
    activities: Dict[str, Dict]  # activity_id -> {start, finish, duration}

@dataclass
class WindowPeriod:
    window_id: str
    start_date: datetime
    end_date: datetime
    planned_progress: float
    actual_progress: float
    delay_days: int
    delay_events: List[str]
    responsible_parties: Dict[str, int]  # party -> delay days

@dataclass
class DelayAnalysisReport:
    project_name: str
    analysis_date: datetime
    original_completion: datetime
    actual_completion: datetime
    total_delay: int
    excusable_delay: int
    non_excusable_delay: int
    concurrent_delay: int
    delay_events: List[DelayEvent]
    delay_by_cause: Dict[str, int]
    delay_by_party: Dict[str, int]
    recommended_extension: int
    potential_damages: float

class DelayAnalyzer:
    """Analyze construction schedule delays."""

    # Daily delay costs by project size
    DEFAULT_DAILY_COSTS = {
        "small": 5000,      # < $10M
        "medium": 15000,    # $10M - $50M
        "large": 40000,     # $50M - $200M
        "mega": 100000      # > $200M
    }

    def __init__(self, project_name: str, contract_completion: datetime):
        self.project_name = project_name
        self.contract_completion = contract_completion
        self.delay_events: Dict[str, DelayEvent] = {}
        self.schedule_versions: Dict[str, ScheduleVersion] = {}
        self.window_periods: List[WindowPeriod] = []
        self.daily_cost = self.DEFAULT_DAILY_COSTS["medium"]

    def set_daily_delay_cost(self, cost: float):
        """Set daily delay cost for damages calculation."""
        self.daily_cost = cost

    def add_schedule_version(self, version_id: str, version_type: str,
                            data_date: datetime, completion_date: datetime,
                            activities: Dict[str, Dict]) -> ScheduleVersion:
        """Add schedule version for analysis."""
        version = ScheduleVersion(
            version_id=version_id,
            version_type=version_type,
            data_date=data_date,
            completion_date=completion_date,
            activities=activities
        )
        self.schedule_versions[version_id] = version
        return version

    def add_delay_event(self, id: str, description: str,
                       cause: DelayCause, delay_type: DelayType,
                       start_date: datetime, end_date: datetime,
                       affected_activities: List[str],
                       responsible_party: str,
                       supporting_docs: List[str] = None) -> DelayEvent:
        """Add delay event for analysis."""
        event = DelayEvent(
            id=id,
            description=description,
            cause=cause,
            delay_type=delay_type,
            start_date=start_date,
            end_date=end_date,
            affected_activities=affected_activities,
            responsible_party=responsible_party,
            supporting_docs=supporting_docs or []
        )
        self.delay_events[id] = event
        return event

    def perform_as_planned_vs_as_built(self) -> Dict:
        """Perform As-Planned vs As-Built analysis."""
        baseline = self.schedule_versions.get("baseline")
        as_built = self.schedule_versions.get("as_built")

        if not baseline or not as_built:
            raise ValueError("Need baseline and as-built schedules")

        total_delay = (as_built.completion_date - baseline.completion_date).days

        # Analyze each activity
        activity_delays = []
        for act_id, baseline_act in baseline.activities.items():
            if act_id in as_built.activities:
                as_built_act = as_built.activities[act_id]

                baseline_finish = baseline_act['finish']
                actual_finish = as_built_act['finish']

                if isinstance(baseline_finish, str):
                    baseline_finish = datetime.fromisoformat(baseline_finish)
                if isinstance(actual_finish, str):
                    actual_finish = datetime.fromisoformat(actual_finish)

                delay = (actual_finish - baseline_finish).days

                if delay > 0:
                    activity_delays.append({
                        'activity_id': act_id,
                        'planned_finish': baseline_finish,
                        'actual_finish': actual_finish,
                        'delay_days': delay
                    })

        return {
            'method': 'As-Planned vs As-Built',
            'baseline_completion': baseline.completion_date,
            'actual_completion': as_built.completion_date,
            'total_delay': total_delay,
            'activity_delays': sorted(activity_delays, key=lambda x: -x['delay_days'])
        }

    def perform_time_impact_analysis(self, delay_event_id: str) -> Dict:
        """Perform Time Impact Analysis for specific delay event."""
        if delay_event_id not in self.delay_events:
            raise ValueError(f"Delay event {delay_event_id} not found")

        event = self.delay_events[delay_event_id]

        # Find schedule version just before delay
        pre_delay_schedule = None
        for version in sorted(self.schedule_versions.values(),
                            key=lambda v: v.data_date, reverse=True):
            if version.data_date < event.start_date:
                pre_delay_schedule = version
                break

        if not pre_delay_schedule:
            pre_delay_schedule = self.schedule_versions.get("baseline")

        if not pre_delay_schedule:
            raise ValueError("No pre-delay schedule found")

        # Calculate impact
        original_completion = pre_delay_schedule.completion_date
        delay_duration = (event.end_date - event.start_date).days

        # Check if delay is on critical path
        critical_impact = False
        for act_id in event.affected_activities:
            if act_id in pre_delay_schedule.activities:
                act = pre_delay_schedule.activities[act_id]
                if act.get('is_critical', False):
                    critical_impact = True
                    break

        if critical_impact:
            impact_days = delay_duration
            new_completion = original_completion + timedelta(days=delay_duration)
        else:
            # Need to check float
            impact_days = max(0, delay_duration - 5)  # Simplified - assume 5 days float
            new_completion = original_completion + timedelta(days=impact_days)

        event.calculated_impact = impact_days

        return {
            'method': 'Time Impact Analysis',
            'delay_event': event.id,
            'delay_description': event.description,
            'delay_duration': delay_duration,
            'critical_path_impact': critical_impact,
            'schedule_impact_days': impact_days,
            'original_completion': original_completion,
            'impacted_completion': new_completion,
            'delay_type': event.delay_type.value,
            'responsible_party': event.responsible_party
        }

    def identify_concurrent_delays(self) -> List[Tuple[str, str, int]]:
        """Identify concurrent delay events."""
        concurrent = []

        events = list(self.delay_events.values())
        for i, event1 in enumerate(events):
            for event2 in events[i+1:]:
                # Check for overlap
                overlap_start = max(event1.start_date, event2.start_date)
                overlap_end = min(event1.end_date, event2.end_date)

                if overlap_start < overlap_end:
                    overlap_days = (overlap_end - overlap_start).days
                    concurrent.append((event1.id, event2.id, overlap_days))

                    event1.concurrent_with.append(event2.id)
                    event2.concurrent_with.append(event1.id)

        return concurrent

    def perform_windows_analysis(self, window_days: int = 30) -> List[WindowPeriod]:
        """Perform windows analysis by dividing project into periods."""
        baseline = self.schedule_versions.get("baseline")
        as_built = self.schedule_versions.get("as_built")

        if not baseline or not as_built:
            raise ValueError("Need baseline and as-built schedules")

        windows = []
        current_start = baseline.data_date
        window_num = 1

        while current_start < as_built.completion_date:
            window_end = min(
                current_start + timedelta(days=window_days),
                as_built.completion_date
            )

            # Find delay events in this window
            window_events = [
                e.id for e in self.delay_events.values()
                if e.start_date < window_end and e.end_date > current_start
            ]

            # Calculate delay by party
            party_delays = defaultdict(int)
            for event_id in window_events:
                event = self.delay_events[event_id]
                overlap_start = max(event.start_date, current_start)
                overlap_end = min(event.end_date, window_end)
                days = (overlap_end - overlap_start).days
                party_delays[event.responsible_party] += days

            window = WindowPeriod(
                window_id=f"W{window_num:02d}",
                start_date=current_start,
                end_date=window_end,
                planned_progress=0.0,  # Would calculate from schedule
                actual_progress=0.0,
                delay_days=sum(party_delays.values()),
                delay_events=window_events,
                responsible_parties=dict(party_delays)
            )
            windows.append(window)

            current_start = window_end
            window_num += 1

        self.window_periods = windows
        return windows

    def calculate_delay_damages(self) -> Dict:
        """Calculate potential delay damages."""
        # Summarize delays by type
        excusable_compensable = 0
        excusable_non_compensable = 0
        non_excusable = 0

        for event in self.delay_events.values():
            impact = event.calculated_impact or (event.end_date - event.start_date).days

            # Adjust for concurrency
            if event.concurrent_with:
                impact = impact // 2  # Simplified concurrency handling

            if event.delay_type == DelayType.EXCUSABLE_COMPENSABLE:
                excusable_compensable += impact
            elif event.delay_type == DelayType.EXCUSABLE_NON_COMPENSABLE:
                excusable_non_compensable += impact
            elif event.delay_type == DelayType.NON_EXCUSABLE:
                non_excusable += impact

        # Calculate damages
        contractor_damages = excusable_compensable * self.daily_cost
        owner_ld = non_excusable * self.daily_cost

        return {
            'excusable_compensable_days': excusable_compensable,
            'excusable_non_compensable_days': excusable_non_compensable,
            'non_excusable_days': non_excusable,
            'recommended_time_extension': excusable_compensable + excusable_non_compensable,
            'contractor_delay_damages': contractor_damages,
            'owner_liquidated_damages': owner_ld,
            'daily_rate_used': self.daily_cost
        }

    def generate_analysis_report(self, actual_completion: datetime) -> DelayAnalysisReport:
        """Generate comprehensive delay analysis report."""
        total_delay = (actual_completion - self.contract_completion).days

        # Categorize delays
        delay_by_cause = defaultdict(int)
        delay_by_party = defaultdict(int)
        excusable = 0
        non_excusable = 0
        concurrent = 0

        for event in self.delay_events.values():
            impact = event.calculated_impact or (event.end_date - event.start_date).days

            delay_by_cause[event.cause.value] += impact
            delay_by_party[event.responsible_party] += impact

            if event.concurrent_with:
                concurrent += impact // 2
            elif event.delay_type in [DelayType.EXCUSABLE_COMPENSABLE,
                                      DelayType.EXCUSABLE_NON_COMPENSABLE]:
                excusable += impact
            else:
                non_excusable += impact

        damages = self.calculate_delay_damages()

        return DelayAnalysisReport(
            project_name=self.project_name,
            analysis_date=datetime.now(),
            original_completion=self.contract_completion,
            actual_completion=actual_completion,
            total_delay=total_delay,
            excusable_delay=excusable,
            non_excusable_delay=non_excusable,
            concurrent_delay=concurrent,
            delay_events=list(self.delay_events.values()),
            delay_by_cause=dict(delay_by_cause),
            delay_by_party=dict(delay_by_party),
            recommended_extension=damages['recommended_time_extension'],
            potential_damages=damages['contractor_delay_damages']
        )

    def generate_report_markdown(self, report: DelayAnalysisReport) -> str:
        """Generate markdown report."""
        lines = [
            "# Delay Analysis Report",
            "",
            f"**Project:** {report.project_name}",
            f"**Analysis Date:** {report.analysis_date.strftime('%Y-%m-%d')}",
            "",
            "## Schedule Summary",
            "",
            f"| Milestone | Date |",
            f"|-----------|------|",
            f"| Contract Completion | {report.original_completion.strftime('%Y-%m-%d')} |",
            f"| Actual Completion | {report.actual_completion.strftime('%Y-%m-%d')} |",
            f"| **Total Delay** | **{report.total_delay} days** |",
            "",
            "## Delay Classification",
            "",
            f"| Category | Days |",
            f"|----------|------|",
            f"| Excusable Delay | {report.excusable_delay} |",
            f"| Non-Excusable Delay | {report.non_excusable_delay} |",
            f"| Concurrent Delay | {report.concurrent_delay} |",
            "",
            "## Delay by Cause",
            ""
        ]

        for cause, days in sorted(report.delay_by_cause.items(), key=lambda x: -x[1]):
            lines.append(f"- **{cause}**: {days} days")

        lines.extend([
            "",
            "## Delay by Responsible Party",
            ""
        ])

        for party, days in sorted(report.delay_by_party.items(), key=lambda x: -x[1]):
            lines.append(f"- **{party}**: {days} days")

        lines.extend([
            "",
            "## Recommendations",
            "",
            f"- **Recommended Time Extension:** {report.recommended_extension} days",
            f"- **Potential Delay Damages:** ${report.potential_damages:,.0f}",
            ""
        ])

        return "\n".join(lines)

Read the full file on GitHub · 537 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. 7d ago First seen · 537 lines · 29 tokens per session scan A ea3dcdae90be

Subscribe to this mod's changes

delay-analysis is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 19d ago), licensed MIT. It adds 29 tokens to every session and 4,175 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 2026-09-03.

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