analyzing-nft-rarity

@jeremylongshore/analyzing-nft-rarity

jeremylongshore

1,004

123 forks

Updated 1/18/2026

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Calculate NFT rarity scores and rank tokens by trait uniqueness. Use when analyzing NFT collections, checking token rarity, or comparing NFTs. Trigger with phrases like "check NFT rarity", "analyze collection", "rank tokens", "compare NFTs".

Installation

$skills install @jeremylongshore/analyzing-nft-rarity

Claude Code

Cursor

Copilot

Codex

Antigravity

Details

Repositoryjeremylongshore/claude-code-plugins-plus-skills

Pathplugins/crypto/nft-rarity-analyzer/skills/analyzing-nft-rarity/SKILL.md

Branchmain

Scoped Name@jeremylongshore/analyzing-nft-rarity

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions

name: analyzing-nft-rarity description: | Calculate NFT rarity scores and rank tokens by trait uniqueness. Use when analyzing NFT collections, checking token rarity, or comparing NFTs. Trigger with phrases like "check NFT rarity", "analyze collection", "rank tokens", "compare NFTs". allowed-tools: Read, Bash(python3 *) version: 1.0.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT

Analyzing NFT Rarity

Overview

NFT rarity analysis skill that:

Fetches collection metadata from OpenSea API
Parses and normalizes trait attributes
Calculates rarity using multiple algorithms
Ranks tokens by composite rarity score
Exports data in JSON and CSV formats

Prerequisites

Python 3.8+ with requests library
Optional: OPENSEA_API_KEY for higher rate limits
Optional: ALCHEMY_API_KEY for direct metadata fetching

Instructions

1. Analyze a Collection

cd {baseDir}/scripts && python3 rarity_analyzer.py collection boredapeyachtclub

Options:

--limit 500: Fetch more tokens for analysis
--top 50: Show top 50 tokens
--traits: Include trait distribution
--rarest: Show rarest traits
--algorithm [statistical|rarity_score|average|information]

2. Check Specific Token

cd {baseDir}/scripts && python3 rarity_analyzer.py token pudgypenguins 1234

3. Compare Multiple Tokens

cd {baseDir}/scripts && python3 rarity_analyzer.py compare azuki 1234,5678,9012

4. View Trait Distribution

cd {baseDir}/scripts && python3 rarity_analyzer.py traits doodles

5. Export Rankings

JSON:

cd {baseDir}/scripts && python3 rarity_analyzer.py export coolcats > rankings.json

CSV:

cd {baseDir}/scripts && python3 rarity_analyzer.py export coolcats --format csv > rankings.csv

6. Manage Cache

cd {baseDir}/scripts && python3 rarity_analyzer.py cache --list
cd {baseDir}/scripts && python3 rarity_analyzer.py cache --clear

Rarity Algorithms

Algorithm	Description	Best For
`rarity_score`	Sum of 1/frequency (default)	General use, matches rarity.tools
`statistical`	Same as rarity_score	Backward compatibility
`average`	Mean of trait rarities	Balanced scoring
`information`	Entropy-based (-log2)	Information theory approach

Output

Collection Summary: Name, supply, trait types
Rankings: Tokens sorted by rarity score with percentile
Token Detail: Full trait breakdown with contribution
Comparison: Side-by-side trait comparison

Supported Collections

Works with any ERC-721/ERC-1155 collection that has:

OpenSea listing
Standard attributes array format
Accessible metadata

Error Handling

See {baseDir}/references/errors.md for:

API rate limiting
IPFS gateway issues
Collection not found
Token ID not found

Examples

See {baseDir}/references/examples.md for:

Collection analysis workflows
Token comparison
Export and caching
Algorithm comparison

Resources

OpenSea API - Metadata source
Rarity Tools - Reference rankings
IPFS - Decentralized metadata

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