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[Free Special Lecture] Is “65% Token Savings” Real? — We Tested the Viral Caveman Skill Ourselves with the Same Questions

Before installing the caveman skill that has earned over 110,000 stars with its “65% token reduction” claim, I tested it myself. I ran the same questions 156 times in Claude Code under four conditions—default, “Answer concisely,” caveman, and ultracave—and used a script to count output tokens and correct answers. In under 10 minutes, I’ll show you how to measure fairly and share a free measurement kit you can use with your own questions.

4 learners are taking this course

Level Beginner

Course period Unlimited

AI
AI
prompt engineering
prompt engineering
LLM
LLM
AI Agent
AI Agent
claude
claude
AI
AI
prompt engineering
prompt engineering
LLM
LLM
AI Agent
AI Agent
claude
claude

What you will gain after the course

  • How to recheck viral figures like “N% token savings” with real-world measurements, keeping the question the same and changing only the conditions

  • Why the real benchmark for token-saving tools is not “no instructions,” but the one-line instruction “Answer concisely”—and why

  • How to add the caveman skill as a single project-level skill without installing it globally, then test it pinned to a commit hash

  • How to use the free measurement kit (claude-code-token-meter) to measure and tabulate output tokens and correct answers for my questions by condition

FREE · 10-MIN · CLAUDE CODE TOKEN METER

“65% fewer tokens,”
Test it with your own prompts before installing it

Same question · only the conditions changed · 156 runs. The script counts the answers—not an AI judge. The measuring kit is yours, free of charge.

📏 156 real-world tests under your conditions

Basic · “Answer concisely.” · caveman · ultracave

⚖️ The real basis for comparison

One line of “Answer concisely,” not “No instructions”

🧰 Free measurement kit

Default: practice run · standard library only · MIT

Our measurements — 10 questions × 3 runs · median output tokens (Claude Code 2.1.314 · Opus 5.5 · 2026-10-31)

Default 1,100 · “Answer concisely.” 335(−69.5%) · caveman 571(−48.1%) · ultracave 499.5(−54.6%)
“Answer concisely.” was shorter than caveman for all 10 questions · Answers were the same under all four conditions (verified by human review)
3 file-editing tasks × 3 runs: default 863 · “Answer concisely.” 677 · caveman 895 · ultracave 798 · All checks PASS: 9/9

Best for:

  • “Token-saving” skill · For those who want to check the impact before installing the prompt
  • For those who’d rather test it with their own questions than take the viral numbers at face value
  • For those whose AI answers are so long they have to add “keep it short” every time

To be honest,

  • What did we measure? We measured only one of caveman 4.0’s six components: “response style (Say less).” We did not measure the other components, such as the proxy or auto-apply, or global installation.
  • The author said the same thing: the caveman README says “Be concise” is the real baseline, and reports a further median reduction of 3% on top of that across 10 English questions (version 3.1.0 rules). Under our conditions (Korean · version 4.0.1 rules), “Answer concisely.” was shorter. It’s not that “the viral claim is false”; it’s that “the results may differ if you change the baseline and measure using your own questions.”
  • Limitations: Based on 10 Korean questions and 3 tasks, using fresh sessions and Claude Code without user settings or MCP. The cost figures are CLI estimates, not actual charges.
  • The voice is AI-generated speech (TTS), and the screen shows an actual recording of Claude Code running in conversation mode. Segments played at faster speed are labeled on screen as “actual N seconds → M seconds.”
  • This lesson is unrelated to the Caveman project. “Caveman” is the author’s name, and no logo was used.

How to decide where to use tokens is covered in the companion course "Where to Put Tokens".

A place to share together

The fastest way to get unstuck on an error is to ask AI first. Share your measurement results in the open chat room.

Recommended for
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Who is this course right for?

  • “Token-saving” skill · Claude Code users who want to check whether the prompt actually works before installing it

  • Solo creators, strategists, and developers who want to measure viral metrics themselves before turning them into a course or an article.

  • For anyone who keeps adding “keep it short” because the AI’s answers are too long.

Need to know before starting?

  • No coding knowledge required — proceed with the kit folder and copy-and-paste commands.

  • Recommended environment: macOS or Windows PC · Python 3 · Claude Code (measurements use my Claude usage — the kit defaults to practice runs)

Hello
This is gptssam

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I’m GPT Teacher, researching and building AI agent workflows and content automation systems.

After implementing and validating trending global AI technologies and workflows firsthand, I teach them so that even non-developers can reproduce the same setup on their own computers in just 10 minutes by sharing the execution logs, prompt specifications, and open-source starter kits as complete files. Instead of making exaggerated promises about views or revenue, I show you with data and code what actually worked and how it was validated.

[Key Research and Course Areas]

  • Building AI Agents & a Second Brain: Knowledge compilation and workflow automation based on Claude Code · Google Antigravity · Karpathy LLM Wiki

  • AI video and content production systems: Fact-checking and automated rendering pipelines combining Google Flow (Veo), TTS, and agents

  • Integrity verification automation: A lint pipeline that comprehensively checks broken links and factual accuracy across scripts, wikis, and data

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