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wash-trade-detector洗售交易探测器

Agent Skill

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。

总安装

10,704

周安装

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GitHub Stars

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下载量

3,568
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

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

简介

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。

SKILL.md

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.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

96.75%
按下载量换算3,452

安全审计

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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

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来源信息

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