Binary Patcher

Community

ptn1411

binary-patcher

Expert methodology for binary patching and bypass development. Focuses on Assembly modification (NOP, JMP, JE/JNE inversion) using debuggers like x64dbg. Includes automated patching scripts for applying modifications to binary files.

Overview

Publisher	ptn1411
Repository	`skill`
Skill name	`binary-patcher`
Stars	275
Forks	133
Bundled files	4
Links	Skill source Repository Homepage

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.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ptn1411 on GitHub. Read the source before you install it.

Installation

Install the Binary Patcher AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ptn1411/skill/tree/main/binary-patcher
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.

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/ptn1411/skill.git /tmp/skill
mkdir -p .claude/skills
cp -r /tmp/skill/binary-patcher .claude/skills/binary-patcher

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Binary Patcher 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 Binary Patcher 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 Binary Patcher 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.

Binary Patcher Skill

Reverse engineer and patch binary applications to bypass license checks, enable premium features, or modify logic behavior.

Language rule: All skill instructions use English. Final summary presented to the user must be in Vietnamese.

Patching Methodology (The Laragon Strategy)

1. Identify the Branch (JE/JNE Bypass)

Most license checks involve a CALL to a validation function followed by a conditional jump (JE, JNE, JZ, JNZ).

Search: Use "String References" in x64dbg to find error messages like "License not valid".
Trace: Follow the code execution upwards to find the branch that jumps to that error.
Patch: Modify the branch to skip the error.

Condition	Goal	Patch Strategy
`JE <Error>`	Skip error	Change to `NOP` (No Operation)
`JNE <Success>`	Force success	Change to `JMP <Success>`
`CALL <Check>`	Force true	Patch `EAX`/`RAX` to 1 after call

2. Forced State (NOPing the "Kick-out")

If the app checks for "Commercial" or "Pro" status and kicks you to a different branch if failed:

Locate: Find the jump that "kicks out" the Pro status.
Action: Replace the jump with NOP (0x90). This ensures the code "falls through" to the Pro logic.

Step 1 — Finding the Offset

In x64dbg, when you find a patch, look at the File Offset (not just the Virtual Address).

RVA to File Offset: Right-click in x64dbg -> Copy -> File Offset.

Step 2 — Automated Patching

Once you have identified the offsets and bytes, use scripts/apply_patch.py to create a permanent cracked binary.

bash
# bash / WSL / Linux / macOS
python scripts/apply_patch.py target.exe \
  --patch <OFFSET> <ORIGINAL_HEX> <PATCH_HEX> \
  --out cracked.exe

powershell
# Windows PowerShell (use backtick for line continuation)
python scripts\apply_patch.py target.exe `
  --patch <OFFSET> <ORIGINAL_HEX> <PATCH_HEX> `
  --out cracked.exe

Example (based on Laragon guide):

bash
# Bypass Validation (JNE -> NOP) — bash
python scripts/apply_patch.py laragon.exe --patch 0x61C14 7505 9090 --out cracked.exe

powershell
# Bypass Validation (JNE -> NOP) — PowerShell
python scripts\apply_patch.py laragon.exe --patch 0x61C14 7505 9090 --out cracked.exe

Final Report to User (always in Vietnamese)


✅ Quy trình Patch Binary hoàn tất:

  📍 Địa chỉ tìm thấy  : <OFFSETS>
  🛠️ Lệnh đã vá       : <OPCODE_CHANGES> (e.g., JE -> NOP)
  📂 File đầu ra       : <CRACKED_BINARY>

▶  Hướng dẫn sử dụng:
   - Chạy file đã vá để kiểm tra tính năng Premium.
   - Nếu ứng dụng có cơ chế kiểm tra checksum, bạn cần vá thêm hàm xác thực file.

Anti-Patterns

❌ Don't	✅ Do
Patch without verification	Always check the "Original Hex" matches before applying a patch
Modify sizes	Ensure `PATCH_HEX` is the same byte length as `ORIGINAL_HEX` (use `90` for padding)
Forget the File Offset	x64dbg shows memory addresses (VA); you need file offsets for permanent patching

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 Binary Patcher AI skill do?

Why use Binary Patcher on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ptn1411/skill/tree/main/binary-patcher. 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 Binary Patcher?

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 Binary Patcher?

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

Is the Binary Patcher AI skill free?

It is published on GitHub by ptn1411. 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Binary Patcher Skill

Patching Methodology (The Laragon Strategy)

1. Identify the Branch (JE/JNE Bypass)

2. Forced State (NOPing the "Kick-out")

Step 1 — Finding the Offset

Step 2 — Automated Patching

Final Report to User (always in Vietnamese)

Anti-Patterns

Bundled files

Frequently asked questions

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