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 change-order-analysisgit 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/change-order-analysis)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/change-order-analysis"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/change-order-analysis/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/change-order-analysis"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/change-order-analysis.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 | $0.00033 | $0.04694 |
| Opus 5 | $0.00016 | $0.02347 |
| Sonnet 5 | $0.00007 | $0.00939 |
| Haiku 4.5 | $0.00003 | $0.00469 |
Grade A, and why
change-order-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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- change-order-analysis — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 595 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Change Order Analysis
Overview
This skill implements machine learning-based change order analysis for construction projects. Predict change order costs, classify types, identify patterns in historical data, and streamline approval processes.
Capabilities:
- Change order classification
- Cost impact prediction
- Schedule impact analysis
- Pattern identification
- Root cause analysis
- Approval workflow optimization
Quick Start
from dataclasses import dataclass, field
from datetime import date, datetime
from typing import List, Dict, Optional
from enum import Enum
class ChangeOrderType(Enum):
DESIGN_CHANGE = "design_change"
OWNER_REQUEST = "owner_request"
FIELD_CONDITION = "field_condition"
CODE_COMPLIANCE = "code_compliance"
VALUE_ENGINEERING = "value_engineering"
ERROR_OMISSION = "error_omission"
SCOPE_CHANGE = "scope_change"
class ChangeOrderStatus(Enum):
DRAFT = "draft"
SUBMITTED = "submitted"
UNDER_REVIEW = "under_review"
APPROVED = "approved"
REJECTED = "rejected"
IMPLEMENTED = "implemented"
@dataclass
class ChangeOrder:
co_number: str
title: str
description: str
co_type: ChangeOrderType
status: ChangeOrderStatus
submitted_date: date
requested_by: str
cost_impact: float
schedule_impact_days: int
affected_elements: List[str] = field(default_factory=list)
def classify_change_order(description: str) -> ChangeOrderType:
"""Simple rule-based classification"""
description_lower = description.lower()
if any(word in description_lower for word in ['design', 'drawing', 'specification']):
return ChangeOrderType.DESIGN_CHANGE
elif any(word in description_lower for word in ['owner', 'client', 'request']):
return ChangeOrderType.OWNER_REQUEST
elif any(word in description_lower for word in ['site', 'field', 'condition', 'unforeseen']):
return ChangeOrderType.FIELD_CONDITION
elif any(word in description_lower for word in ['code', 'regulation', 'compliance']):
return ChangeOrderType.CODE_COMPLIANCE
elif any(word in description_lower for word in ['value', 'alternative', 'savings']):
return ChangeOrderType.VALUE_ENGINEERING
elif any(word in description_lower for word in ['error', 'omission', 'mistake']):
return ChangeOrderType.ERROR_OMISSION
else:
return ChangeOrderType.SCOPE_CHANGE
# Example
co = ChangeOrder(
co_number="CO-001",
title="Additional structural reinforcement",
description="Site conditions revealed weaker soil requiring additional foundation reinforcement",
co_type=classify_change_order("Site conditions revealed weaker soil"),
status=ChangeOrderStatus.SUBMITTED,
submitted_date=date.today(),
requested_by="Site Engineer",
cost_impact=50000,
schedule_impact_days=5
)
print(f"CO Type: {co.co_type.value}")
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.
- 9d ago First seen · 595 lines · 33 tokens per session scan A fd7d2cf56104
change-order-analysis is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (312 stars, last pushed 21d ago), licensed MIT. It adds 33 tokens to every session and 4,694 once invoked, about $0.0002 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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