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Pr To Main

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breaking-brake
pr-to-main

Create a PR to the main branch for feature/fix changes in this pnpm + Changesets monorepo. Use when the user says "PRを作成", "mainにPR", or wants to submit changes for review. Always run this in the monorepo-aware way — identify the affected package(s) and make sure a changeset exists, because the release pipeline is Changesets-driven.

Overview

Publisherbreaking-brake
Repositorycc-wf-studio
Skill namepr-to-main
Stars
5.4K
Forks
568
Bundled files
3
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.

  • 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 breaking-brake on GitHub. Read the source before you install it.

Installation

Install the Pr To Main 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/breaking-brake/cc-wf-studio/tree/main/.claude/skills/pr-to-main
  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/breaking-brake/cc-wf-studio.git /tmp/cc-wf-studio
mkdir -p .claude/skills
cp -r /tmp/cc-wf-studio/.claude/skills/pr-to-main .claude/skills/pr-to-main
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pr To Main 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 Pr To Main 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 Pr To Main 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.

PR to Main Branch

Create a PR for a feature/fix branch targeting main in the cc-wf-studio monorepo.

This is a pnpm monorepo with four packages (packages/core, packages/mcp, packages/cli, packages/vscode) and versioning is driven by Changesets, not commit messages. Two monorepo facts shape this workflow:

  • A release-worthy change must ship with a .changeset/*.md file in the same PR. Without it, the change merges but never gets released — and pending changesets later block promotion to production.
  • Knowing which package(s) changed drives the changeset target, the PR scope, and the commit scope (e.g. fix(vscode):).

So the job here is not just "open a PR" — it's "open a PR that the release pipeline can actually act on."

Workflow

  1. Gather context (run in parallel):

    • git status (no -uall)
    • git diff (staged + unstaged) and git diff origin/main...HEAD for the full branch delta
    • git log origin/main..HEAD --oneline for every commit on the branch
    • Check whether the branch tracks a remote and needs pushing
    • ls .changeset/*.md to see whether a changeset already exists
  2. Identify scope and type:

    • Affected package(s): map changed paths to packages — packages/core → @cc-wf-studio/core, packages/mcp → @cc-wf-studio/mcp, packages/cli → @cc-wf-studio/cli, packages/vscode → cc-wf-studio (the VSCode extension). Changes outside packages/ (root config, .github/, docs) usually need no release.
    • Type: read the commit prefix, allowing an optional scope — fix: / fix(vscode): → fix, feat: / feat(core): → feat, improvement: → improvement. Review all commits on the branch, not just the latest.
  3. Ensure a changeset exists (the monorepo-critical step):

    • If a .changeset/*.md already covers this change, confirm it names the right package(s) and bump level, and move on.
    • If the change should be released and no changeset exists, create one with pnpm changeset — select the affected package(s) from step 2, choose patch/minor/major, and write a one-line summary (this becomes the CHANGELOG entry). Note: bumping @cc-wf-studio/core auto-bumps its dependents, so you don't author separate changesets for cli/mcp.
    • If the change genuinely needs no release (CI-only, tooling, docs), record that intent explicitly with pnpm changeset add --empty so the pipeline isn't left guessing.
    • Why this matters: releases are cut later by manually opening the Release PR (which consumes the accumulated changesets) and merging it — that merge auto-publishes. A PR that lands without the right changeset silently never gets released. (See docs/release-flow.md for the release side — releasing is a human-only action, not something to self-trigger.)
  4. Verify the build is green before opening the PR:

    • Run pnpm check && pnpm build from the repo root (Biome + type-checks, then full compilation). Reviewers and CI expect a clean tree, and catching breakage now is cheaper than after review.
  5. Select the PR template based on type (from step 2):

    • fix: → assets/fix-template.md
    • feat: → assets/feat-template.md
    • improvement: → assets/improvement-template.md
  6. Create the PR:

    • Push the branch with git push -u if it has no upstream.
    • Open it with gh pr create --base main, filling the chosen template.

PR Format

  • Language: always write the PR title and body in English (repo rule).
  • Title — main merges via squash, so the PR title becomes the squash commit subject. It must therefore follow the commit convention: <type>(<scope>): <description>.
    • <type>: matches the change type from step 2 — fix / feat / improvement / docs / refactor / chore / ci.
    • <scope>: the affected package/area from step 2 — vscode, cli, mcp, core (use the directory name, not the npm name). List several as (cli, mcp). Omit the scope for repo-wide changes (root config, .github/, top-level docs), e.g. ci: guard against pending changesets.
    • <description>: imperative mood, no trailing period, concise (aim ≤50 chars per the commit guideline; GitHub appends a (#NN) suffix, with a leading space, on squash — so don't pad it).
    • Examples (from this repo's history): fix(vscode): parse Changesets-format CHANGELOG, feat(cli): ccwf install-skills subcommand, docs: add root README for the monorepo.
  • Body: in the Changes section, write real monorepo paths (packages/vscode/src/..., packages/core/...), not generic placeholders.
  • Mention the changeset in the body so reviewers can see the intended release effect — e.g. "Adds a patch changeset for cc-wf-studio" or "No release needed (empty changeset)".

Templates

  • fix: assets/fix-template.md — Problem/Solution with Current vs Expected behavior.
  • feat: assets/feat-template.md — Summary/Motivation/Changes.
  • improvement: assets/improvement-template.md — Before/After comparison.

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 Pr To Main AI skill do?

Create a PR to the main branch for feature/fix changes in this pnpm + Changesets monorepo. Use when the user says "PRを作成", "mainにPR", or wants to submit changes for review. Always run this in the monorepo-aware way — identify the affected package(s) and make sure a changeset exists, because the release pipeline is Changesets-driven.

Why use Pr To Main on TypingMind?

Because you install it once and use it with any model. Pr To Main 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 Pr To Main in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/breaking-brake/cc-wf-studio/tree/main/.claude/skills/pr-to-main. 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 Pr To Main?

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 Pr To Main?

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

Is the Pr To Main AI skill free?

It is published on GitHub by breaking-brake. 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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