kpi-dashboard

kpi-dashboard is a skill for Claude Code, Codex from jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction. It costs 24 tokens per session (3,177 once invoked), scanned A, a copy of kpi-dashboard, MIT.

A dashboard builder for construction KPIs, or key performance indicators. It brings together cost, schedule, quality, safety, productivity, and sustainability measures with targets and status levels.

In plain words
What is it for?
Use it to collect, visualize, and monitor project metrics, including CPI, SPI, quality, and safety measures, with alerts.
Why use it?
It gives project teams and executives one view of performance instead of requiring them to inspect many separate data sources.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to collect, visualize, and monitor project metrics, including CPI, SPI, quality, and safety measures, with alerts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/kpi-dashboard
Install

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.

Any agent
npx skills add jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill kpi-dashboard
Clone the repo
git clone --depth 1 https://github.com/jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for kpi-dashboard

README.md
[![agentmods](https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/kpi-dashboard/github.svg)](https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/kpi-dashboard)
Your own site
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/kpi-dashboard"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/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.

agentmods 80×15 button for kpi-dashboard

Your own site · 80×15
<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/kpi-dashboard"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/kpi-dashboard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,177 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash 4195538feece, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

Origin

This is a copy

100% identical to kpi-dashboard — 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.

2_DDC_Book/4.1-Analytics-KPI-Dashboard/kpi-dashboard/SKILL.md · 393 lines

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

Read the full file on GitHub · 393 lines

Files

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.

Changes

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.

  1. 9d ago First seen · 393 lines · 24 tokens per session scan A 4195538feece

Subscribe to this mod's changes

kpi-dashboard 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 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. It is 100% identical to kpi-dashboard, differing in 0 lines, and is treated as a copy.

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