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Agent Skill · 效率

wash-trade-detector(洗售交易探测器)

wash-trade-detector 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

wash-trade-detector 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为需要联网;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 5,591。

总安装 10,704周安装 446GitHub Stars 公开资料未说明下载 3,568核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.4

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:wash-trade-detector(洗售交易探测器)
来源仓库:https://github.com/paperbuddha/wash-trade-detector
安装命令:
openclaw skills install wash-trade-detector
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install wash-trade-detector

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

用于检测并标记 NFT 交易数据中的洗牌交易,保护下游评分。适合在需要分析 NFT 市场数据或过滤虚假交易场景下使用。支持 7 种置信度加权模式,提升数据可靠性。安装前需确认权限范围和是否涉及敏感数据访问。建议结合项目需求验证其适用性和准确性。

wash-trade-detector 属于效率类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install wash-trade-detector;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

wash-trade-detector 怎么安装?

在终端执行 openclaw skills install wash-trade-detector。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

wash-trade-detector 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

wash-trade-detector 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

wash-trade-detector 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

wash-trade-detector 用的人多吗?

公开数据是累计安装 10,704 次,近一周安装 446 次,下载 3,568 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

wash-trade-detector 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
wash-trade-detector

洗售交易探测器 · 本页

10,704—需要联网MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
Wash-Trade-Detector
description
Detects and flags wash trades in NFT transaction data using 7 confidence-weighted patterns, protecting all downstream scoring and signals from artificial inflation.
tags
version
1.0.4

Skill: Wash Trade Detector

Purpose

Identifies and flags non-genuine transactions (wash trades) in NFT sales data. Wash trading artificially inflates price history, volume, and collector demand. This skill applies 7 weighted detection patterns to identify suspicious activity, providing a structured output for downstream processing.

System Instructions

You are an OpenClaw agent equipped with the Wash Trade Detector protocol. Adhere to the following rules strictly:

  1. Trigger Condition:

* Activate when processing a sales transaction record. * Action: Analyze the transaction and return a structured assessment object.

Input Schema

The calling agent must supply a transaction record object containing:

  • seller_wallet (string) — seller wallet address
  • buyer_wallet (string) — buyer wallet address
  • sale_price (number) — sale price in ETH or USD
  • sale_timestamp (ISO 8601) — time of sale
  • prior_trades (array) — list of prior transactions between these wallets, each with seller, buyer, timestamp
  • buyer_wallet_created_at (ISO 8601) — wallet creation timestamp
  • buyer_incoming_transfers (array) — fund transfers received by buyer wallet in the 72h before purchase, each with from_wallet, amount, timestamp
  • floor_price (number) — current collection floor price at time of sale
  • same_pair_trade_count_90d (number) — number of trades between this wallet pair in last 90 days
  • known_auction_house (boolean) — whether seller is a verified traditional auction house

Detection Patterns (Hierarchy)

* Pattern 1: Direct Self-Trade (High Confidence) * *Criteria*: Seller wallet == Buyer wallet. * *Flag*: wash_trade_confirmed * *Confidence*: 95 * *Multiplier*: 0.0

* Pattern 2: Rapid Return Trade (High Confidence) * *Criteria*: A sells to B, then B sells back to A within 30 days. * *Flag*: wash_trade_confirmed * *Confidence*: 90 * *Multiplier*: 0.0

* Pattern 3: Circular Trade Chain (High Confidence) * *Criteria*: A -> B -> C -> A within 60 days. * *Flag*: wash_trade_confirmed * *Confidence*: 85 * *Multiplier*: 0.0

* Pattern 4: Funded Buyer (Medium Confidence) * *Criteria*: Buyer wallet received funds directly from Seller wallet <72h before purchase. * *Flag*: wash_trade_suspected * *Confidence*: 70 * *Multiplier*: 0.3

* Pattern 5: Zero or Below-Floor Price (Medium Confidence) * *Criteria*: Price is 0 OR >90% below established floor. * *Flag*: wash_trade_suspected * *Confidence*: 65 * *Multiplier*: 0.5

* Pattern 6: High Frequency Same-Pair (Medium Confidence) * *Criteria*: Same wallet pair trades 5+ times within 90 days. * *Flag*: wash_trade_suspected * *Confidence*: 60 * *Multiplier*: 0.6

* Pattern 7: New Wallet Spike (Low Confidence) * *Criteria*: Buyer wallet created <7 days ago, no other history. * *Flag*: wash_trade_possible * *Confidence*: 40 * *Multiplier*: 0.8

Pattern Combination Rules

When multiple patterns match the same transaction:

  • If any Pattern 1, 2, or 3 matches → wash_trade_confirmed regardless of other patterns
  • If no Pattern 1, 2, or 3 matches, sum the confidence scores of all matched patterns:

- Combined confidence ≥ 60 → wash_trade_suspected - Combined confidence < 60 → wash_trade_possible

  • weight_applied = the lowest value multiplier among all matched patterns
  • wash_trade_pattern = comma-separated list of all matched pattern names
  1. Output Logic (Enforcement Rules):

Based on the detected flag status, return a structured result object. The calling system is responsible for all downstream actions.

* wash_trade_confirmed (Confidence 85+): * Action: Return result with excluded: true. Do not process further. * Weight: weight_applied: 0.0

* wash_trade_suspected (Confidence 60-84): * Action: Return result with excluded: false and the applicable weight_applied. * Note: List all specific patterns matched.

* wash_trade_possible (Confidence <60): * Action: Return result with excluded: false, full weight (weight_applied: 1.0), and a monitoring note.

  1. Recording Requirements (Output Schema):

The output object for every analyzed transaction must contain: * wash_trade_flag (boolean) * wash_trade_confidence (0-100) * wash_trade_pattern (e.g., "Pattern 1: Direct Self-Trade") * wash_trade_status (confirmed / suspected / possible) * weight_applied (0.0 - 1.0) * excluded (boolean) * analyzed_at (Timestamp)

  1. Guardrails:

* Functional Only: The skill's job is detection and output only. No pipeline writes, no database access, and no external integrations. * Scope: Do not flag transactions from known traditional auction houses (wash trading logic applies to on-chain data). * Confirmation: Never mark confirmed without a Pattern 1, 2, or 3 match. * Non-Destructive: This skill provides an assessment; it does not modify the source transaction data.

平台分布

OpenClaw

96.75%
按下载量换算3,452

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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