App Store Changelog logo

App Store Changelog

CommunityPopular
Dimillian
app-store-changelog

Create user-facing App Store release notes by collecting and summarizing all user-impacting changes since the last git tag (or a specified ref). Use when asked to generate a comprehensive release changelog, App Store "What's New" text, or release notes based on git history or tags.

Overview

PublisherDimillian
RepositorySkills
Skill nameapp-store-changelog
Stars
4K
Forks
206
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the App Store Changelog 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/Dimillian/Skills.git /tmp/Skills
mkdir -p .claude/skills
cp -r /tmp/Skills/app-store-changelog .claude/skills/app-store-changelog
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable App Store Changelog 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 App Store Changelog 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 App Store Changelog 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.

App Store Changelog

Overview

Generate a comprehensive, user-facing changelog from git history since the last tag, then translate commits into clear App Store release notes.

Workflow

1) Collect changes

  • Run scripts/collect_release_changes.sh from the repo root to gather commits and touched files.
  • If needed, pass a specific tag or ref: scripts/collect_release_changes.sh v1.2.3 HEAD.
  • If no tags exist, the script falls back to full history.

2) Triage for user impact

  • Scan commits and files to identify user-visible changes.
  • Group changes by theme (New, Improved, Fixed) and deduplicate overlaps.
  • Drop internal-only work (build scripts, refactors, dependency bumps, CI).

3) Draft App Store notes

  • Write short, benefit-focused bullets for each user-facing change.
  • Use clear verbs and plain language; avoid internal jargon.
  • Prefer 5 to 10 bullets unless the user requests a different length.

4) Validate

  • Ensure every bullet maps back to a real change in the range.
  • Check for duplicates and overly technical wording.
  • Ask for clarification if any change is ambiguous or possibly internal-only.

Commit-to-Bullet Examples

The following shows how raw commits are translated into App Store bullets:

Raw commit messageApp Store bullet
fix(auth): resolve token refresh race condition on iOS 17• Fixed a login issue that could leave some users unexpectedly signed out.
feat(search): add voice input to search bar• Search your library hands-free with the new voice input option.
perf(timeline): lazy-load images to reduce scroll jank• Scrolling through your timeline is now smoother and faster.

Internal-only commits that are dropped (no user impact):

  • chore: upgrade fastlane to 2.219
  • refactor(network): extract URLSession wrapper into module
  • ci: add nightly build job

Example Output

What's New in Version 3.4

• Search your library hands-free with the new voice input option.
• Scrolling through your timeline is now smoother and faster.
• Fixed a login issue that could leave some users unexpectedly signed out.
• Added dark-mode support to the settings screen.
• Improved load times when opening large photo albums.

Output Format

  • Title (optional): "What's New" or product name + version.
  • Bullet list only; one sentence per bullet.
  • Stick to storefront limits if the user provides one.

Resources

  • scripts/collect_release_changes.sh: Collect commits and touched files since last tag.
  • references/release-notes-guidelines.md: Language, filtering, and QA rules for App Store notes.

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 App Store Changelog AI skill do?

Create user-facing App Store release notes by collecting and summarizing all user-impacting changes since the last git tag (or a specified ref). Use when asked to generate a comprehensive release changelog, App Store "What's New" text, or release notes based on git history or tags.

Why use App Store Changelog on TypingMind?

Because you install it once and use it with any model. App Store Changelog 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 App Store Changelog in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dimillian/Skills/tree/main/app-store-changelog. 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 App Store Changelog?

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 App Store Changelog?

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

Is the App Store Changelog AI skill free?

Yes. It is published on GitHub by Dimillian 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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