agentleFS
Sign inStart free
Lesile-YinLesile-Yin/agent-skills/skills/caveman/SKILL.md

caveman

Skill2 starsChanged 3 months agoMITView on GitHub

Compress verbose agent/human communication into dense "caveman" mode — strip hedging, restatement, politeness, and meta-commentary while preserving 100% of the technical meaning. Use when a message, transcript, or tool output is token-heavy, repeats itself, or will be re-fed to an agent; typically saves 65–75% of output tokens with zero information loss.

184 other projects use a version of this.

Adopted by

Most recently taken up by

Used with

Projects with it also use addskill, apply-firmware and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • ryanportfolioSkill

    addskill

    Create, import, update, or install repo or personal skills: runtime ownership, resources, discovery checks.

    Also in 6 projects that use this

  • ryanportfolioSkill

    apply-firmware

    Bring current folder (plain folder, existing repo, old or partial firmware copy) up to full Harness Firmware in place. Mirror external repo → adopt-repo.

    Also in 6 projects that use this

  • +10 more from ryanportfolio

The file

We split this skill into the rules it gives an agent

1 rule. 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • 0

    Common

    Most of the 121 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Do not also execute the task the text describes unless asked.

    line 65

“Common” is measured: kept by at least 6 in 10 of the 121 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Caveman

Turn padded text into its load-bearing skeleton. Caveman mode is not rudeness — it is lossless compression of intent. The goal: every surviving token changes what the reader/agent does next; every removed token was noise.

When to use

  • A chat transcript or status update restates the same point three ways.
  • A tool output or log dump is about to be pasted back into an agent context.
  • A PR description, commit message, or design doc is 80% framing and 20% content.
  • You are about to pay token cost to re-read something you already said.

Workflow

Step 1 — Identify load-bearing tokens

Mark, privately, every token an executor would need to act correctly: identifiers (paths, names, IDs, versions), numbers/units, constraints, negations, error strings, and deadlines. These are immutable.

Step 2 — Delete the noise classes

Remove, in order:

  1. Hedging — "perhaps", "maybe", "I think", "it seems", "just to clarify".
  2. Restatement — the second and third paraphrase of one idea.
  3. Politeness/scaffolding — "thank you", "I hope this helps", "as mentioned".
  4. Meta-commentary — "let me explain", "here is what I did", "to summarize".

Step 3 — Collapse to Subject–Verb–Object

Rewrite survivors as short declarative clauses. Prefer lists over prose. Drop articles where meaning survives ("the" before known entities). One clause per line re-tokenizes cheapest and reads fastest.

Step 4 — Verify losslessness

Re-read the compressed text against Step 1's list. If any load-bearing token is missing or its meaning changed, restore it. Then estimate the token delta and report it.

Anti-patterns (never do these)

  • Removing a qualifier that flips meaning ("not", "except", "only", "must").
  • Merging two distinct points into one clause — ambiguity is worse than length.
  • Dropping error messages or stack traces — they are load-bearing.
  • Inventing brevity by changing a number, path, or name.
  • Compressing a reply to a human who asked for tone — caveman is for context, not for how you talk to people.

Output contract

Return:

  1. The compressed text (dense, line-per-clause).
  2. A one-line token delta estimate (e.g. "≈ 720 → 190 tokens, −74%").
  3. A short list of the critical tokens you preserved (paths/IDs/numbers), so the user can confirm nothing load-bearing was dropped.

Do not also execute the task the text describes unless asked. See references/compression-rules.md for the noise-class table and worked examples.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free