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 cwicr-report-generatorgit 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/cwicr-report-generator)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-report-generator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-report-generator/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/cwicr-report-generator"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/cwicr-report-generator.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 | $0.00029 | $0.02541 |
| Opus 5 | $0.00015 | $0.01270 |
| Sonnet 5 | $0.00006 | $0.00508 |
| Haiku 4.5 | $0.00003 | $0.00254 |
Grade A, and why
cwicr-report-generator 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 13d 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:
- cwicr-report-generator — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 315 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CWICR Report Generator
Overview
Generate professional cost reports from CWICR calculations - executive summaries, detailed breakdowns, charts, and export to multiple formats.
Python Implementation
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import datetime
from pathlib import Path
import json
@dataclass
class ReportSection:
"""Report section content."""
title: str
content: str
chart_type: Optional[str] = None
chart_data: Optional[Dict] = None
@dataclass
class CostReport:
"""Complete cost report."""
project_name: str
generated_date: datetime
total_cost: float
currency: str
sections: List[ReportSection]
line_items: List[Dict]
summary: Dict[str, Any]
class CWICRReportGenerator:
"""Generate cost estimation reports."""
def __init__(self, project_name: str = "Project",
currency: str = "USD"):
self.project_name = project_name
self.currency = currency
self.sections: List[ReportSection] = []
self.line_items: List[Dict] = []
def add_summary(self, summary_data: Dict[str, float]):
"""Add executive summary section."""
content = f"""
<div class="summary-box">
<h3>Cost Summary</h3>
<table class="summary-table">
<tr><td>Labor</td><td class="amount">${summary_data.get('labor', 0):,.2f}</td></tr>
<tr><td>Materials</td><td class="amount">${summary_data.get('material', 0):,.2f}</td></tr>
<tr><td>Equipment</td><td class="amount">${summary_data.get('equipment', 0):,.2f}</td></tr>
<tr><td>Overhead</td><td class="amount">${summary_data.get('overhead', 0):,.2f}</td></tr>
<tr><td>Profit</td><td class="amount">${summary_data.get('profit', 0):,.2f}</td></tr>
<tr class="total"><td>TOTAL</td><td class="amount">${summary_data.get('total', 0):,.2f}</td></tr>
</table>
</div>
"""
self.sections.append(ReportSection(
title="Executive Summary",
content=content,
chart_type="pie",
chart_data={
'labels': ['Labor', 'Materials', 'Equipment', 'Overhead', 'Profit'],
'values': [
summary_data.get('labor', 0),
summary_data.get('material', 0),
summary_data.get('equipment', 0),
summary_data.get('overhead', 0),
summary_data.get('profit', 0)
]
}
))
def add_breakdown_by_category(self, breakdown: Dict[str, float]):
"""Add breakdown by category section."""
rows = ""
for category, cost in sorted(breakdown.items(), key=lambda x: -x[1]):
rows += f"<tr><td>{category}</td><td class='amount'>${cost:,.2f}</td></tr>"
content = f"""
<table class="detail-table">
<thead><tr><th>Category</th><th>Cost</th></tr></thead>
<tbody>{rows}</tbody>
</table>
"""
self.sections.append(ReportSection(
title="Cost by Category",
content=content,
chart_type="bar",
chart_data={
'labels': list(breakdown.keys()),
'values': list(breakdown.values())
}
))
def add_line_items(self, items: List[Dict]):
"""Add detailed line items."""
self.line_items = items
rows = ""
for item in items[:50]: # Limit for report
rows += f"""
<tr>
<td>{item.get('code', '')}</td>
<td>{item.get('description', '')[:50]}</td>
<td>{item.get('quantity', 0):,.2f}</td>
<td>{item.get('unit', '')}</td>
<td class="amount">${item.get('unit_price', 0):,.2f}</td>
<td class="amount">${item.get('total', 0):,.2f}</td>
</tr>
"""
content = f"""
<table class="line-items">
<thead>
<tr>
<th>Code</th>
<th>Description</th>
<th>Qty</th>
<th>Unit</th>
<th>Unit Price</th>
<th>Total</th>
</tr>
</thead>
<tbody>{rows}</tbody>
</table>
"""
self.sections.append(ReportSection(
title="Line Items",
content=content
))
def generate_html(self) -> str:
"""Generate HTML report."""
sections_html = ""
for section in self.sections:
sections_html += f"""
<section class="report-section">
<h2>{section.title}</h2>
{section.content}
</section>
"""
html = f"""
<!DOCTYPE html>
<html>
<head>
<title>Cost Report - {self.project_name}</title>
<style>
body {{ font-family: 'Segoe UI', Arial, sans-serif; margin: 40px; background: #f5f5f5; }}
.report-container {{ max-width: 1200px; margin: 0 auto; background: white; padding: 40px; box-shadow: 0 2px 10px rgba(0,0,0,0.1); }}
h1 {{ color: #2c3e50; border-bottom: 3px solid #3498db; padding-bottom: 10px; }}
h2 {{ color: #34495e; margin-top: 30px; }}
.summary-box {{ background: #ecf0f1; padding: 20px; border-radius: 8px; }}
table {{ width: 100%; border-collapse: collapse; margin: 20px 0; }}
th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #ddd; }}
th {{ background: #3498db; color: white; }}
.amount {{ text-align: right; font-family: monospace; }}
.total {{ font-weight: bold; background: #f8f9fa; }}
.line-items td {{ font-size: 0.9em; }}
.meta {{ color: #7f8c8d; font-size: 0.9em; margin-bottom: 20px; }}
</style>
</head>
<body>
<div class="report-container">
<h1>Cost Estimation Report</h1>
<div class="meta">
<p>Project: {self.project_name}</p>
<p>Generated: {datetime.now().strftime('%Y-%m-%d %H:%M')}</p>
<p>Currency: {self.currency}</p>
</div>
{sections_html}
<footer style="margin-top: 40px; color: #95a5a6; text-align: center;">
Generated by DDC CWICR | DataDrivenConstruction.io
</footer>
</div>
</body>
</html>
"""
return html
def save_html(self, output_path: str) -> str:
"""Save HTML report to file."""
html = self.generate_html()
with open(output_path, 'w', encoding='utf-8') as f:
f.write(html)
return output_path
def generate_excel(self, output_path: str) -> str:
"""Generate Excel report."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary sheet
if self.sections:
summary_data = []
for section in self.sections:
if section.chart_data:
for i, label in enumerate(section.chart_data.get('labels', [])):
summary_data.append({
'Category': label,
'Amount': section.chart_data.get('values', [])[i]
})
if summary_data:
pd.DataFrame(summary_data).to_excel(
writer, sheet_name='Summary', index=False)
# Line items sheet
if self.line_items:
pd.DataFrame(self.line_items).to_excel(
writer, sheet_name='Line Items', index=False)
return output_path
def generate_json(self) -> str:
"""Generate JSON report."""
report = {
'project_name': self.project_name,
'generated_date': datetime.now().isoformat(),
'currency': self.currency,
'sections': [
{
'title': s.title,
'chart_data': s.chart_data
} for s in self.sections
],
'line_items': self.line_items
}
return json.dumps(report, indent=2)
class QuickReport:
"""Quick report generation from cost data."""
@staticmethod
def from_dataframe(df: pd.DataFrame,
project_name: str = "Project") -> CWICRReportGenerator:
"""Generate report from cost DataFrame."""
gen = CWICRReportGenerator(project_name)
# Calculate summary
summary = {
'labor': df['labor_cost'].sum() if 'labor_cost' in df.columns else 0,
'material': df['material_cost'].sum() if 'material_cost' in df.columns else 0,
'equipment': df['equipment_cost'].sum() if 'equipment_cost' in df.columns else 0,
'overhead': df['overhead_cost'].sum() if 'overhead_cost' in df.columns else 0,
'profit': df['profit_cost'].sum() if 'profit_cost' in df.columns else 0,
'total': df['total_cost'].sum() if 'total_cost' in df.columns else 0
}
gen.add_summary(summary)
# Category breakdown
if 'category' in df.columns and 'total_cost' in df.columns:
breakdown = df.groupby('category')['total_cost'].sum().to_dict()
gen.add_breakdown_by_category(breakdown)
# Line items
items = df.to_dict('records')
gen.add_line_items(items)
return gen
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.
- 13d ago First seen · 315 lines · 29 tokens per session scan A 3cdf9db5a362
cwicr-report-generator 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 29 tokens to every session and 2,541 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 2026-08-30.
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