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autoscaler-dashboard

Evaluate HPA autoscaling quality for TiDB Cloud components and generate HTML reports.

Updated Jun 3, 2026
Search Tags:#kubernetes#prometheus#autoscaling#hpa#tidb#performance-diagnosis#scaling-report
One-click install
npx skills add https://github.com/alex-quan-001/nutshell-skills --skill autoscaler-dashboard
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: autoscaler-dashboard
Source: https://github.com/alex-quan-001/nutshell-skills/tree/main/skills/diagnosis/autoscaler-dashboard
Command: npx skills add https://github.com/alex-quan-001/nutshell-skills --skill autoscaler-dashboard

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes assets (resource) and references (resource) components.

What problem does it solve?

This Skill eliminates the guesswork in evaluating HPA autoscaling quality for TiDB Cloud components, replacing manual log inspection and ad-hoc metrics queries with a deterministic scoring methodology and automated report generation.

Core Features & Use Cases

  • Deterministic Scoring Rubric: Evaluates HPA performance across 5 categories (Scale-Out, Scale-In, Workload Impact, Efficiency, Stability) using v0 heuristic brackets with clear thresholds.
  • Automated Report Generation: Produces self-contained HTML reports with timeline charts, event tables, and actionable recommendations by injecting JSON data into a template.
  • Component-Specific Methodology: Includes built-in evaluation logic for Coprocessor Worker HPA, with extensible architecture for TiFlash, TiDB, and other components.

Quick Start

Use the autoscaler-dashboard skill to evaluate the HPA scaling quality for cluster 2013516099403907072 and generate a visual HTML report.

Frequently Asked Questions about autoscaler-dashboard

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

FAQPage Schema
How do I evaluate HPA autoscaling performance for Kubernetes workloads?▼

To evaluate HPA autoscaling performance, this Skill applies a deterministic scoring rubric across five categories—Scale-Out, Scale-In, Workload Impact, Efficiency, and Stability—using Prometheus metrics to generate a visual HTML report.

What is the best way to generate an autoscaling report for TiDB Cloud components?▼

Generating an autoscaling report for TiDB Cloud components involves injecting JSON data into a template to produce a self-contained HTML report with timeline visualizations, event tables, and configuration recommendations.

Can I use Prometheus metrics to score HPA scaling quality?▼

Yes, you can use Prometheus metrics to score HPA scaling quality by applying v0 heuristic brackets with clear thresholds to the data, evaluating scaling performance to produce actionable configuration recommendations.

Does the autoscaling evaluation support components other than TiDB?▼

Yes, the autoscaling evaluation supports components other than TiDB. It includes built-in logic for Coprocessor Worker HPA and an extensible architecture for TiFlash, TiDB, and other HPA-managed workloads requiring scaling performance analysis.

Why does my HPA scale-in score show low stability in the performance report?▼

Your HPA scale-in score shows low stability because the deterministic scoring rubric evaluates stability as a distinct category using v0 heuristic brackets, identifying rapid scaling fluctuations or delayed metric responses within your Prometheus data.

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