schedule-delay-analyzer

schedule-delay-analyzer is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 21 tokens per session (1,602 once invoked), scanned A, original, MIT.

A schedule delay analysis tool records delays, their causes, their timing, and their effect on project activities.

In plain words
What is it for?
Use it to analyze weather, design, owner, material, labor, permit, and contractor delays, including their schedule and cost impacts.
Why use it?
It helps separate different types of delay and calculate how much they affect completion, including delays that happen at the same time.

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 analyze weather, design, owner, material, labor, permit, and contractor delays, including their schedule and cost impacts.

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

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schedule-delay-analyzer"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schedule-delay-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,602 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 $0.00021 $0.01602
Opus 5 $0.00010 $0.00801
Sonnet 5 $0.00004 $0.00320
Haiku 4.5 $0.00002 $0.00160

Measured 9d ago against content hash be18d02580eb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

schedule-delay-analyzer 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 9d 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/Schedule-Management/schedule-delay-analyzer/SKILL.md · 197 lines

How it starts

The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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)

Read the full file on GitHub · 197 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. 9d ago First seen · 197 lines · 21 tokens per session scan A be18d02580eb

Subscribe to this mod's changes

schedule-delay-analyzer is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 21d ago), licensed MIT. It adds 21 tokens to every session and 1,602 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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