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.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill schedule-delay-analyzergit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote 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.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/schedule-delay-analyzer)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.01602 |
| Opus 5.5 | $0.00008 | $0.00641 |
| Sonnet 5.5 | $0.00004 | $0.00320 |
| Haiku 4.5 | $0.00002 | $0.00160 |
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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- schedule-delay-analyzer — 100% identical, 0 lines differ
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)
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.
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.
- 1mo ago First seen · 197 lines · 21 tokens per session scan A be18d02580eb
schedule-delay-analyzer is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (374 stars, last pushed 8d ago), licensed MIT. It adds 21 tokens to every session and 1,602 once invoked, about $0.0001 per session on Opus 5.5. 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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