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 kpi-dashboardgit 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/kpi-dashboard)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/kpi-dashboard"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/kpi-dashboard/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/kpi-dashboard"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/kpi-dashboard.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 | $0.00024 | $0.03177 |
| Opus 5 | $0.00012 | $0.01588 |
| Sonnet 5 | $0.00005 | $0.00635 |
| Haiku 4.5 | $0.00002 | $0.00318 |
Grade A, and why
kpi-dashboard 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:
- kpi-dashboard — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 393 lines — stays where its author put it; the contents beside it link to each section on GitHub.
KPI Dashboard Builder
Business Case
Problem Statement
Project monitoring challenges:
- Multiple metrics to track
- Data from various sources
- Real-time visibility needed
- Executive reporting
Solution
Unified KPI dashboard system for construction projects with automated data collection, visualization, and alerting.
Technical Implementation
import pandas as pd
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from datetime import date, datetime
from enum import Enum
class KPICategory(Enum):
COST = "cost"
SCHEDULE = "schedule"
QUALITY = "quality"
SAFETY = "safety"
PRODUCTIVITY = "productivity"
SUSTAINABILITY = "sustainability"
class KPIStatus(Enum):
ON_TARGET = "on_target"
AT_RISK = "at_risk"
CRITICAL = "critical"
class TrendDirection(Enum):
IMPROVING = "improving"
STABLE = "stable"
DECLINING = "declining"
@dataclass
class KPIDefinition:
kpi_id: str
name: str
category: KPICategory
unit: str
target: float
warning_threshold: float
critical_threshold: float
higher_is_better: bool = True
formula: str = ""
@dataclass
class KPIValue:
kpi_id: str
value: float
date: date
status: KPIStatus
trend: TrendDirection
class KPIDashboard:
"""Build and manage KPI dashboards for construction projects."""
def __init__(self, project_name: str):
self.project_name = project_name
self.kpis: Dict[str, KPIDefinition] = {}
self.history: Dict[str, List[KPIValue]] = {}
self._define_standard_kpis()
def _define_standard_kpis(self):
"""Define standard construction KPIs."""
standard_kpis = [
# Cost KPIs
KPIDefinition("CPI", "Cost Performance Index", KPICategory.COST,
"ratio", 1.0, 0.95, 0.90, True, "BCWP / ACWP"),
KPIDefinition("CV", "Cost Variance", KPICategory.COST,
"$", 0, -50000, -100000, True, "BCWP - ACWP"),
KPIDefinition("BUDGET_USED", "Budget Utilization", KPICategory.COST,
"%", 100, 105, 110, False),
# Schedule KPIs
KPIDefinition("SPI", "Schedule Performance Index", KPICategory.SCHEDULE,
"ratio", 1.0, 0.95, 0.90, True, "BCWP / BCWS"),
KPIDefinition("SV", "Schedule Variance", KPICategory.SCHEDULE,
"days", 0, -7, -14, True),
KPIDefinition("COMPLETION", "Project Completion", KPICategory.SCHEDULE,
"%", 100, 95, 90, True),
# Quality KPIs
KPIDefinition("DEFECT_RATE", "Defect Rate", KPICategory.QUALITY,
"per 1000 units", 0, 5, 10, False),
KPIDefinition("FIRST_PASS", "First Pass Yield", KPICategory.QUALITY,
"%", 95, 90, 85, True),
KPIDefinition("REWORK", "Rework Percentage", KPICategory.QUALITY,
"%", 0, 3, 5, False),
# Safety KPIs
KPIDefinition("TRIR", "Total Recordable Incident Rate", KPICategory.SAFETY,
"per 200k hours", 0, 2, 4, False),
KPIDefinition("LOST_DAYS", "Lost Time Injuries", KPICategory.SAFETY,
"incidents", 0, 1, 3, False),
KPIDefinition("SAFETY_OBSERVATIONS", "Safety Observations", KPICategory.SAFETY,
"count", 50, 30, 20, True),
# Productivity KPIs
KPIDefinition("LABOR_PROD", "Labor Productivity", KPICategory.PRODUCTIVITY,
"%", 100, 90, 80, True),
KPIDefinition("EQUIP_UTIL", "Equipment Utilization", KPICategory.PRODUCTIVITY,
"%", 85, 70, 60, True),
]
for kpi in standard_kpis:
self.kpis[kpi.kpi_id] = kpi
self.history[kpi.kpi_id] = []
def add_custom_kpi(self, kpi: KPIDefinition):
"""Add custom KPI definition."""
self.kpis[kpi.kpi_id] = kpi
self.history[kpi.kpi_id] = []
def record_value(self, kpi_id: str, value: float, record_date: date = None):
"""Record KPI value."""
if kpi_id not in self.kpis:
return
kpi = self.kpis[kpi_id]
record_date = record_date or date.today()
# Calculate status
status = self._calculate_status(kpi, value)
# Calculate trend
trend = self._calculate_trend(kpi_id, value)
kpi_value = KPIValue(
kpi_id=kpi_id,
value=value,
date=record_date,
status=status,
trend=trend
)
self.history[kpi_id].append(kpi_value)
def _calculate_status(self, kpi: KPIDefinition, value: float) -> KPIStatus:
"""Calculate KPI status based on thresholds."""
if kpi.higher_is_better:
if value >= kpi.target:
return KPIStatus.ON_TARGET
elif value >= kpi.warning_threshold:
return KPIStatus.AT_RISK
else:
return KPIStatus.CRITICAL
else:
if value <= kpi.target:
return KPIStatus.ON_TARGET
elif value <= kpi.warning_threshold:
return KPIStatus.AT_RISK
else:
return KPIStatus.CRITICAL
def _calculate_trend(self, kpi_id: str, current_value: float) -> TrendDirection:
"""Calculate trend direction."""
history = self.history.get(kpi_id, [])
if len(history) < 2:
return TrendDirection.STABLE
# Compare with average of last 3 values
recent_values = [h.value for h in history[-3:]]
avg = sum(recent_values) / len(recent_values)
kpi = self.kpis[kpi_id]
diff = current_value - avg
if abs(diff) < avg * 0.05: # Within 5%
return TrendDirection.STABLE
elif (diff > 0 and kpi.higher_is_better) or (diff < 0 and not kpi.higher_is_better):
return TrendDirection.IMPROVING
else:
return TrendDirection.DECLINING
def get_current_values(self) -> Dict[str, KPIValue]:
"""Get most recent value for each KPI."""
current = {}
for kpi_id, history in self.history.items():
if history:
current[kpi_id] = history[-1]
return current
def get_dashboard_summary(self) -> Dict[str, Any]:
"""Get dashboard summary."""
current = self.get_current_values()
summary = {
'project': self.project_name,
'date': date.today().isoformat(),
'total_kpis': len(self.kpis),
'by_status': {s.value: 0 for s in KPIStatus},
'by_category': {},
'alerts': []
}
for kpi_id, value in current.items():
summary['by_status'][value.status.value] += 1
category = self.kpis[kpi_id].category.value
if category not in summary['by_category']:
summary['by_category'][category] = {'on_target': 0, 'at_risk': 0, 'critical': 0}
summary['by_category'][category][value.status.value] += 1
if value.status == KPIStatus.CRITICAL:
summary['alerts'].append({
'kpi': self.kpis[kpi_id].name,
'value': value.value,
'target': self.kpis[kpi_id].target,
'status': 'critical'
})
return summary
def get_kpi_details(self, kpi_id: str) -> Dict[str, Any]:
"""Get detailed KPI information."""
if kpi_id not in self.kpis:
return {}
kpi = self.kpis[kpi_id]
history = self.history.get(kpi_id, [])
return {
'definition': {
'id': kpi.kpi_id,
'name': kpi.name,
'category': kpi.category.value,
'unit': kpi.unit,
'target': kpi.target,
'formula': kpi.formula
},
'current': {
'value': history[-1].value if history else None,
'status': history[-1].status.value if history else None,
'trend': history[-1].trend.value if history else None
},
'history': [
{'date': h.date.isoformat(), 'value': h.value, 'status': h.status.value}
for h in history
]
}
def generate_html_dashboard(self) -> str:
"""Generate HTML dashboard."""
summary = self.get_dashboard_summary()
current = self.get_current_values()
html = f"""
<!DOCTYPE html>
<html>
<head>
<title>KPI Dashboard - {self.project_name}</title>
<style>
body {{ font-family: Arial, sans-serif; margin: 20px; }}
.header {{ background: #2196F3; color: white; padding: 20px; margin-bottom: 20px; }}
.kpi-grid {{ display: grid; grid-template-columns: repeat(4, 1fr); gap: 15px; }}
.kpi-card {{ border: 1px solid #ddd; padding: 15px; border-radius: 5px; }}
.on_target {{ border-left: 4px solid #4CAF50; }}
.at_risk {{ border-left: 4px solid #FF9800; }}
.critical {{ border-left: 4px solid #F44336; }}
.kpi-value {{ font-size: 24px; font-weight: bold; }}
.kpi-name {{ color: #666; font-size: 14px; }}
</style>
</head>
<body>
<div class="header">
<h1>{self.project_name} - KPI Dashboard</h1>
<p>Last updated: {summary['date']}</p>
</div>
<div class="kpi-grid">
"""
for kpi_id, value in current.items():
kpi = self.kpis[kpi_id]
html += f"""
<div class="kpi-card {value.status.value}">
<div class="kpi-name">{kpi.name}</div>
<div class="kpi-value">{value.value:.2f} {kpi.unit}</div>
<div>Target: {kpi.target} | Trend: {value.trend.value}</div>
</div>
"""
html += "</div></body></html>"
return html
def export_to_excel(self, output_path: str) -> str:
"""Export dashboard to Excel."""
with pd.ExcelWriter(output_path, engine='openpyxl') as writer:
# Summary
summary = self.get_dashboard_summary()
summary_df = pd.DataFrame([{
'Project': summary['project'],
'Date': summary['date'],
'On Target': summary['by_status']['on_target'],
'At Risk': summary['by_status']['at_risk'],
'Critical': summary['by_status']['critical']
}])
summary_df.to_excel(writer, sheet_name='Summary', index=False)
# Current values
current = self.get_current_values()
current_data = []
for kpi_id, value in current.items():
kpi = self.kpis[kpi_id]
current_data.append({
'KPI': kpi.name,
'Category': kpi.category.value,
'Value': value.value,
'Unit': kpi.unit,
'Target': kpi.target,
'Status': value.status.value,
'Trend': value.trend.value
})
current_df = pd.DataFrame(current_data)
current_df.to_excel(writer, sheet_name='Current KPIs', index=False)
return output_path
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 · 393 lines · 24 tokens per session scan A 4195538feece
kpi-dashboard 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 24 tokens to every session and 3,177 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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