Reducing Entropy logo

Reducing Entropy

OrganizationPopular
softaworks
reducing-entropy

Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias toward deletion.

Overview

Publishersoftaworks
Repositoryagent-toolkit
Skill namereducing-entropy
Stars
2.5K
Forks
226
Bundled files
5
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 5 bundled files

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

  • Open source

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

Installation

Install the Reducing Entropy 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/softaworks/agent-toolkit.git /tmp/agent-toolkit
mkdir -p .claude/skills
cp -r /tmp/agent-toolkit/skills/reducing-entropy .claude/skills/reducing-entropy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reducing Entropy 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 Reducing Entropy 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 Reducing Entropy 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.

Reducing Entropy

More code begets more code. Entropy accumulates. This skill biases toward the smallest possible codebase.

Core question: "What does the codebase look like after?"

Before You Begin

Load at least one mindset from references/

  1. List the files in the reference directory
  2. Read frontmatter descriptions to pick which applies
  3. Load at least one
  4. State which you loaded and its core principle

Do not proceed until you've done this.

The Goal

The goal is less total code in the final codebase - not less code to write right now.

  • Writing 50 lines that delete 200 lines = net win
  • Keeping 14 functions to avoid writing 2 = net loss
  • "No churn" is not a goal. Less code is the goal.

Measure the end state, not the effort.

Three Questions

1. What's the smallest codebase that solves this?

Not "what's the smallest change" - what's the smallest result.

  • Could this be 2 functions instead of 14?
  • Could this be 0 functions (delete the feature)?
  • What would we delete if we did this?

2. Does the proposed change result in less total code?

Count lines before and after. If after > before, reject it.

  • "Better organized" but more code = more entropy
  • "More flexible" but more code = more entropy
  • "Cleaner separation" but more code = more entropy

3. What can we delete?

Every change is an opportunity to delete. Ask:

  • What does this make obsolete?
  • What was only needed because of what we're replacing?
  • What's the maximum we could remove?

Red Flags

  • "Keep what exists" - Status quo bias. The question is total code, not churn.
  • "This adds flexibility" - Flexibility for what? YAGNI.
  • "Better separation of concerns" - More files/functions = more code. Separation isn't free.
  • "Type safety" - Worth how many lines? Sometimes runtime checks in less code wins.
  • "Easier to understand" - 14 things are not easier than 2 things.

When This Doesn't Apply

  • The codebase is already minimal for what it does
  • You're in a framework with strong conventions (don't fight it)
  • Regulatory/compliance requirements mandate certain structures

Reference Mindsets

See references/ for philosophical grounding.

To add new mindsets, see adding-reference-mindsets.md.

Bias toward deletion. Measure the end state.

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 Reducing Entropy AI skill do?

Manual-only skill for minimizing total codebase size. Only activate when explicitly requested by user. Measures success by final code amount, not effort. Bias toward deletion.

Why use Reducing Entropy on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/softaworks/agent-toolkit/tree/main/skills/reducing-entropy. 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 Reducing Entropy?

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 Reducing Entropy?

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

Is the Reducing Entropy AI skill free?

Yes. It is published on GitHub by softaworks 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.

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