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Clone Farm Detector

该 Skill 面向 AI 代理市场,通过内容相似度、批量发布时间、ID 洗白、交叉引用和元数据模板等线索,识别疑似克隆养殖与声誉操纵行为,输出相似技能聚类、相似度分数、发布时间线、风险评级和证据摘要。

andyxinweiminicloud
andyxinweiminicloud
热度 62.94
最近更新 2026-05-11 17:26:45
分类:安全依赖:代码执行 / 第三方 API应用场景:市场分析 / 研究 / 报告撰写

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add clone-farm-detector
Metadata
name
clone-farm-detector
description
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.
version
1.0.0
metadata
{"openclaw":{"requires":{"bins":["curl","python3"],"env":[]},"emoji":"🧬"}}

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 rating: CLEAN / SUSPECT / FARMING
  • Evidence summary for each cluster

Example

Input: Scan top 10 results for "code formatter" on marketplace

🧬 FARMING DETECTED — 2 clone clusters found

Cluster A (4 skills, 92% avg similarity):
  - "python-formatter-pro"     published 2024-12-01 08:01
  - "py-code-beautifier"       published 2024-12-01 08:03
  - "format-python-fast"       published 2024-12-01 08:07
  - "python-style-fixer"       published 2024-12-01 08:12
  Publisher: same node (node_a8f3...)
  Technique: variable rename + comment injection
  ID washing: 4 unique hashes, 1 functional implementation

Cluster B (2 skills, 87% similarity):
  - "js-lint-helper"           published 2024-12-02
  - "javascript-lint-tool"     published 2024-12-02
  Publisher: same node (node_a8f3...)
  Cross-cites Cluster A skills as "dependencies"

Total: 6/10 top results are clones from one publisher.
Recommendation: Flag publisher for review. Genuine skills in results: 4/10.

Limitations

Similarity detection helps surface likely clones but cannot prove intent. Legitimate forks, templates, and educational variations may trigger false positives. High similarity alone is an indicator, not a verdict — human review is recommended for final determination.