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Simplify

OrganizationPopular
QwenLM
simplify

Review recent code changes for reuse, code quality, and efficiency, then directly apply straightforward cleanup improvements. Use when the user wants a post-implementation cleanup pass, pre-PR polish, or asks to simplify/refine recent changes. Invoke with `/simplify` or `/simplify <focus>`.

Overview

PublisherQwenLM
Repositoryqwen-code
Skill namesimplify
Stars
27.9K
Forks
3.1K
Bundled files
Instructions only
LicenseApache-2.0
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 QwenLM 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/QwenLM/qwen-code.git /tmp/qwen-code
mkdir -p .claude/skills
cp -r /tmp/qwen-code/packages/core/src/skills/bundled/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 Recent Changes

You are running a structured cleanup workflow over recent code changes. Your goal is not just to comment on the code, but to safely improve it.

Step 1: Identify the review scope

Determine which files and changes to review.

  1. First inspect the current git state.
  2. If there are staged changes, review against HEAD so both staged and unstaged tracked changes are included.
  3. Otherwise review the current uncommitted diff.
  4. If there is no git diff, fall back to git ls-files --modified --others --exclude-standard so the scope respects .gitignore (this keeps build output, node_modules, and other ignored paths out of the cleanup).
  5. If that is still empty, fall back to files edited in this conversation.
  6. If you still cannot identify a meaningful scope, stop and tell the user there are no recent changes to simplify.

Preferred commands:

  • git diff --name-only
  • git diff --staged --name-only
  • git diff HEAD --name-only
  • git diff
  • git diff HEAD
  • git status --short

Use git diff HEAD whenever staged changes exist. Otherwise use git diff.

Step 2: Launch three review passes in parallel

Use the agent tool and launch all review passes in a single response so they run concurrently. Set subagent_type: "general-purpose" and run_in_background: false on every call — each pass must return its findings inline. Do NOT fork them: never set subagent_type: "fork". A fork runs fire-and-forget and never returns its findings, so there would be nothing to aggregate in Step 3. Each pass must receive the same review scope and diff command. These passes are read-only: each one inspects and reports findings only and must not modify files — all edits happen later in Step 4.

Keep each review prompt short and focused. Do not paste the full diff into the prompt. Tell each pass to read the diff itself and inspect only files relevant to its findings.

Pass 1: Code Reuse Review

Look for opportunities to reduce duplication and reuse existing code:

  • existing utilities or helpers that should be reused
  • duplicated logic introduced in new code
  • inline logic that should delegate to an existing abstraction
  • ad-hoc helpers for string, path, env, parsing, or type checks when a project utility already exists

Pass 2: Code Quality Review

Look for maintainability issues:

  • copy-paste variants that should be unified
  • parameter sprawl or awkward APIs
  • redundant state or indirection
  • abstraction leaks
  • stringly-typed code that should be modeled more clearly
  • unnecessary nesting
  • unnecessary comments that explain what instead of why
  • naming or structure that does not match surrounding code

Pass 3: Efficiency Review

Look for wasteful work and unnecessary overhead:

  • repeated work that can be memoized, cached, or removed
  • serial work that can be parallelized safely
  • unnecessary scans, allocations, reads, or traversals
  • hot-path blocking work
  • redundant no-op updates
  • overly broad operations when a narrower one would work
  • existence-check patterns that introduce TOCTOU style waste or risk

Step 3: Aggregate findings

Wait for all three passes to finish, then merge overlapping findings.

Prioritize fixes that are:

  • low risk
  • local in scope
  • clearly aligned with existing project patterns
  • easy to validate with tests or targeted commands

Do not force a cleanup if it would require speculative architectural changes.

Step 4: Apply straightforward improvements

Directly implement safe cleanup improvements.

Examples of good automatic fixes:

  • replace duplicated logic with an existing helper
  • remove redundant code, but only after a repository-wide search confirms it has no remaining callers
  • simplify conditionals or control flow
  • tighten loops or repeated work
  • reduce unnecessary state or wrapper code
  • remove low-value comments
  • align code with nearby conventions

Skip items that are uncertain, risky, or too invasive. Do not spend time debating rejected findings; simply move on.

Step 5: Verify the cleanup

After making changes:

  1. Run focused tests for the changed area when they exist.
  2. Run the relevant project quality checks you can identify for the touched code.
  3. If there are no applicable tests, at least run a targeted build, typecheck, or lint command that covers the edited files.

Prefer targeted verification over whole-repo commands unless the project only exposes repo-wide checks.

Additional focus

If the user supplied extra instructions after /simplify, treat them as additional review focus and prioritize them alongside the default dimensions.

The raw user invocation appears below when present. Use it to extract any extra focus such as performance, duplication, rendering, API clarity, testability, or naming consistency.

Frequently asked questions

What does the Simplify AI skill do?

Review recent code changes for reuse, code quality, and efficiency, then directly apply straightforward cleanup improvements. Use when the user wants a post-implementation cleanup pass, pre-PR polish, or asks to simplify/refine recent changes. Invoke with `/simplify` or `/simplify <focus>`.

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/QwenLM/qwen-code/tree/main/packages/core/src/skills/bundled/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?

Yes. It is published on GitHub by QwenLM under the Apache-2.0 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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