Pull Request logo

Pull Request

OrganizationPopular
EpicenterHQ
pull-request

Draft and review Epicenter pull request titles and bodies, including changelog and merge details. Use when creating a PR or editing its text, not for local commits, branches, or issue replies.

Overview

PublisherEpicenterHQ
Repositoryepicenter
Skill namepull-request
Stars
4.8K
Forks
386
Bundled files
5
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.

  • 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 EpicenterHQ on GitHub. Read the source before you install it.

Installation

Install the Pull Request 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.
    https://github.com/EpicenterHQ/epicenter/tree/main/.agents/skills/pull-request
  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/EpicenterHQ/epicenter.git /tmp/epicenter
mkdir -p .claude/skills
cp -r /tmp/epicenter/.agents/skills/pull-request .claude/skills/pull-request
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pull Request 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 Pull Request 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 Pull Request 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.

Pull Request Guidelines

If the task is only staging, splitting, or committing local changes, use git. If the task is issue triage or public issue replies, use github-issues. The writing-voice rules govern the prose in any body you write.

Explain the change

A pull request body explains why the change should exist and why it takes this shape. Briefly state what changed so the reader can place the explanation, then develop the reasoning the diff cannot supply: what prompted the change, why this approach fits, and which consequences or tradeoffs matter.

Build a readable rhythm of explanation and concrete things the reader can inspect. Use code to show behavior, diagrams to expose relationships, and tables to make comparisons visible. Let these carry part of the explanation so the reader has less to hold in their head. Give each visual enough context to read it, then explain what follows from it.

Let the change determine the body’s shape and length. A small fix may need two sentences. An ownership change may need a diagram and its rationale. A breaking API change needs old and new usage with migration guidance. End when the reader has enough context to understand and judge the change.

Ground the explanation

Read the final diff and affected callers before drafting. Identify the main change and the reason for it; check examples against the implementation. Distinguish demonstrated behavior from intended benefits, and do not invent a rationale when the evidence leaves it unresolved.

Write for someone who has not followed the conversation. Keep the explanation useful after merge. Rewrite the title and body when the final scope changes, and include history only when it explains a decision that still matters.

Read the draft as that reader: can they tell what changed, why it was needed, and why this approach fits? Does each example help them judge a claim? Cut repetition of the diff and of what the visuals already show. Use the references for examples when useful; no body shape or sequence is mandatory.

Hard Rules

  • Do not include ## Summary, ## Changes, ## Testing, ## Test Plan, or ## Verification sections unless the user explicitly asks.
  • Report commands run, tests run, and verification gaps in the chat final response, not the PR body.
  • Do not list changed files. The diff tab already does that.
  • Do not include AI or tool attribution.
  • PR titles use the same conventional commit format as commits.
  • Include code examples for public API, CLI, HTTP, config, or type-signature changes.
  • Name breaking changes with old and new examples.
  • Add a ## Changelog section only for feat: and fix: PRs with user-visible changes.

References

Load these on demand:

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 Pull Request AI skill do?

Draft and review Epicenter pull request titles and bodies, including changelog and merge details. Use when creating a PR or editing its text, not for local commits, branches, or issue replies.

Why use Pull Request on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/EpicenterHQ/epicenter/tree/main/.agents/skills/pull-request. 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 Pull Request?

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 Pull Request?

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

Is the Pull Request AI skill free?

It is published on GitHub by EpicenterHQ. 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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