Analyzing Nft RarityCAUTION
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
Overview
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
4f83675ca38aOBSERVED · 2026-10-08Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- 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: 1.25.0 author: Jeremy Longshore <[email protected]> license: MIT tags: - crypto - analyzing-nft compatibility: Designed for Claude Code --- # 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 ```bash cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py collection boredapeyachtclub ``` Options: 1. `--limit 500`: Fetch more tokens for analysis 2. `--top 50`: Show top 50 tokens 3. `--traits`: Include trait distribution 4. `--rarest`: Show rarest traits 5. `--algorithm [statistical|rarity_score|average|information]` ### 2. Check Specific Token ```bash cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py token pudgypenguins 1234 # port 1234 - example/test ``` ### 3. Compare Multiple Tokens ```bash cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py compare azuki 1234,5678,9012 # 5678: 1234: 9012 = configured value ``` ### 4. View Trait Distribution ```bash cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py traits doodles ``` ### 5. Export Rankings JSON: ```bash cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py export coolcats > rankings.json ``` CSV: ```bash cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py export coolcats --format csv > rankings.csv ``` ### 6. Manage Cache ```bash cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py cache --list cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py cache --clear ``` ## Rarity Algorithms | Algorithm | Description | Best For | |-----------|-------------|----------| | `rarity_score` | Sum of 1/frequency (default) | General use, matches rarity.tools | | `statistical` | Same as rarity_score | Backward compatibility | | `average` | Mean of trait rarities | Balanced scoring | | `information` | Entropy-based (-log2) | Information theory approach | ## Output - **Collection Summary**: Name, supply, trait types - **Rankings**: Tokens sorted by rarity score with percentile - **Token Detail**: Full trait breakdown with contribution - **Comparison**: Side-by-side trait comparison ## Supported Collections Works with any ERC-721/ERC-1155 collection that has: - OpenSea listing - Standard attributes array format - Accessible metadata ## Error Handling See `${CLAUDE_SKILL_DIR}/references/errors.md` for: - API rate limiting - IPFS gateway issues - Collection not found - Token ID not found ## Examples See `${CLAUDE_SKILL_DIR}/references/examples.md` for: - Collection analysis workflows - Token comparison - Export and caching - Algorithm comparison ## Resources - [OpenSea API](https://docs.opensea.io/reference/api-overview) - Metadata source - [Rarity Tools](https://rarity.tools/) - Reference rankings - [IPFS](https://ipfs.io/) - Decentralized metadata
Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | WARN |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- declared (5 observation(s))
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (3)
print(f"\nSample Token: {token.name}")print(f"Attributes: {len(token.attributes)}")1. Use Read tool to load API credentials from ${CLAUDE_SKILL_DIR}/config/crypto-apis.envGates applied: no_behavioural_pass.
4f83675ca38afull audit observations/trust-audit/skill/jeremylongshore__analyzing-nft-rarity.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-08 | 4f83675ca38a | CAUTION | B | 89 | first audit |
Questions
What does the Analyzing Nft Rarity skill do?
Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com.
Is Analyzing Nft Rarity safe to install?
With care. The audit graded it B (89/100) and found 3 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can Analyzing Nft Rarity access on my machine?
The audit observed that it reaches the network. Each of those is consistent with what it says it does. Secrets in the source: none found.
Which assistants does Analyzing Nft Rarity work with?
Its documentation mentions claude-code. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (4f83675ca38a), read on 2026-10-08. The repository is watched, and a new audit runs when it changes — this is the first audit.