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Simplify

Organization
Factory-AI
simplify

Review changed code for reuse, quality, and efficiency, then fix any issues found.

Overview

PublisherFactory-AI
Repositoryfactory-plugins
Skill namesimplify
Stars
111
Forks
15
Bundled files
Instructions only
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 Factory-AI on GitHub. Read the source before you install it.

Installation

Install the Simplify 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/Factory-AI/factory-plugins.git /tmp/factory-plugins
mkdir -p .claude/skills
cp -r /tmp/factory-plugins/plugins/core/skills/simplify .claude/skills/simplify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Simplify 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 Simplify 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 Simplify 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.

Simplify: Code Review and Cleanup

Review all changed files for reuse, quality, and efficiency. Fix any issues found.

Phase 1: Identify Changes

Run git diff (or git diff HEAD if there are staged changes) to see what changed. If there are no git changes, review the most recently modified files that the user mentioned or that you edited earlier in this conversation.

Phase 2: Launch Three Review Agents in Parallel

Use the Task tool to launch all three agents concurrently in a single message. Pass each agent the full diff so it has the complete context.

Agent 1: Code Reuse Review

For each change:

  1. Search for existing utilities and helpers that could replace newly written code. Look for similar patterns elsewhere in the codebase — common locations are utility directories, shared modules, and files adjacent to the changed ones.
  2. Flag any new function that duplicates existing functionality. Suggest the existing function to use instead.
  3. Flag any inline logic that could use an existing utility — hand-rolled string manipulation, manual path handling, custom environment checks, ad-hoc type guards, and similar patterns are common candidates.

Agent 2: Code Quality Review

Review the same changes for hacky patterns:

  1. Redundant state: state that duplicates existing state, cached values that could be derived, observers/effects that could be direct calls
  2. Parameter sprawl: adding new parameters to a function instead of generalizing or restructuring existing ones
  3. Copy-paste with slight variation: near-duplicate code blocks that should be unified with a shared abstraction
  4. Leaky abstractions: exposing internal details that should be encapsulated, or breaking existing abstraction boundaries
  5. Stringly-typed code: using raw strings where constants, enums (string unions), or branded types already exist in the codebase
  6. Unnecessary JSX nesting: wrapper Boxes/elements that add no layout value — check if inner component props (flexShrink, alignItems, etc.) already provide the needed behavior

Agent 3: Efficiency Review

Review the same changes for efficiency:

  1. Unnecessary work: redundant computations, repeated file reads, duplicate network/API calls, N+1 patterns
  2. Missed concurrency: independent operations run sequentially when they could run in parallel
  3. Hot-path bloat: new blocking work added to startup or per-request/per-render hot paths
  4. Recurring no-op updates: state/store updates inside polling loops, intervals, or event handlers that fire unconditionally — add a change-detection guard so downstream consumers aren't notified when nothing changed. Also: if a wrapper function takes an updater/reducer callback, verify it honors same-reference returns (or whatever the "no change" signal is) — otherwise callers' early-return no-ops are silently defeated
  5. Unnecessary existence checks: pre-checking file/resource existence before operating (TOCTOU anti-pattern) — operate directly and handle the error
  6. Memory: unbounded data structures, missing cleanup, event listener leaks
  7. Overly broad operations: reading entire files when only a portion is needed, loading all items when filtering for one

Phase 3: Fix Issues

Wait for all three agents to complete. Aggregate their findings and fix each issue directly. If a finding is a false positive or not worth addressing, note it and move on — do not argue with the finding, just skip it.

When done, briefly summarize what was fixed (or confirm the code was already clean).

Frequently asked questions

What does the Simplify AI skill do?

Review changed code for reuse, quality, and efficiency, then fix any issues found.

Why use Simplify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Factory-AI/factory-plugins/tree/master/plugins/core/skills/simplify. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Simplify?

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 Simplify?

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

Is the Simplify AI skill free?

It is published on GitHub by Factory-AI. 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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