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Caveman Compress

CommunityPopular
JuliusBrussee
caveman-compress

Compress a memory file such as CLAUDE.md or a todo list into caveman format to save input tokens, keeping a readable backup. Trigger: /caveman-compress.

Overview

PublisherJuliusBrussee
Repositorycaveman
Skill namecaveman-compress
Stars
106.3K
Forks
6.2K
Bundled files
7
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by JuliusBrussee on GitHub. Read the source before you install it.

Installation

Install the Caveman Compress AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/JuliusBrussee/caveman.git /tmp/caveman
mkdir -p .claude/skills
cp -r /tmp/caveman/plugins/caveman/skills/caveman-compress .claude/skills/caveman-compress
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Caveman Compress in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Caveman Compress on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Caveman Compress is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Caveman Compress

Purpose

Compress natural language files (CLAUDE.md, todos, preferences) into caveman-speak to reduce input tokens. Compressed version overwrites original. Human-readable backup saved as <filename>.original.md, but NOT beside the source file — it lives in an out-of-tree data dir ($XDG_DATA_HOME/caveman-compress/backups/<parent-dir-name>/, or %LOCALAPPDATA%\caveman-compress\backups\<parent-dir-name>\ on Windows) so skill auto-loaders don't re-ingest it as a live file.

Trigger

/caveman-compress <filepath> or when user asks to compress a memory file.

Process

  1. The compression scripts live in scripts/ (adjacent to this SKILL.md). If the path is not immediately available, search for scripts/__main__.py next to this SKILL.md.

  2. From the directory containing this SKILL.md, run:

python3 -m scripts <absolute_filepath>

  1. The CLI will:
  • detect file type (no tokens)
  • call Claude to compress
  • validate output (no tokens)
  • if errors: cherry-pick fix with Claude (targeted fixes only, no recompression)
  • retry up to 2 times
  • if still failing after 2 retries: report error to user, leave original file untouched
  1. Return result to user

Compression Rules

Remove

  • Articles: a, an, the
  • Filler: just, really, basically, actually, simply, essentially, generally
  • Pleasantries: "sure", "certainly", "of course", "happy to", "I'd recommend"
  • Hedging: "it might be worth", "you could consider", "it would be good to"
  • Redundant phrasing: "in order to" → "to", "make sure to" → "ensure", "the reason is because" → "because"
  • Connective fluff: "however", "furthermore", "additionally", "in addition"

Preserve EXACTLY (never modify)

  • Code blocks (fenced ``` and indented)
  • Inline code (backtick content)
  • URLs and links (full URLs, markdown links)
  • File paths (/src/components/..., ./config.yaml)
  • Commands (npm install, git commit, docker build)
  • Technical terms (library names, API names, protocols, algorithms)
  • Proper nouns (project names, people, companies)
  • Dates, version numbers, numeric values
  • Environment variables ($HOME, NODE_ENV)

Preserve Structure

  • All markdown headings (keep exact heading text, compress body below)
  • Bullet point hierarchy (keep nesting level)
  • Numbered lists (keep numbering)
  • Tables (compress cell text, keep structure)
  • Frontmatter/YAML headers in markdown files

Compress

  • Use short synonyms: "big" not "extensive", "fix" not "implement a solution for", "use" not "utilize"
  • Fragments OK: "Run tests before commit" not "You should always run tests before committing"
  • Drop "you should", "make sure to", "remember to" — just state the action
  • Merge redundant bullets that say the same thing differently
  • Keep one example where multiple examples show the same pattern

CRITICAL RULE: Anything inside ... must be copied EXACTLY. Do not:

  • remove comments
  • remove spacing
  • reorder lines
  • shorten commands
  • simplify anything

Inline code (...) must be preserved EXACTLY. Do not modify anything inside backticks.

If file contains code blocks:

  • Treat code blocks as read-only regions
  • Only compress text outside them
  • Do not merge sections around code

Pattern

Original:

You should always make sure to run the test suite before pushing any changes to the main branch. This is important because it helps catch bugs early and prevents broken builds from being deployed to production.

Compressed:

Run tests before push to main. Catch bugs early, prevent broken prod deploys.

Original:

The application uses a microservices architecture with the following components. The API gateway handles all incoming requests and routes them to the appropriate service. The authentication service is responsible for managing user sessions and JWT tokens.

Compressed:

Microservices architecture. API gateway route all requests to services. Auth service manage user sessions + JWT tokens.

Boundaries

  • ONLY compress natural language files (.md, .txt, .typ, .typst, .tex, extensionless)
  • NEVER modify: .py, .js, .ts, .json, .yaml, .yml, .toml, .env, .lock, .css, .html, .xml, .sql, .sh
  • If file has mixed content (prose + code), compress ONLY the prose sections
  • If unsure whether something is code or prose, leave it unchanged
  • Original file is backed up as FILE.original.md before overwriting — in the out-of-tree backup data dir (see Purpose), not beside the source file
  • Never compress FILE.original.md (skip it)

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Caveman Compress AI skill do?

Compress a memory file such as CLAUDE.md or a todo list into caveman format to save input tokens, keeping a readable backup. Trigger: /caveman-compress.

Why use Caveman Compress on TypingMind?

Because you install it once and use it with any model. Caveman Compress is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Caveman Compress in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/JuliusBrussee/caveman/tree/main/plugins/caveman/skills/caveman-compress. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Caveman Compress?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Caveman Compress?

As many as you like. As long as a model supports skills, you can use Caveman Compress with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Caveman Compress AI skill free?

It is published on GitHub by JuliusBrussee. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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