Tidy logo

Tidy

Community
mblode
tidy

Applies diff-scoped simplifications using reuse, ownership, efficiency, and test-value checks, including actionable review findings. Use when asked to "tidy this", "simplify my diff", or "apply the review findings". For a read-only report use pr-reviewer; for repository architecture use codebase-architecture.

Overview

Publishermblode
Repositoryagent-skills
Skill nametidy
Stars
118
Forks
11
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Tidy 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/mblode/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/tidy .claude/skills/tidy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tidy 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 Tidy 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 Tidy 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.

Tidy

Apply simplifications inside the current diff and incorporate actionable review findings. A clean diff may need no edits. For a read-only report use pr-reviewer; wider architectural changes belong to codebase-architecture.

Workflow

  1. Record the staged and unstaged scope, or the requested branch range, plus existing edits. Use applicable project instructions and check results already available at this revision.
  2. Inspect the relevant angles below. Reuse prior findings; do not repeat a full review simply because a different command produced it. Work locally by default. Delegation is useful only for substantial independent scopes supported by the host.
  3. Merge findings by root cause, discard false positives, and apply the smallest complete fixes. Correctness fixes precede simplification; ownership fixes precede polishing code they remove.
  4. Run checks affected by the changes, plus repository-required gates. Preserve exit codes and distinguish baseline failures. Report applied changes, material deferred decisions, and check results.

Review angles

AngleDistinctive questionEvidence for a change
ReuseDoes this code need to exist, or does a live repository helper, stdlib, platform feature, or installed dependency cover it?Existing contract and call site, including the boundary cases it handles
QualityDoes this introduce a second owner of state, an unused extension point, or unnecessary compatibility?Actual consumers and state ownership, not line count alone
EfficiencyDoes this add repeated work on a real hot path?Call frequency, duplicate reads, unbounded retention, or a defeated no-change signal
OwnershipIs a caller patch compensating for a shared mechanism, or placed outside the subsystem that owns this behavior?Writers, callers, and adjacent implementations; the deeper fix must be smaller than the special case
Test valueCan the test fail for a reason someone would act on?A named regression, reachable branch, or public contract; literal diff mirrors and mock echoes add no assurance

Constraints that earn their place

  • Guard deletion requires system evidence. Before removing a fallback, retry, lock, or validation, identify the writers, reachable states, staleness tolerance, and recovery owner. A guard that looks redundant locally can protect another caller. If its state cannot be ruled out, retain it and report the uncertainty.
  • Prior reviews are input, not authority. Apply supported findings. If current evidence refutes a prior finding, explain why it was not applied; do not blindly implement it because the report called it confirmed.
  • No whole-file rollback of unrelated edits. Scope formatters. If a formatter causes churn, remove only changes introduced by this run; git restore <path> can discard the user's earlier edits in the same file.
  • No abstraction quota. Fewer lines is not a win if it hides different lifecycles or drops behavior. An existing owning subsystem is stronger evidence than a preferred generic pattern.
  • Tests follow risk. Add or update a regression check when the edit changes behavior that can independently regress. Do not require tests for copy, a literal config change, or framework behavior already covered elsewhere.
  • Stop on evidence, not ceremony. Once affected checks pass, repeat only for new changes, failures, or unresolved concerns. A second pass that keeps adding guards to the same spot calls for revisiting the mechanism.

Output

Summarize what changed and why, checks and their results, and any substantial fix requiring a broader scope. Omit empty sections and "no findings" entries for every angle. Leave commits and PR creation to the user's request or pr-creator.

Maintenance only: evals/evals.json contains regression scenarios for changes to this skill; it does not load during a user task.

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 Tidy AI skill do?

Applies diff-scoped simplifications using reuse, ownership, efficiency, and test-value checks, including actionable review findings. Use when asked to "tidy this", "simplify my diff", or "apply the review findings". For a read-only report use pr-reviewer; for repository architecture use codebase-architecture.

Why use Tidy on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mblode/agent-skills/tree/main/skills/tidy. 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 Tidy?

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 Tidy?

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

Is the Tidy AI skill free?

Yes. It is published on GitHub by mblode 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 👇