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Operational dashboard generation skill for KPI visualization and real-time monitoring.
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npx skills add https://github.com/a5c-ai/babysitter --skill operational-dashboard-generator명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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Reference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.
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SOC 직업 분류 기준
| name | operational-dashboard-generator |
| description | Operational dashboard generation skill for KPI visualization and real-time monitoring. |
| allowed-tools | Bash(*) Read Write Edit Glob Grep WebFetch |
| metadata | {"author":"babysitter-sdk","version":"1.0.0","category":"continuous-improvement","backlog-id":"SK-IE-037"} |
| graph | {"domains":["domain:industrial-engineering"],"skillAreas":["skill-area:statistical-analysis","skill-area:organizational-design","skill-area:data-analysis"],"roles":["role:operations-analyst","role:research-engineer"]} |
You are operational-dashboard-generator - a specialized skill for generating operational dashboards with KPI visualization and real-time monitoring capabilities.
This skill enables AI-powered dashboard generation including:
pandas pd
numpy np
dataclasses dataclass
typing , ,
enum Enum
():
SAFETY =
QUALITY =
DELIVERY =
COST =
PRODUCTIVITY =
MORALE =
:
:
name:
category: KPICategory
formula:
unit:
target:
warning_threshold:
critical_threshold:
higher_is_better: =
frequency: =
():
[
KPIDefinition(
=, name=, category=KPICategory.PRODUCTIVITY,
formula=,
unit=, target=, warning_threshold=, critical_threshold=
),
KPIDefinition(
=, name=, category=KPICategory.QUALITY,
formula=,
unit=, target=, warning_threshold=, critical_threshold=
),
KPIDefinition(
=, name=, category=KPICategory.DELIVERY,
formula=,
unit=, target=, warning_threshold=, critical_threshold=
),
KPIDefinition(
=, name=, category=KPICategory.SAFETY,
formula=,
unit=, target=, warning_threshold=, critical_threshold=,
higher_is_better=
),
KPIDefinition(
=, name=, category=KPICategory.PRODUCTIVITY,
formula=,
unit=, target=, warning_threshold=, critical_threshold=
),
KPIDefinition(
=, name=, category=KPICategory.COST,
formula=,
unit=, target=, warning_threshold=, critical_threshold=,
higher_is_better=
)
]
():
:
value = (kpi.formula, {: {}}, data)
kpi.higher_is_better:
value >= kpi.target:
status =
value >= kpi.warning_threshold:
status =
:
status =
:
value <= kpi.target:
status =
value <= kpi.warning_threshold:
status =
:
status =
{
: kpi.,
: kpi.name,
: (value, ),
: kpi.unit,
: kpi.target,
: status,
: (value - kpi.target, )
}
Exception e:
{: kpi., : (e)}
def generate_dashboard_layout(kpis: List[KPIDefinition], layout_type: str = "standard"):
"""
Generate dashboard layout configuration
layout_type: 'standard', 'executive', 'operational', 'lean'
"""
if layout_type == "standard":
layout = {
"type": "grid",
"columns": 4,
"rows": 3,
"sections": [
{
"id": "header",
"row": 1, "col_span": 4,
"content": "title_and_period_selector"
},
{
"id": "summary_cards",
"row": 2, "col_span": 4,
"content": "kpi_summary_cards",
"kpis": [k.id for k in kpis[:6]]
},
{
"id": "trends",
"row": 3, "col": 1, "col_span": 2,
"content": "trend_charts"
},
{
"id": "details",
"row": 3, "col": 3, "col_span": 2,
"content": "detail_tables"
}
]
}
elif layout_type == "executive":
layout = {
"type": "single_page",
"sections": [
{"id": "headline_kpis", "position": "top", "height": "20%"},
{"id": "trend_summary", "position": "middle", "height": "50%"},
{"id": "action_items", "position": "bottom", "height": "30%"}
]
}
elif layout_type == "lean":
# Visual management board style
layout = {
"type": "sqdc", # Safety, Quality, Delivery, Cost
"columns": 4,
"sections": [
{"id": "safety", "col": 1, "category": "SAFETY"},
{"id": "quality", "col": 2, "category": "QUALITY"},
{"id": "delivery", "col": 3, "category": "DELIVERY"},
{"id": "cost", "col": 4, "category": "COST"}
],
"row_types": ["current_status", "trend_sparkline", "action_items"]
}
return layout
def generate_kpi_card_config(kpi: KPIDefinition):
"""Generate configuration for a KPI card widget"""
return {
"widget_type": "kpi_card",
"kpi_id": kpi.id,
"title": kpi.name,
"display": {
"value_format": f"{{value:.1f}}{kpi.unit}",
"show_target": True,
"show_trend": True,
"trend_periods": 7
},
"colors": {
"green": kpi.target,
"yellow": kpi.warning_threshold,
"red": kpi.critical_threshold
},
"gauge": kpi.id in ["oee", "fpy", "otd"] # Show gauge for percentage KPIs
}
def analyze_kpi_trends(historical_data: pd.DataFrame, kpi_id: str,
periods: int = 30):
"""
Analyze trends for a KPI
historical_data: DataFrame with ['date', 'kpi_id', 'value']
"""
kpi_data = historical_data[historical_data['kpi_id'] == kpi_id].copy()
kpi_data = kpi_data.sort_values('date').tail(periods)
if len(kpi_data) < 2:
return {"error": "Insufficient data for trend analysis"}
values = kpi_data['value'].values
dates = kpi_data['date'].values
# Calculate statistics
current = values[-1]
previous = values[-2]
change = current - previous
change_pct = (change / previous * 100) if previous != 0 else 0
# Moving averages
ma_7 = np.mean(values[-7:]) if len(values) >= 7 else np.mean(values)
ma_30 = np.mean(values[-30:]) if len(values) >= 30 else np.mean(values)
# Trend direction (linear regression)
x = np.arange(len(values))
slope, intercept = np.polyfit(x, values, 1)
if slope > 0.01:
trend_direction = "improving"
elif slope < -0.01:
trend_direction = "declining"
else:
trend_direction = "stable"
# Variability
std_dev = np.std(values)
cv = (std_dev / np.mean(values) * 100) if np.mean(values) != 0 else 0
return {
"kpi_id": kpi_id,
"current_value": round(current, 2),
"previous_value": round(previous, 2),
"change": round(change, 2),
"change_percent": round(change_pct, 1),
"trend_direction": trend_direction,
"slope": round(slope, 4),
"moving_average_7": round(ma_7, 2),
"moving_average_30": round(ma_30, 2),
"std_deviation": round(std_dev, 2),
"coefficient_of_variation": round(cv, 1),
"sparkline_data": values.tolist()
}
def configure_alerts(kpis: List[KPIDefinition], notification_config: dict):
"""
Configure alert rules for dashboard
notification_config: {'email': [], 'sms': [], 'teams': []}
"""
alerts = []
for kpi in kpis:
# Warning alert
alerts.append({
"alert_id": f"{kpi.id}_warning",
"kpi_id": kpi.id,
"level": "warning",
"condition": f"value {'<' if kpi.higher_is_better else '>'} {kpi.warning_threshold}",
"message": f"{kpi.name} has reached warning level",
"notifications": notification_config.get('email', []),
"frequency": "first_occurrence"
})
# Critical alert
alerts.append({
"alert_id": f"{kpi.id}_critical",
"kpi_id": kpi.id,
"level": "critical",
"condition": f"value {'<' if kpi.higher_is_better else '>'} {kpi.critical_threshold}",
"message": f"{kpi.name} has reached critical level - immediate action required",
"notifications": notification_config.get('email', []) + notification_config.get('sms', []),
"frequency": "every_occurrence",
"escalation_after_minutes": 30
})
# Trend alert
alerts.append({
"alert_id": f"{kpi.id}_trend",
"kpi_id": kpi.id,
"level": "info",
"condition": "consecutive_decline >= 3",
"message": f"{kpi.name} has declined for 3 consecutive periods",
"notifications": notification_config.get('email', []),
"frequency": "daily_digest"
})
return {
"alerts": alerts,
"total_rules": len(alerts),
"notification_channels": list(notification_config.keys())
}
def configure_drilldowns(kpi: KPIDefinition, dimensions: list):
"""
Configure drill-down paths for a KPI
dimensions: ['shift', 'line', 'product', 'operator']
"""
drilldowns = []
for i, dim in enumerate(dimensions):
drilldowns.append({
"level": i + 1,
"dimension": dim,
"aggregation": "sum" if "count" in kpi.formula else "avg",
"chart_type": "bar" if i < 2 else "table",
"filter_enabled": True
})
# Add time-based drilldown
drilldowns.append({
"level": len(dimensions) + 1,
"dimension": "time",
"granularity": ["month", "week", "day", "shift", "hour"],
"chart_type": "line",
"default_granularity": "day"
})
return {
"kpi_id": kpi.id,
"drilldown_path": drilldowns,
"max_levels": len(drilldowns)
}
def export_dashboard_config(layout: dict, kpis: List[dict],
alerts: List[dict], drilldowns: List[dict]):
"""
Export complete dashboard configuration
"""
config = {
"dashboard": {
"name": "Operations Dashboard",
"version": "1.0",
"refresh_rate_seconds": 300,
"timezone": "local"
},
"layout": layout,
"kpis": kpis,
"widgets": [],
"alerts": alerts,
"drilldowns": drilldowns,
"data_sources": [
{
"id": "production_db",
"type": "database",
"refresh": "5min"
},
{
"id": "mes_api",
"type": "api",
"refresh": "realtime"
}
],
"filters": [
{"id": "date_range", "type": "date_range", "default": "last_30_days"},
{"id": "shift", "type": "dropdown", "options": ["All", "Day", "Night"]},
{"id": "line", "type": "multi_select", "source": "production_db.lines"}
]
}
# Generate widgets from KPIs
for kpi in kpis:
config["widgets"].append({
"widget_id": f"card_{kpi['kpi_id']}",
"type": "kpi_card",
"kpi": kpi['kpi_id'],
"position": "auto"
})
return config
This skill integrates with the following processes:
performance-monitoring-setup.jsvisual-management-implementation.jscontinuous-improvement-program.js{
"dashboard_config": {
"name": "Operations Dashboard",
"layout": "standard",
"refresh_rate": 300
},
"kpis": [
{
"id": "oee",
"name": "OEE",
"current_value": 82.5,
"target": 85,
"status": "yellow",
"trend": "improving"
}
],
"alerts": {
"active": 2,
"critical": 0,
"warning": 2
},
"layout_spec": {
"type": "grid",
"columns": 4,
"widgets": 12
}
}