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fermi-estimation

Produce order-of-magnitude estimates for system capacity, latency, throughput, storage, cost, and feasibility.

2|Updated Apr 30, 2026
Search Tags:#performance#scalability#estimation#latency#cost analysis#capacity planning#systems design
One-click install
npx skills add https://github.com/NlightNFotis/skills --skill fermi-estimation-nlightnfotis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fermi-estimation
Source: https://github.com/NlightNFotis/skills/tree/main/fermi-estimation
Command: npx skills add https://github.com/NlightNFotis/skills --skill fermi-estimation-nlightnfotis

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you quickly judge whether a system idea is feasible before investing time in design, implementation, or optimization. It reduces costly mistakes by turning vague scale questions about capacity, latency, throughput, storage, and cost into defensible order-of-magnitude estimates.

Core Features & Use Cases

  • Back-of-the-envelope system estimation: Breaks large questions into simple factors, brackets uncertainty, and combines them into a practical estimate.
  • Engineering sanity checks: Compares results against physical limits and trusted reference points like network latency, disk speed, RAM bandwidth, and human response time.
  • Decision support for design tradeoffs: Useful for sizing queues, caches, databases, server fleets, storage growth, and cloud spend before committing to an architecture.
  • Use Case: If a team proposes serving 10 million users from a single region on one database, this Skill helps estimate peak load, expected QPS, and whether the plan is robust or likely to fail under realistic assumptions.

Quick Start

Ask the fermi-estimation skill to estimate whether your proposed architecture can meet a stated user, latency, or cost target within one order of magnitude.

Frequently Asked Questions about fermi-estimation

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

FAQPage Schema
How do I estimate system capacity and throughput before implementation?▼

Back-of-the-envelope system estimation breaks large capacity, latency, and throughput questions into simple measurable factors, brackets uncertainty with low-high ranges, and combines them into a practical order-of-magnitude estimate for sizing server fleets and databases.

What is the best way to perform a sanity check on cloud cost sizing and scalability decisions?▼

The best way to perform a cloud cost sizing sanity check is to estimate expected scale by validating your results against physical limits and trusted operational reference points like network latency, disk speed, RAM bandwidth, and human response time.

Can I use back-of-the-envelope estimation to evaluate database scalability for 10 million users?▼

Yes, you can use back-of-the-envelope estimation to evaluate database scalability for 10 million users by estimating peak load, expected QPS, and storage growth to determine whether a single-region architecture is robust or likely to fail under realistic assumptions.

How do I size queues, caches, and server fleets during systems design?▼

To size queues, caches, and server fleets during systems design, use order-of-magnitude estimation to judge feasibility, compare expected load against hardware limits, and support tradeoff decisions before committing to a specific architecture.

When should I not rely on order-of-magnitude estimates for performance budgeting?▼

You should not rely on order-of-magnitude estimates for performance budgeting when precise measurements are required, as this approach targets architecture feasibility, capacity planning, and scalability decisions under uncertainty rather than exact benchmark provisioning.

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