dashboard

Generate observability dashboards for engineer, team, AI, DORA, and health modes.

54|3|Updated Feb 4, 2026

Search Tags:#analytics #ai #metrics #dashboard #observability #engineering #dora

Ready to run

npx skills add https://github.com/arcasilesgroup/ai-engineering --skill dashboard-arcasilesgroup

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: dashboard
Source: https://github.com/arcasilesgroup/ai-engineering/tree/main/.agents/skills/dashboard
Command: npx skills add https://github.com/arcasilesgroup/ai-engineering --skill dashboard-arcasilesgroup

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill provides comprehensive observability dashboards to gain insights into various aspects of AI-assisted software delivery, including code quality, team performance, AI behavior, and overall system health.

Core Features & Use Cases

Multi-Mode Dashboards: Generates insights across five distinct modes: engineer, team, AI, DORA, and health.
Audience Tiers: Caters to different audiences by providing tailored metrics and analysis.
Actionable Recommendations: Identifies trends, anomalies, and suggests top improvement actions.
Use Case: A team lead can use the 'team' mode to understand framework adoption and skill usage, while an AI engineer can use the 'ai' mode to identify opportunities for self-optimization.

Quick Start

Generate an observability dashboard for the engineer mode.

Frequently Asked Questions about dashboard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I generate observability dashboards for AI engineering metrics?▼

Observability dashboards for AI engineering are generated via CLI commands, analyzing code quality, delivery velocity, AI self-awareness, and system health to provide actionable recommendations and confidence indicators.

What DORA metrics are included in software delivery observability dashboards?▼

DORA dashboards analyze delivery velocity alongside code quality, AI behavior, and overall system health, providing tailored metrics for different audience tiers to identify trends and anomalies.

How do I monitor team performance and AI behavior in software engineering?▼

Monitoring team performance and AI behavior uses distinct dashboard modes: the team mode tracks framework adoption and skill usage, while the AI mode identifies opportunities for AI self-optimization.

Can I tailor engineering metrics dashboards for different audience tiers?▼

Engineering metrics dashboards cater to different audience tiers by providing tailored metrics and analysis across five distinct modes: engineer, team, AI, DORA, and health.

What are the limitations of using CLI commands for system health observability?▼

System health observability operates via CLI commands and adheres to strict rules for data collection and reporting, which may constrain automated integration into continuous deployment pipelines without external orchestration.