SkillAtlasSkill 详情

analyzing-nft-rarity

A model-agnostic agent-skills platform.

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项目 README

来源文件:README.md

抓取于 2026年8月26日

Tons of Skills

A model-agnostic agent-skills platform. The canonical layer is harness-free by construction; Claude Code is currently the verified-native harness. Other harnesses remain engineering candidates until their native-path integration is verified; source research alone is never presented as public support.

Release CLI Plugins Skills GitHub Stars skills.sh Sponsor: Kobiton Buy me a monster

ko-fi

Install

Inside Claude Code, one command installs the whole marketplace:

/plugin marketplace add jeremylongshore/claude-code-plugins

Or use the CLI:

pnpm add -g @intentsolutionsio/ccpi
ccpi install devops-automation-pack

Browse the marketplace · Explore plugins · Download bundles

Killer Skill of the Week — no-ai-slop by Peter Yang

Strip AI slop from any draft — named-pattern edits that keep the writer's real voice

no-ai-slop does two jobs and refuses to fake a third. In Edit mode it makes the minimum effective edit — cutting throat-clearing, weak verbs, and abstract nouns while deliberately preserving the writer's cadence, bluntness, humor, and honest admissions, so a rough draft still sounds like the same person afterward. In Detect mode it names each AI-slop pattern it finds, quotes the offending line, and gives the fix in a few words — and pointedly does NOT score the draft or guess whether an AI wrote it. That restraint is the whole point: AI detectors guess; named patterns are evidence the reader can check. MIT-licensed, single focused skill, actively maintained by Peter Yang.

"AI detectors guess. Named patterns are evidence the user can check." — Peter Yang

Grade: A | Week of July 22, 2026 (W30) | View on GitHub

Previous picks: tonone, mnemos, databricks-pack, kobiton-automate, skyvern, code-cleanup, web-analytics, token-optimizer, executive-assistant-skills, skill-creator, cursor-pack, crypto-portfolio-tracker. See all at tonsofskills.com.

Scale, labeled

Every number below names the cohort it counts and the command that reproduces it — an unlabeled count is how a corpus ends up with five contradictory answers to "how many skills."

CountCohortReproduce with
442catalog plugins (catalog-entry cohort)node scripts/generate-readme-toc.mjs over marketplace.extended.json
3,067marketplace-visible skills (distinct)node -e "import('./scripts/corpus-resolver.mjs').then(m=>console.log(m.resolveCorpus('marketplace-visible').length))"
347agent definitions in pluginsgit ls-files 'plugins/**' | grep '/agents/.*\.md'
19plugin categoriesls -d plugins/*/

📦 Live npm Downloads

Across 396 published packages in the claude-code-plugins namespace. Updated daily by GitHub Actions.

WindowAll packagesEstablished (>30d)
Last 24 hours962962
Last 7 days2,9202,916
Last 30 days12,86812,779

"Established" excludes packages first published within the last 30 days, so a bulk-publish event doesn't dominate the headline.

Top 10 by last 30 days:

#PackageLast 30d
1@intentsolutionsio/openrouter-pack556
2@intentsolutionsio/groq-pack496
3@intentsolutionsio/databricks-pack274
4@intentsolutionsio/clickhouse-pack273
5@intentsolutionsio/wallet-security-auditor263
6@intentsolutionsio/notion-pack258
7@intentsolutionsio/elevenlabs-pack244
8@intentsolutionsio/freshie-inventory-manager214
9@intentsolutionsio/supabase-pack210
10@intentsolutionsio/agency-os204

Last refreshed 2026-08-19T03:03:05.709Z.

Ways in

Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:

Browse by category

The 19 categories below link into the live marketplace. Plugin counts are the catalog-entry cohort — regenerated from marketplace.extended.json by this generator; the catalog itself lives on tonsofskills.com, never in this file (§ 6A of the platform blueprint).

CategoryPlugins
🤖AI & Machine Learning36
🎭AI Agents & Agency10
🔌API Development26
💼Business Tools6
👥Community21
₿Crypto & Web327
💾Database26
🎨Design2
🔧DevOps & Infrastructure36
📚Examples & Templates5
🧩MCP Servers16
📦Packages5
⚡Performance25
✅Productivity30
🎁SaaS Skill Packs106
🔐Security27
✨Skill Enhancers9
🧪Testing28
📁Analytics1

What the classes mean

Four artifact classes live in this repository, distinguished on sight and never blurred — provenance is a truth requirement here, not a UX nicety:

ClassWhat it isHow the reader can tell
Canonical skillFirst-party, harness-free, the source of truthNo .source.json in its plugin directory
Generated adapterA thin, machine-produced harness projectionLives under a generated path with a "generated — do not edit" header
First-party packageAn Intent Solutions distribution (npm, cowork zip)@intentsolutionsio scope, IS-authored license
Upstream mirrorSomebody else's work, hosted mirror-by-default.source.json present — upstream author, license, and pinned commit recorded

Certification

Not yet certified. The certification program (tiers T0–T4 with retained, hash-matched evidence) is a later epic of the platform blueprint; until its report exists, no artifact on this surface claims a tier. This line is rendered from the absence of certification-report.json — honestly, not cosmetically.

Contribute

Start with the contribution guide, then the intake and review standards every submission passes through:

Governance

Provenance

External plugins are hosted mirror-by-default: the contributor's repository stays the source of truth, every mirrored source is pinned in a content lockfile, and upstream credit — author, license, resolved commit — is recorded in the mirror itself. Improvements flow by upstreaming to the author's repository, never by silently editing the mirror. The full decision record is the external-sync model.

License

MIT for the repository scaffolding and first-party tooling; each plugin carries its own license in its manifest, and mirrored plugins keep their upstream license verbatim.

其他

中风险

  • 来源需自行核对维护者身份。
  • 包含脚本或命令调用,安装前请复核。
  • 可能需要外部 token、网络权限或第三方服务。
  • 未检测到高风险命令。
  • 扫描发现:3 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/analyzing-nft-rarity" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/analyzing-nft-rarity" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/analyzing-nft-rarity" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/analyzing-nft-rarity" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/analyzing-nft-rarity" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
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 <jeremy@intentsolutions.io>
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

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

cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py token pudgypenguins 1234  # port 1234 - example/test

3. Compare Multiple Tokens

cd ${CLAUDE_SKILL_DIR}/scripts && python3 rarity_analyzer.py compare azuki 1234,5678,9012  # 5678: 1234: 9012 = configured value

4. View Trait Distribution

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

5. Export Rankings

JSON:

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

CSV:

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

6. Manage Cache

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

Rarity Algorithms

AlgorithmDescriptionBest For
rarity_scoreSum of 1/frequency (default)General use, matches rarity.tools
statisticalSame as rarity_scoreBackward compatibility
averageMean of trait raritiesBalanced scoring
informationEntropy-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

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