Gptpro logo

Gptpro

Community
avenoxai
gptpro

Export a monorepo into review-ready zip bundles for a non-agentic frontier model (GPT Pro on the ChatGPT web UI, or any chat model with file upload) — node_modules-free, secret-free, split by subsystem so a model with no shell and a bounded context can navigate the codebase a part at a time. Use for packaging a repo for external LLM review, audit, or architecture feedback. Pairs with the `gptpro-handoff` workflow skill.

Overview

Publisheravenoxai
Repositoryavenoxskills
Skill namegptpro
Stars
105
Forks
6
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Gptpro 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/avenoxai/avenoxskills/tree/main/skills/gptpro
  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/avenoxai/avenoxskills.git /tmp/avenoxskills
mkdir -p .claude/skills
cp -r /tmp/avenoxskills/skills/gptpro .claude/skills/gptpro
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gptpro 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 Gptpro 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 Gptpro 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.

GPTPro Export

Package a monorepo into clean, logically-split zip bundles for review by a powerful non-agentic model — GPT Pro on the ChatGPT web UI, or any chat model you upload files to.

That model has no shell, no live filesystem, and a bounded context. So we split by subsystem and strip node_modules, build output, binary assets, and secrets — giving it something it can actually navigate instead of a 400MB tarball it will choke on.

Run it with no config

bash
PROJECT_ROOT=/path/to/repo ./scripts/export.sh

With no bundles.conf, the script autodetects: one bundle per workspace package (apps/*, packages/*, services/*, libs/*), a schema-only db bundle from whatever migrations directory exists, and a docs bundle. A single-package repo gets one src bundle instead of the workspace walk.

That gets you a usable split on the first run. A hand-written config gets you a good one — the model navigates by asking "which bundle would this live in", so a split that matches your architecture is worth writing.

Configure

Bundles are defined in bundles.conf, which overrides autodetect entirely. Copy the example and edit:

bash
cp bundles.example.conf bundles.conf

Each line declares one bundle:

bash
define_bundle <name> "<description>" <source>...

Sources are dir:<path> or file:<path>[:<subdir>], relative to the repo root:

bash
define_bundle core "domain logic — the most important bundle for architecture review" \
  dir:packages/core

define_bundle db "schema architecture only — no rows, no seed data" \
  dir:supabase/migrations \
  file:supabase/config.toml:supabase \
  file:docs/DB_PLAN.md

define_bundle docs "architecture and convention context" \
  dir:docs dir:adrs file:README.md file:AGENTS.md

Run

bash
./scripts/export.sh

Overrides:

bash
PROJECT_ROOT=/path/to/repo \
OUTPUT_DIR=~/Desktop/review-bundles \
BUNDLE_PREFIX=myproject \
./scripts/export.sh
VariableDefaultMeaning
PROJECT_ROOT$PWDrepo to export
OUTPUT_DIR./gptpro-bundleswhere zips land (upload folder)
BUNDLE_PREFIXrepo dir namezip name prefix → <prefix>-core.zip
BUNDLES_CONFbundles.conf next to the scriptbundle definitions; autodetect if absent
ALLOW_SECRETS01 continues past a hard secret-scan hit

What gets excluded

Every bundle drops: .git, node_modules, .next, .turbo, dist, build, out, coverage, .vercel, *.tsbuildinfo, .env, .env.*, *.log, .DS_Store, and binary assets (images, video, fonts).

Tests are kept on purpose — they encode contracts and are some of the most useful review material in the repo.

Secret scan

After staging and before zipping, every bundle is scanned. Two tiers, because a one-tier scan on a real repo is all false positives and you end up passing ALLOW_SECRETS=1 every run — which is the same as having no scan:

  • HARD — provider-issued credential shapes: private key headers, AKIA…, ghp_…, github_pat_…, xox…, AIza…, long sk-…/sk-ant-…, JWTs. A hit aborts the export, file and line printed. Override with ALLOW_SECRETS=1 only after reading every hit.
  • SOFT — generic secret = "…" / password: "…" assignments. Printed for your eyes, never fatal.

Hits under test/, __tests__/, fixtures/, *.test.*, or carrying an obvious placeholder marker (FAKE, EXAMPLE, xxxxxxxx, env(…)) are demoted to SOFT — a secret-handling test suite is supposed to contain credential-shaped strings, and aborting on those trains you to ignore the guard.

Notes

  • Split by subsystem, not by size. The model navigates by asking "which bundle would this live in" — a split that matches your architecture is worth more than evenly-sized zips.
  • Database bundles should be architectural, not data. Ship the schema map and forward-only migrations so the model can see how the database is constructed; never ship rows or seed data.
  • The output directory is wiped of prior *.zip on each run, so re-running gives a clean set with no stale bundles.
  • Keep one bundle for docs/ADRs/conventions. Models reason much better about a codebase when they can read why it's shaped the way it is.

Pairs with gptpro-handoff — the workflow skill for authoring the prompt, receiving the report, and verifying findings before implementing.

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 Gptpro AI skill do?

Export a monorepo into review-ready zip bundles for a non-agentic frontier model (GPT Pro on the ChatGPT web UI, or any chat model with file upload) — node_modules-free, secret-free, split by subsystem so a model with no shell and a bounded context can navigate the codebase a part at a time. Use for packaging a repo for external LLM review, audit, or architecture feedback. Pairs with the `gptpro-handoff` workflow skill.

Why use Gptpro on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/avenoxai/avenoxskills/tree/main/skills/gptpro. 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 Gptpro?

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 Gptpro?

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

Is the Gptpro AI skill free?

Yes. It is published on GitHub by avenoxai 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 👇