metodologia-performance-engineering

Deliver baselines, load tests, capacity models, caching, CDN rules, and SLO design.

Updated Mar 31, 2026

Search Tags:#performance #caching #cdn #load-testing #slo #sla #capacity-planning

Ready to run

npx skills add https://github.com/JaviMontano/metodologia-propuesta-agent-public --skill metodologia-performance-engineering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: metodologia-performance-engineering
Source: https://github.com/JaviMontano/metodologia-propuesta-agent-public/tree/main/.claude/skills/quality/performance-engineering
Command: npx skills add https://github.com/JaviMontano/metodologia-propuesta-agent-public --skill metodologia-performance-engineering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Performance engineering helps ensure systems meet latency, throughput, and reliability targets under current and projected load by delivering actionable baselines, testing strategies, and capacity plans.

Core Features & Use Cases

Baseline assessment (p50/p95/p99), profiling, and bottleneck identification for any system.
Load testing strategy design (baseline, ramp, stress, spike, soak) and CI/CD gating.
Capacity planning using USL-based models and runway calculations.
Caching, CDN, and edge strategy design to optimize latency and cost.
SLA/SLO design with error budgets and burn-rate alerts.
Templates and artifacts for deliverables (reports, dashboards, playbooks).

Quick Start

Run a baseline performance assessment on the target system to establish p50/p95/p99 latency, then proceed with load testing and capacity planning.

Frequently Asked Questions about metodologia-performance-engineering

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

FAQPage Schema

How do I establish a performance baseline for my system's latency?▼

To establish a performance baseline, you profile the system to capture p50, p95, and p99 latency metrics. This identifies bottlenecks and provides actionable data to guide load testing and capacity planning.

What is the best way to design load tests for microservices and serverless architectures?▼

The best way to design load tests is creating strategies for baseline, ramp, stress, spike, and soak scenarios. These tests can be integrated into CI/CD pipelines to ensure microservices and serverless architectures meet latency targets.

How does USL capacity planning work for projected traffic loads?▼

USL capacity planning uses Universal Scalability Law models to calculate system runway and predict throughput limits. It ensures architectures handle projected traffic loads without violating latency or reliability targets.

When do I need to design SLOs with error budgets and burn-rate alerts?▼

You need to design SLOs with error budgets and burn-rate alerts when your system requires strict reliability targets. This process tracks service level agreements and manages the trade-off between feature velocity and system stability.

Can I use caching and CDN rules to optimize edge latency and cost?▼

Yes, you can use caching and CDN rules to optimize edge latency and cost. Designing a targeted edge strategy reduces origin load, improves response times across monoliths and microservices, and lowers infrastructure expenses.

What artifacts are produced during performance engineering?▼

Performance engineering produces actionable artifacts including templates, dashboards, playbooks, and capacity models. These deliverables support decision-making by translating percentile metrics and load testing results into operational guidelines.