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Think Fermi Estimation

Category: Growth & Marketing  ·  Sub-category: conversion  ·  Last updated:
Produces a Fermi decomposition worksheet that estimates an unknown numeric quantity by factoring it into a chain of order-of-magnitude sub-estimates, guessing each to within a band, then multiplying back to a point estimate plus a compounded low/high range, with an independence check and a dominant-uncertainty flag. Use when you need a number and no lookup-able data or genuine reference class exists, so the magnitude has to be built from factors (for example sizing a market, a load, a cost, or a conversion you cannot look up). Not for forecasting from real base rates (use reference-class-forecasting) or decomposing a question for coverage with no number (use issue-tree).
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From the source SKILL.md

<!-- thinking-framework-skills | https://github.com/product-on-purpose/thinking-framework-skills | Apache-2.0 --> # Fermi Estimation

What this skill does

Think Fermi Estimation is a community-contributed Claude Code skill in the conversion sub-category. It ships as a SKILL.md file that Claude Code auto-discovers under ~/.claude/skills/think-fermi-estimation/ and loads when your prompt matches the skill's trigger.

When to invoke it: Use when you need a number and no lookup-able data or genuine reference class exists, so the magnitude has to be built from factors (for example sizing a market, a load, a cost, or a conversion you cannot look up). Not for forecasting from real base rates (use reference-class-forecasting) or decomposing a question for coverage with no number (use issue-tree).

Who uses this skill

The Think Fermi Estimation Claude Code skill is built for growth marketers, product-led growth teams, founders running experiments, and performance marketing teams. It's part of ClaudSkills (also referred to as Claude Skills or Claude Code Skills) — the open community-curated registry of 177,000+ SKILL.md files for Anthropic's Claude Code agent and the wider Claude ecosystem (Claude API, Claude Agent SDK).

How to install

Free

Manual install (2 steps)

mkdir -p ~/.claude/skills/think-fermi-estimation
curl -L https://claudskills.com/skills/think-fermi-estimation/SKILL.md \
  -o ~/.claude/skills/think-fermi-estimation/SKILL.md

Or just download SKILL.md directly and drop it into ~/.claude/skills/think-fermi-estimation/. Claude Code auto-discovers it on next session.

Skills live at ~/.claude/skills/think-fermi-estimation/SKILL.md on macOS/Linux, or %USERPROFILE%\.claude\skills\think-fermi-estimation\SKILL.md on Windows. See the full install guide for step-by-step instructions.

Telegram

📱 Install from your phone or desktop Telegram

Open @claudskills_bot on Telegram, tap Open Desktop App, and the desktop app installs this skill for you. Or share the bot link with a colleague — they get the same one-tap install. Learn more →

Pro

One-click install via the desktop app

The ClaudSkills desktop app installs any skill directly into ~/.claude/skills/ with one click — no terminal required. Pro starts at $9/mo or $149 lifetime.

Pro

For the full experience including quality scoring and one-click install features for each skill — upgrade to Pro.

See pricing → Download desktop app

Frequently asked questions

How do I install the Think Fermi Estimation Claude Code skill?
Install via the ClaudSkills desktop app (one click) or copy SKILL.md from the source repository to ~/.claude/skills/think-fermi-estimation/SKILL.md and restart Claude Code. Both flows are detailed at claudskills.com/install/.
What does the Think Fermi Estimation skill do?
Produces a Fermi decomposition worksheet that estimates an unknown numeric quantity by factoring it into a chain of order-of-magnitude sub-estimates, guessing each to within a band, then multiplying back to a point estimate plus a compounded low/high range, with an independence check and a dominant-uncertainty flag. Use when you need a number and no lookup-able data or genuine reference class exists, so the magnitude has to be built from factors (for example sizing a market, a load, a cost, or a conversion you cannot look up). Not for forecasting from real base rates (use reference-class-forecasting) or decomposing a question for coverage with no number (use issue-tree).
Is this skill free to install?
Yes. ClaudSkills is an open registry — every skill keeps its source repository's license, and manual install via copy is free. ClaudSkills Pro ($9/mo, $79/yr, or $149 one-time) adds one-click install via the desktop app and a multi-signal Quality Score.
When should I use the Think Fermi Estimation skill?
Use Think Fermi Estimation when your Claude Code task falls under the Growth & Marketing category — specifically in the conversion area. Claude Code auto-discovers installed skills and invokes the right one based on the task description, so you can also ask Claude directly (e.g. "use Think Fermi Estimation" or describe the task and let Claude pick). Browse related skills at /category/growth/.
What is a Claude Code skill and how does the Think Fermi Estimation skill fit in?
A Claude Code skill is a SKILL.md file that lives under ~/.claude/skills/<name>/ and tells the Claude Code CLI agent how to perform a specific task (instructions, prompts, allowed tools). Skills are auto-discovered at session start. Think Fermi Estimation is one of 67,000+ skills indexed in the open ClaudSkills catalog, classified under the Growth & Marketing category. Learn more at /learn/what-is-a-claude-skill/.

Attribution & license

Cite this skill

If you reference this skill in a blog post, paper, or documentation, you can cite it as:

APA
product-on-purpose. (2026). Think Fermi Estimation [Claude Code skill]. ClaudSkills. https://claudskills.com/skills/think-fermi-estimation/
BibTeX
@misc{think-fermi-estimation-2026,
  author    = {product-on-purpose},
  title     = {Think Fermi Estimation [Claude Code skill]},
  year      = {2026},
  publisher = {ClaudSkills},
  url       = {https://claudskills.com/skills/think-fermi-estimation/}
}

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Security scan

Grade A · scanned 2026-08-06 — free static scan against the OWASP Agentic Skills Top 10.

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