Caveman Skill

Caveman Skill

'Why use many token when few do trick.' A skill that cuts a coding agent's output tokens by more than half. An Adobe Research paper measured 1.4–3x cost savings; it hit #1 on GitHub Trending and Hacker News.

Use for Claude Code skill, saving tokens

Tools Claude Code · Codex · Cursor · ChatGPT

Prompt

Respond terse like smart caveman. All technical substance stay. Only fluff die.

Rules:
- Drop articles (a/an/the), filler (just/really/basically/actually/simply), pleasantries (sure/certainly/of course/happy to), hedging. Fragments OK.
- Short synonyms: big not extensive, fix not "implement a solution for".
- No tool-call narration, no decorative tables or emoji. Quote only the shortest decisive line of an error.
- Never invent abbreviations (cfg/impl/req): tokenizer splits them same as full word, zero saved, harder to read.
- Never drop not/never/no/only/except. Numbers and units exact. Code blocks unchanged.
- Never ADD words to sound caveman. Compression only.
- One idea per sentence, max 20 words, active voice.

Default style for whole session until user says "stop caveman" or "normal mode".

Tips

Install as SKILL.md and toggle with /caveman.
Code blocks and error text stay intact, so debugging isn't hurt.

Sources

License: MIT (원 저장소). SKILL.md 핵심 규칙 발췌, 출처 표기.

Prompt Dexbuilderlog.net · PROMPT DEX · FALL 2026 · 46 PROMPTS

We hunt the AI prompts people brag about on X, Instagram, TikTok, YouTube, Reddit and GitHub, and file them by what you want to make. Only prompts with public results or ones we ran ourselves, each with a rating and token estimate. This English edition carries the most-copied entries; the Korean dex has all 400+.

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📡 Live inbox

Scraped daily from GitHub, prompt collections and Hacker News. Unverified until a human reviews it

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★ Ratings

5: almost never fails, usable as-is
4: one or two regenerations
3: fun but inconsistent or weakly grounded

🔋 Tokens

Input: estimated at 1.3 Korean chars or 4 Latin chars per token
Text prompts use measured counts from a local Qwen2.5-14B run
One image ≈ 1,290 tokens on Gemini image models
Video and music bill per second, not per token

✅ Verification

Public result: several real outputs published by outlets or creators
Tested locally: text and settings prompts run on a local model to confirm format and intent
Translated prompts keep the original text under "Original text"

⚖️ Licensing and takedowns

Verbatim prompts come only from sources that permit redistribution (CC0, MIT, CC BY 4.0, official docs, public posts); each entry names its license and creator
Prompts reported by commercial outlets were rewritten by the editors with the same function; the source link is a reference, not a quotation
Result images are the creators' published outputs under their licenses; images showing identifiable real people are not included. Trademarks and characters appear for illustration and belong to their owners
Rights holders can request removal via Contact; we take it down on confirmation
Entries marked "written by Builderlog" are © Builderlog. Personal, non-commercial use in your own agent only. No commercial use and no copying, reposting or redistributing the text in whole or in part (blogs, social media, courses and paid materials included). Share by linking to the entry page. A license stated on the entry takes precedence

📡 Inbox

scripts/collect.py pulls GitHub search, collection READMEs and Hacker News into inbox.json (no LLM)
Runs daily; first-seen items are marked NEW
Inbox items are unverified until promoted into the dex