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Port Github App To Origin

OrganizationPopular
cursor
port-github-app-to-origin

Plans the port of an existing GitHub App to a Cursor Origin App. Use when the task is to bring a GitHub App to Origin or compare what it uses against the Origin API. Reads the app's needs out of its code, maps them onto the live Origin spec, and writes a porting brief with feedback for Cursor. Planning only.

Overview

Publishercursor
Repositoryplugins
Skill nameport-github-app-to-origin
Stars
10.9K
Forks
1.1K
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 cursor on GitHub. Read the source before you install it.

Installation

Install the Port Github App To Origin 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/cursor/plugins/tree/main/origin-apps/skills/port-github-app-to-origin
  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/cursor/plugins.git /tmp/plugins
mkdir -p .claude/skills
cp -r /tmp/plugins/origin-apps/skills/port-github-app-to-origin .claude/skills/port-github-app-to-origin
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Port Github App To Origin 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 Port Github App To Origin 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 Port Github App To Origin 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.

Port a GitHub App to an Origin App

Run this inside the app's codebase. The output is a porting brief for the team plus a Feedback for Cursor section they can send as is (references/brief.md). This skill plans. It does not write or change code unless the user asks for that after reading the brief. Follow the origin-api skill for the docs and the rules to check first. Two more rules:

  1. Find it in the code, do not ask. Read what the app uses out of its source. Anything you cannot find becomes a question for the team.
  2. Feedback describes use cases, not the team's code. The team's parts of the brief may cite file:line. Feedback for Cursor says only what the app needs to do and what Origin lacks for it, in Origin terms, with no file paths, module names, framework details, or repository names.

What to find

Record file:line for each item, and note what you looked for but did not find.

  • Declared permissions and events, from a manifest or infrastructure code if one is checked in. Otherwise derive them from the calls.
  • Every webhook event the app handles, and every payload field each handler reads, including fields it only logs.
  • Every REST and GraphQL call, with the parameters and filters the code passes, the response fields it reads, whether it runs on every webhook or in a loop, and how it paginates.
  • Authentication: the JWT algorithm, how the app learns the installation ID after install, how it handles token expiry, any user sign-in and what it is for, and whether the app clones or pushes git.
  • The webhook receiver: how it verifies signatures, whether it has the raw body when it verifies, and how it deduplicates deliveries.
  • Calls a framework or library makes for the app. Probot's receiver, token cache, and config loader; Octokit App's installation and repository listing; app-auth libraries. Read the dependency's docs and list these calls marked "from <dependency>".

How to map

A brief needs the whole spec, but openapi.yaml and llms-full.txt are each several hundred kilobytes, too large to read into context whole. Save them locally if you can, in a temporary location outside the app's repository so nothing in the working tree is overwritten or left behind, then search them and read only the matching part:

  • x-origin-scopes: in openapi.yaml marks every operation with its scopes; the operationId sits a line above.
  • x-origin-webhook-events: in openapi.yaml marks every webhook payload schema with the event slugs that deliver it.
  • ### <Endpoint name> and ### <Payload type> headings in llms-full.txt start the section that spells out that endpoint's or payload's fields as dotted paths; read from the heading to the next ###.

For a single question later, start at llms.txt and fetch just that section.

  • A matching name is a candidate, not an answer. Read the operation's description, parameters, and response fields against what the code passes and reads. If a parameter or field the code depends on is missing, that is a workaround or a gap, not a match.
  • GitHub's pull request calls that live under /issues/{n}/… (comments, labels) live under the pull request endpoints on Origin. If the code uses them on real issues, see "Where GitHub features live on Origin" in references/brief.md.
  • Origin has no GraphQL. Break each query into REST calls and record the fan-out.
  • The scopes to request are the union of x-origin-scopes.scopes over the operations you named. Do not translate the GitHub manifest.
  • Each GitHub event and action pair maps to at most one slug in "Events". The action is part of the slug. A pair with no slug is not an event on Origin.
  • For each payload field the code reads, record whether it is in the payload, in the delivery envelope around the payload (event.type carries the action), needs an extra read (say which operation and how many calls per event), can be computed from other fields, or is missing. Payloads are snapshots. A field the REST resource has but the payload lacks needs an extra read.
  • A capability the docs never mention is not available today. Ask the team about it. A behavior the docs neither confirm nor deny gets a question plus a step in the end-to-end test that checks it. Do not assume it works the way it did on GitHub.

Before finishing

Every claim about Origin points at something in the saved docs. Every gap has a feedback entry that names its cost. The feedback reveals nothing about the team's internals. The summary names the native-or-mirror question.

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 Port Github App To Origin AI skill do?

Plans the port of an existing GitHub App to a Cursor Origin App. Use when the task is to bring a GitHub App to Origin or compare what it uses against the Origin API. Reads the app's needs out of its code, maps them onto the live Origin spec, and writes a porting brief with feedback for Cursor. Planning only.

Why use Port Github App To Origin on TypingMind?

Because you install it once and use it with any model. Port Github App To Origin 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 Port Github App To Origin in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cursor/plugins/tree/main/origin-apps/skills/port-github-app-to-origin. 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 Port Github App To Origin?

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 Port Github App To Origin?

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

Is the Port Github App To Origin AI skill free?

Yes. It is published on GitHub by cursor 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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