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https://github.com/wbnns/meng

Analyzes the security and potential risks of Ethereum tokens using the GoPlus Token Security API and Rug Pull Detection API
https://github.com/wbnns/meng

analysis base cryptocurrency due-diligence ethereum evm goplus op-stack risk security token tokenomics

Last synced: 23 days ago
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Analyzes the security and potential risks of Ethereum tokens using the GoPlus Token Security API and Rug Pull Detection API

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README 

        
# Meng



![Alt](https://repobeats.axiom.co/api/embed/42b94a1c432bc7812ecee4afdefcc26c5a51f6df.svg "Repobeats analytics image")



## Overview



Meng is a Python application that analyzes the security and potential risks of Ethereum tokens using the GoPlus Token Security API and Rug Pull Detection API. It generates a detailed report in Markdown format and stores the analysis data in an SQLite database. The results should not be considered financial/investment advice and are for informational purposes only. Users should perform their own due diligence and [DYOR](https://hackmd.io/@wbnns/dyor-do-your-own-research).



## Features



- Retrieves token security and rug pull risk data from the GoPlus API

- Generates a comprehensive Markdown report with the analysis results

- Saves the report to a file named after the token symbol and current date

- Stores the JSON data returned by each API, along with the contract address and the current date, in an SQLite database



## Prerequisites



- Python 3.x

- `goplus` library



## Installation



1. Clone the repository:

   `git clone https://github.com/your-username/meng.git`



2. Install the required dependencies:

   `pip install goplus`



## Usage



1. Run the script:

   `python main.py`



2. Enter the token contract address when prompted.



3. The script will analyze the token and generate a report. The report will be displayed in the console and saved as a Markdown file in the `reports` folder with the filename format `_.md`.



4. The JSON data returned by each API, along with the contract address and the current date, will be stored in an SQLite database named `token_data.db`.



## Report Format



The generated report includes the following sections:



- Token Analysis Report

- Date

- Report date

- Token Security Analysis

- Raw JSON data from the token security API

- Token Security Analysis Breakdown

- Open Source

- Proxy Contract

- Mint Function

- Owner Address

- Can Take Back Ownership

- Owner Can Change Balance

- Hidden Owner

- Selfdestruct

- External Call

- In DEX

- Buy Tax

- Sell Tax

- Can't Buy

- Can't Sell All

- Slippage Modifiable

- Honeypot

- Transfer Pausable

- Blacklist

- Whitelist

- Anti Whale

- Anti Whale Modifiable

- Trading Cooldown

- Personal Slippage Modifiable

- Token Name

- Token Symbol

- Holder Count

- Total Supply

- Creator Address

- Creator Balance

- Creator Percent

- LP Holder Count

- LP Total Supply

- Is True Token

- Is Airdrop Scam

- Is In Trust List

- Other Potential Risks

- Note

- Fake Token

- Rug Pull Security Analysis

- Raw JSON data from the rug pull security API

- Rug Pull Security Analysis Breakdown

- Owner Name

- Owner Address

- Owner Type

- Privilege Withdraw

- Cannot Withdraw

- Contract Verified

- Blacklist Function

- Contract Name

- Self-Destruct

- Potential Approval Abuse

- Proxy Contract



## Database Schema



The SQLite database `token_data.db` contains a table named `token_data` with the following schema:



- `id` (INTEGER): Primary key

- `contract_address` (TEXT): The token contract address

- `token_security_data` (TEXT): The JSON data returned by the token security API

- `rug_pull_data` (TEXT): The JSON data returned by the rug pull security API

- `date` (TEXT): The date when the analysis was performed



## License



This project is licensed under the [MIT License](LICENSE.md).