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caveman

Compress verbose agent/human communication into dense "caveman" mode — strip hedging, restatement, politeness, and meta-commentary while preserving 100% of…

Skill by Lesile-Yin

IndexedMIT★ 2
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Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.

npx agentmarket install skill/lesile-yin-caveman

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About

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.

Use cases: agent-building

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Permissions

What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.

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Network hosts it talks to 1

  • codeload.github.com · found in install command

Files it touches

  • ~/.claude/skills/caveman

What's inside

Skill name
caveman
Description
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.
License
MIT
Instructions
58 lines in SKILL.md · view on GitHub

Files (2)

  • SKILL.md
  • compression-rules.md · references/

SKILL.md (first lines)

# 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.

From Lesile-Yin/agent-skills, MIT.

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Last commit (this folder)
Aug 26, 2026 (2 months ago)

From GitHub (fetched Oct 24, 2026).

Details

License
MIT
First seen
Oct 24, 2026
Last checked
Oct 24, 2026

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Cite this page

caveman skill. Agent Mart. https://agentmart.dev/skills/lesile-yin-caveman (last updated 2026-10-24)