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token-scorer

Score crypto tokens with an 11-rule engine using DexScreener and on-chain data.

5|2|Updated Feb 6, 2026
Search Tags:#liquidity analysis#dexscreener#defi risk#token scoring#volume sanity checks#hot warm cold#bd screening
One-click install
npx skills add https://github.com/buzzbysolcex/buzz-bd-agent --skill token-scorer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: token-scorer
Source: https://github.com/buzzbysolcex/buzz-bd-agent/tree/main/.claude/skills/token-scorer
Command: npx skills add https://github.com/buzzbysolcex/buzz-bd-agent --skill token-scorer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Token scoring for BD and safety checks is slow and expensive when it relies on ad-hoc research or LLM-heavy analysis, so teams need fast, consistent, rule-based screening to decide whether to proceed.

Core Features & Use Cases

  • Real-time rule-based token scoring: Applies an 11-rule engine to evaluate liquidity, volume, security signals, deployer identity, and market dynamics without LLM inference.
  • Actionable results and classifications: Produces a 0–100 score plus COLD/WARM/HOT bands and a rule-triggered breakdown for what to act on.
  • Multi-source data ingestion: Pulls market data from DexScreener API and on-chain data to compute sanity checks and flags such as FDV gap, ghost tokens, and suspicious volume/liquidity ratios.

Use Case: Before listing or interacting with a new token, run an automated screen to quickly filter out likely bad candidates, prioritize promising ones, and route HOT/WARM tokens into a monitoring or outreach pipeline.

Quick Start

Ask the AI to score a contract address and return the classification, score, and triggered rule list.

Frequently Asked Questions about token-scorer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I score crypto tokens without paying LLM inference costs?▼

You can score crypto tokens with zero LLM cost by applying a deterministic 11-rule engine to market and on-chain data. This evaluates liquidity, volume, and security signals to produce a 0–100 score and a COLD/WARM/HOT classification.

What is the best way to filter out bad DeFi token candidates before listing?▼

The best way to filter bad DeFi token candidates is automated rule-based token screening. By running an 11-rule engine on DexScreener and on-chain data, you can instantly flag ghost tokens, FDV gaps, and suspicious volume to prioritize promising assets.

How do I get a DeFi risk breakdown for a smart contract address?▼

To get a DeFi risk breakdown, input the contract address into the token scoring engine. It computes sanity checks on market dynamics and deployer identity, returning a structured rule-triggered breakdown detailing exactly which safety flags are active.

Does token scoring work with DexScreener data for BD pipeline prioritization?▼

Yes, token screening works directly with DexScreener API data for BD pipeline prioritization. It ingests real-time market information to compute volume and liquidity ratios, routing HOT or WARM tokens directly into your monitoring or outreach pipeline.

Can I use deterministic volume sanity checks instead of LLM-heavy analysis?▼

Yes, you can use deterministic volume sanity checks instead of LLM-heavy analysis. The 11-rule engine applies fixed logic to identify suspicious volume and liquidity ratios, delivering consistent screening results and cached outputs without AI inference.

What are the limitations of rule-based token scoring for screening?▼

A limitation of rule-based token scoring is that it relies strictly on deterministic logic from DexScreener and on-chain data. It serves as a fast pre-interaction filter to reduce manual BD research, but does not replace deep qualitative safety audits.

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