Debloat logo

Debloat

CommunityPopular
LilMGenius
debloat

Compress an artifact that has accreted into bloat — padding, over-qualification, fused sentences, walls of enumeration, adjacent restatement — down to its load-bearing density, meaning preserved. Use when prose is correct and current but has grown verbose or patched-over and you want it tight without a full rewrite.

Overview

PublisherLilMGenius
Repositorypaperthin
Skill namedebloat
Stars
1.3K
Forks
118
Bundled files
Instructions only
LicenseMIT
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Debloat 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.
    https://github.com/LilMGenius/paperthin/tree/main/skills/depth/debloat
  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/LilMGenius/paperthin.git /tmp/paperthin
mkdir -p .claude/skills
cp -r /tmp/paperthin/skills/depth/debloat .claude/skills/debloat
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debloat 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 Debloat 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 Debloat 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.

Cut a bloated artifact to its load-bearing minimum: same meaning, fewer words.

Goal

An artifact that is correct and current can still rot a second way: it accretes. Each edit bolts a qualifier onto the nearest sentence, a rule gets restated wherever it might apply, an enumeration grows into a wall — until a reader must study what they should skim. debloat compresses that bloat to its load-bearing density: every claim that carries weight survives, every word that does not is cut. It is not a rewrite (re0), a dedup (ssotize), or a tell-remover (dedash, detool) — it is a density pass on prose that is otherwise fine.

Workflow

  1. Pin the artifact and read it end to end; note in one line what each section must convey — the load-bearing content that has to survive.
  2. Find the bloat, not the content: padding that adds length but not meaning; a qualifier or parenthetical the sentence holds without; a fused sentence carrying three ideas; a wall of enumeration where a rule plus a short list would do; a point restated within reach of itself; litigation-history (why-we-decided-it) where the rule alone suffices.
  3. Compress in place: cut the padding, split the fused sentence or drop its dead clause, collapse the wall, keep a repeated point once. Move nothing to another artifact and re-derive nothing.
  4. Preserve every load-bearing claim — a rule, fact, constraint, or example that carries weight. If cutting a word would lose one, keep the word.
  5. When the bloat is really duplication across artifacts, or the content has drifted stale, stop and name it: hand duplication to ssotize and drift to re0. debloat only tightens.
  6. Re-read cold and cut again — the first pass always leaves some.

Rules

  • Cut words, never load-bearing claims. Every rule, fact, and constraint that was there is still there; only the density changed.
  • Preserve the artifact's voice and structure; compress within it, do not re-style it.
  • Not a rewrite, a dedup, or a tell-remover: re-derive nothing (re0), move nothing to another home (ssotize), and leave em-dashes and stack nouns to dedash/detool.
  • Respect intended richness: human-facing prose meant to teach or orient (a quickstart, a worked example) earns its length — compress the bloat, not the accessibility.
  • A pass that finds nothing genuinely bloated changes nothing.
  • Mutate with edit-safety: assert each target exists (report a MISS, never a silent no-op), edit unicode-safe (PYTHONUTF8=1), replace per occurrence, and script a large structural move.

Verification

Before finishing:

  1. Every load-bearing claim present before is present after — only words were cut.
  2. The result reads tighter to a cold reader, and reads as intended-terse, not amputated.
  3. Anything that was duplication or drift, not bloat, was handed to ssotize/re0, not force-compressed.

Frequently asked questions

What does the Debloat AI skill do?

Compress an artifact that has accreted into bloat — padding, over-qualification, fused sentences, walls of enumeration, adjacent restatement — down to its load-bearing density, meaning preserved. Use when prose is correct and current but has grown verbose or patched-over and you want it tight without a full rewrite.

Why use Debloat on TypingMind?

Because you install it once and use it with any model. Debloat 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 Debloat in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LilMGenius/paperthin/tree/main/skills/depth/debloat. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debloat?

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 Debloat?

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

Is the Debloat AI skill free?

Yes. It is published on GitHub by LilMGenius under the MIT license. 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇