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 jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill pdf-report-generatorgit clone --depth 1 https://github.com/jdmorag97-rgb/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/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/pdf-report-generator)<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/pdf-report-generator"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/pdf-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/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/pdf-report-generator"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/pdf-report-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.03714 |
| Opus 5 | $0.00010 | $0.01857 |
| Sonnet 5 | $0.00004 | $0.00743 |
| Haiku 4.5 | $0.00002 | $0.00371 |
Grade A, and why
pdf-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 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.
This is a copy
100% identical to pdf-report-generator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Report Generator
Business Case
Problem Statement
Report generation challenges:
- Manual report creation is time-consuming
- Inconsistent formatting
- Data aggregation from multiple sources
- Repetitive weekly/monthly reports
Solution
Automated PDF report generation from project data with templates, charts, and customizable sections.
Technical Implementation
import pandas as pd
from typing import Dict, Any, List, Optional
from dataclasses import dataclass, field
from datetime import date, datetime
from enum import Enum
from io import BytesIO
class ReportType(Enum):
PROGRESS = "progress"
COST = "cost"
SAFETY = "safety"
QUALITY = "quality"
EXECUTIVE = "executive"
WEEKLY = "weekly"
MONTHLY = "monthly"
class SectionType(Enum):
HEADER = "header"
TEXT = "text"
TABLE = "table"
CHART = "chart"
KPI_CARDS = "kpi_cards"
IMAGE = "image"
PAGE_BREAK = "page_break"
@dataclass
class ReportSection:
section_type: SectionType
title: str = ""
content: Any = None
style: Dict[str, Any] = field(default_factory=dict)
@dataclass
class KPICard:
name: str
value: Any
unit: str = ""
target: Any = None
status: str = "normal" # normal, warning, critical, good
@dataclass
class ChartConfig:
chart_type: str # bar, line, pie
data: Dict[str, List]
title: str = ""
x_label: str = ""
y_label: str = ""
class PDFReportGenerator:
"""Generate PDF reports from construction project data."""
def __init__(self, project_name: str, report_type: ReportType):
self.project_name = project_name
self.report_type = report_type
self.sections: List[ReportSection] = []
self.metadata: Dict[str, Any] = {
'author': '',
'date': date.today(),
'version': '1.0'
}
def set_metadata(self, author: str = "", report_date: date = None, version: str = "1.0"):
"""Set report metadata."""
self.metadata['author'] = author
self.metadata['date'] = report_date or date.today()
self.metadata['version'] = version
def add_header(self, title: str, subtitle: str = ""):
"""Add report header section."""
self.sections.append(ReportSection(
section_type=SectionType.HEADER,
title=title,
content={'subtitle': subtitle, 'date': self.metadata['date'].isoformat()}
))
def add_text(self, title: str, content: str):
"""Add text section."""
self.sections.append(ReportSection(
section_type=SectionType.TEXT,
title=title,
content=content
))
def add_table(self, title: str, df: pd.DataFrame, style: Dict[str, Any] = None):
"""Add table section from DataFrame."""
self.sections.append(ReportSection(
section_type=SectionType.TABLE,
title=title,
content=df.to_dict('records'),
style=style or {}
))
def add_kpi_cards(self, title: str, kpis: List[KPICard]):
"""Add KPI cards section."""
self.sections.append(ReportSection(
section_type=SectionType.KPI_CARDS,
title=title,
content=[{
'name': k.name,
'value': k.value,
'unit': k.unit,
'target': k.target,
'status': k.status
} for k in kpis]
))
def add_chart(self, title: str, chart_config: ChartConfig):
"""Add chart section."""
self.sections.append(ReportSection(
section_type=SectionType.CHART,
title=title,
content={
'type': chart_config.chart_type,
'data': chart_config.data,
'x_label': chart_config.x_label,
'y_label': chart_config.y_label
}
))
def add_page_break(self):
"""Add page break."""
self.sections.append(ReportSection(section_type=SectionType.PAGE_BREAK))
def generate_progress_report(self, data: Dict[str, Any]):
"""Generate standard progress report."""
self.add_header(
f"{self.project_name} - Progress Report",
f"Report Date: {self.metadata['date']}"
)
# KPIs
kpis = [
KPICard("Overall Progress", f"{data.get('overall_progress', 0)}%", target="100%",
status="good" if data.get('overall_progress', 0) >= data.get('planned_progress', 0) else "warning"),
KPICard("SPI", f"{data.get('spi', 1.0):.2f}", target="1.00",
status="good" if data.get('spi', 1) >= 0.95 else "critical"),
KPICard("CPI", f"{data.get('cpi', 1.0):.2f}", target="1.00",
status="good" if data.get('cpi', 1) >= 0.95 else "critical"),
KPICard("Days Remaining", str(data.get('days_remaining', 0)), "days")
]
self.add_kpi_cards("Key Performance Indicators", kpis)
# Progress summary
self.add_text("Executive Summary", data.get('summary', 'No summary provided.'))
# Activities table
if 'activities' in data:
activities_df = pd.DataFrame(data['activities'])
self.add_table("Activity Status", activities_df)
# Progress chart
if 'progress_history' in data:
self.add_chart("Progress Trend", ChartConfig(
chart_type="line",
data=data['progress_history'],
title="Progress Over Time",
x_label="Date",
y_label="Progress %"
))
# Issues
if 'issues' in data:
self.add_text("Current Issues", "\n".join(f"- {issue}" for issue in data['issues']))
def generate_cost_report(self, data: Dict[str, Any]):
"""Generate cost report."""
self.add_header(
f"{self.project_name} - Cost Report",
f"Period: {data.get('period', 'Current')}"
)
# Cost KPIs
budget = data.get('budget', 0)
actual = data.get('actual_cost', 0)
variance = budget - actual
kpis = [
KPICard("Budget", f"${budget:,.0f}"),
KPICard("Actual Cost", f"${actual:,.0f}"),
KPICard("Variance", f"${variance:,.0f}",
status="good" if variance >= 0 else "critical"),
KPICard("CPI", f"{data.get('cpi', 1.0):.2f}",
status="good" if data.get('cpi', 1) >= 0.95 else "warning")
]
self.add_kpi_cards("Cost Summary", kpis)
# Cost breakdown
if 'cost_breakdown' in data:
breakdown_df = pd.DataFrame(data['cost_breakdown'])
self.add_table("Cost Breakdown by Category", breakdown_df)
# Cost trend
if 'cost_history' in data:
self.add_chart("Cost Trend", ChartConfig(
chart_type="bar",
data=data['cost_history'],
title="Monthly Cost",
x_label="Month",
y_label="Cost ($)"
))
def generate_safety_report(self, data: Dict[str, Any]):
"""Generate safety report."""
self.add_header(
f"{self.project_name} - Safety Report",
f"Period: {data.get('period', 'Current')}"
)
# Safety KPIs
kpis = [
KPICard("Days Without Incident", str(data.get('days_without_incident', 0)), "days"),
KPICard("TRIR", f"{data.get('trir', 0):.2f}",
status="good" if data.get('trir', 0) <= 2 else "critical"),
KPICard("Near Misses", str(data.get('near_misses', 0))),
KPICard("Safety Observations", str(data.get('observations', 0)))
]
self.add_kpi_cards("Safety Metrics", kpis)
# Incidents
if 'incidents' in data and data['incidents']:
incidents_df = pd.DataFrame(data['incidents'])
self.add_table("Incident Log", incidents_df)
# Training
if 'training' in data:
self.add_text("Training Summary", data['training'])
def to_html(self) -> str:
"""Generate HTML representation of report."""
html = f"""
<!DOCTYPE html>
<html>
<head>
<title>{self.project_name} Report</title>
<style>
body {{ font-family: Arial, sans-serif; margin: 40px; }}
.header {{ background: #2196F3; color: white; padding: 20px; margin-bottom: 20px; }}
.section {{ margin-bottom: 30px; }}
.section-title {{ color: #333; border-bottom: 2px solid #2196F3; padding-bottom: 5px; }}
.kpi-grid {{ display: grid; grid-template-columns: repeat(4, 1fr); gap: 15px; }}
.kpi-card {{ border: 1px solid #ddd; padding: 15px; border-radius: 5px; text-align: center; }}
.kpi-value {{ font-size: 24px; font-weight: bold; }}
.kpi-name {{ color: #666; }}
.status-good {{ border-left: 4px solid #4CAF50; }}
.status-warning {{ border-left: 4px solid #FF9800; }}
.status-critical {{ border-left: 4px solid #F44336; }}
table {{ width: 100%; border-collapse: collapse; }}
th, td {{ border: 1px solid #ddd; padding: 8px; text-align: left; }}
th {{ background: #f5f5f5; }}
.page-break {{ page-break-after: always; }}
</style>
</head>
<body>
"""
for section in self.sections:
if section.section_type == SectionType.HEADER:
html += f"""
<div class="header">
<h1>{section.title}</h1>
<p>{section.content.get('subtitle', '')}</p>
</div>
"""
elif section.section_type == SectionType.TEXT:
html += f"""
<div class="section">
<h2 class="section-title">{section.title}</h2>
<p>{section.content}</p>
</div>
"""
elif section.section_type == SectionType.KPI_CARDS:
html += f"""
<div class="section">
<h2 class="section-title">{section.title}</h2>
<div class="kpi-grid">
"""
for kpi in section.content:
status_class = f"status-{kpi['status']}" if kpi['status'] != 'normal' else ''
html += f"""
<div class="kpi-card {status_class}">
<div class="kpi-name">{kpi['name']}</div>
<div class="kpi-value">{kpi['value']}</div>
<div class="kpi-target">Target: {kpi['target'] or 'N/A'}</div>
</div>
"""
html += "</div></div>"
elif section.section_type == SectionType.TABLE:
html += f"""
<div class="section">
<h2 class="section-title">{section.title}</h2>
<table>
<tr>
"""
if section.content:
for key in section.content[0].keys():
html += f"<th>{key}</th>"
html += "</tr>"
for row in section.content:
html += "<tr>"
for value in row.values():
html += f"<td>{value}</td>"
html += "</tr>"
html += "</table></div>"
elif section.section_type == SectionType.PAGE_BREAK:
html += '<div class="page-break"></div>'
html += "</body></html>"
return html
def export_to_excel(self, output_path: str) -> str:
"""Export report data to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Metadata
meta_df = pd.DataFrame([{
'Project': self.project_name,
'Report Type': self.report_type.value,
'Date': self.metadata['date'],
'Author': self.metadata['author'],
'Version': self.metadata['version']
}])
meta_df.to_excel(writer, sheet_name='Metadata', index=False)
# Each section
table_count = 0
for section in self.sections:
if section.section_type == SectionType.TABLE:
table_count += 1
sheet_name = section.title[:31] if section.title else f"Table_{table_count}"
df = pd.DataFrame(section.content)
df.to_excel(writer, sheet_name=sheet_name, index=False)
elif section.section_type == SectionType.KPI_CARDS:
kpi_df = pd.DataFrame(section.content)
kpi_df.to_excel(writer, sheet_name='KPIs', index=False)
return output_path
def get_report_structure(self) -> Dict[str, Any]:
"""Get report structure as dictionary."""
return {
'project': self.project_name,
'type': self.report_type.value,
'metadata': self.metadata,
'sections': [
{
'type': s.section_type.value,
'title': s.title,
'content': s.content
}
for s in self.sections
]
}
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 · 466 lines · 21 tokens per session scan A 2be22e11fcaf
pdf-report-generator is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo ago), licensed MIT. It adds 21 tokens to every session and 3,714 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pdf-report-generator, differing in 0 lines, and is treated as a copy.
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