How does the Token Security Analyzer detect honeypots?

It analyzes the last 100 transactions to identify suspicious buy/sell ratios and checks for extreme slippage differences that prevent users from selling their tokens.

Does this skill provide financial advice?

No, it provides technical analysis of on-chain data and identifies risk indicators to help with due diligence, but it never recommends specific investment actions.

Which blockchains does this skill support?

It supports Ethereum, BSC, Base, and other networks indexed by DexPaprika; the skill automatically validates the network and address format before performing analysis.

Can it detect a rug pull before it happens?

It identifies high-risk precursors like low liquidity velocity, high deployer concentration, and suspicious volume patterns, though it cannot predict future developer actions with absolute certainty.

Token Security Analyzer

Name: Token Security Analyzer
Author: coinpaprika

bycoinpaprika

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Security & Testing

Identifies blockchain token security risks, honeypots, and potential rug pulls by analyzing on-chain transaction patterns and liquidity.

About

The Token Security Analyzer skill provides real-time security assessments for Ethereum and other blockchain tokens by interrogating DexPaprika market data. It automatically triggers when users provide a contract address or express concern about a token's safety, performing deep-dive analysis into buy/sell ratios, slippage asymmetry, and liquidity concentration. By identifying suspicious patterns like wash trading or honeypot mechanics, it helps users perform due diligence and avoid malicious smart contracts through structured risk reports and clear security verdicts.

Key Features

Honeypot detection via buy/sell transaction ratio analysis
Structured risk reports with clear verdicts and confidence levels
Automated network normalization and capability validation
Market manipulation identification using volume-to-liquidity ratios
Rug pull risk assessment through liquidity concentration checks
2 GitHub stars

Use Cases

Monitoring liquidity movements and holder concentration for rug pull risks
Verifying the safety of a new token address found on social media or DEXs
Detecting honeypot scams where tokens can be bought but not sold

About

Key Features

Honeypot detection via buy/sell transaction ratio analysis
Structured risk reports with clear verdicts and confidence levels
Automated network normalization and capability validation
Market manipulation identification using volume-to-liquidity ratios
Rug pull risk assessment through liquidity concentration checks
2 GitHub stars

Use Cases

Monitoring liquidity movements and holder concentration for rug pull risks
Verifying the safety of a new token address found on social media or DEXs
Detecting honeypot scams where tokens can be bought but not sold