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clone-farm-detector

Helps detect clone farming and reputation gaming in AI agent marketplaces. Identifies near-duplicate skills that wash IDs, batch-publish patterns, and artificial reputation inflation through coordinated uploads.

andyxinweiminicloud0 来源安装数☆ 0 仓库 Starsv1.0.0

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求,再添加到工作流。

SkillHub CLI

skillhub install andyxinweiminicloud/clone-farm-detector

打开来源仓库,查看说明和兼容环境后,按来源提供的命令或步骤安装。

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技能说明

40% of Marketplace Skills Are Clones — Detect Gene Farming Before It Erodes Trust

Helps identify coordinated clone campaigns that flood agent marketplaces with near-duplicate skills to game reputation systems.

Problem

Agent marketplaces rank skills by popularity, downloads, and publisher reputation. This creates an incentive to game the system: publish dozens of near-identical skills under different names, each citing the others, to artificially inflate metrics. The result? Genuine skills get buried under clones, search results become useless, and users can't distinguish real innovation from reputation farming. This is the AI equivalent of SEO spam — and most marketplaces have no defense against it.

What This Checks

This detector examines a set of marketplace skills for clone farming indicators:

  1. Content similarity — Compares Capsule source code and Gene summaries across skills. Near-identical content with trivially changed variable names, comments, or formatting suggests cloning
  2. Batch publish patterns — Multiple skills published by the same node within a short time window, especially with sequential or templated naming
  3. ID washing — Skills with different SHA-256 hashes but functionally identical code, achieved by injecting whitespace, comments, or no-op statements to bypass deduplication
  4. Cross-citation rings — Skills that reference each other in dependency chains without functional necessity, creating artificial trust graphs
  5. Metadata templating — Identical description structures, same emoji sets, copy-paste summaries with only the noun changed

How to Use

Input: Provide one of:

  • A list of Capsule/Gene JSON objects to compare
  • A publisher node ID to scan their published catalog
  • A marketplace search term to check top results for cloning

Output: A structured report containing:

  • Cluster groups of similar/identical skills
  • Similarity scores between flagged pairs
  • Publishing timeline analysis
  • Risk rat...

来源信息

Source
github
Repository
openclaw/skills
Path
skills/andyxinweiminicloud/clone-farm-detector
Version
1.0.0
Commit
1b7c6573d80036399df71632e865a68bd788d25d
分类来源
rules
状态
active
可见性
public

这些信息来自收录时的来源分析,可能滞后;使用前请核对最新来源文件。

来源内容

SKILL.md
# 40% of Marketplace Skills Are Clones — Detect Gene Farming Before It Erodes Trust

> Helps identify coordinated clone campaigns that flood agent marketplaces with near-duplicate skills to game reputation systems.

## Problem

Agent marketplaces rank skills by popularity, downloads, and publisher reputation. This creates an incentive to game the system: publish dozens of near-identical skills under different names, each citing the others, to artificially inflate metrics. The result? Genuine skills get buried under clones, search results become useless, and users can't distinguish real innovation from reputation farming. This is the AI equivalent of SEO spam — and most marketplaces have no defense against it.

## What This Checks

This detector examines a set of marketplace skills for clone farming indicators:

1. **Content similarity** — Compares Capsule source code and Gene summaries across skills. Near-identical content with trivially changed variable names, comments, or formatting suggests cloning
2. **Batch publish patterns** — Multiple skills published by the same node within a short time window, especially with sequential or templated naming
3. **ID washing** — Skills with different SHA-256 hashes but functionally identical code, achieved by injecting whitespace, comments, or no-op statements to bypass deduplication
4. **Cross-citation rings** — Skills that reference each other in dependency chains without functional necessity, creating artificial trust graphs
5. **Metadata templating** — Identical description structures, same emoji sets, copy-paste summaries with only the noun changed

## How to Use

**Input**: Provide one of:
- A list of Capsule/Gene JSON objects to compare
- A publisher node ID to scan their published catalog
- A marketplace search term to check top results for cloning

**Output**: A structured report containing:
- Cluster groups of similar/identical skills
- Similarity scores between flagged pairs
- Publishing timeline analysis
- Risk rat...

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