Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Wash-Trade-Detector
Detects and flags wash trades in NFT transaction data using 7 confidence-weighted patterns, protecting all downstream scoring and signals from artificial inf...
skill openclawclawhub Free
Detects and flags wash trades in NFT transaction data using 7 confidence-weighted patterns, protecting all downstream scoring and signals from artificial inf...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.4
Provenance
- Publisher
- PaperBuddha
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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: 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) β seller wallet address buyer_wallet (string) β buyer wallet address sale_price (number) β sale price in ETH or USD sale_timestamp (ISO 8601) β time of sale prior_trades (array) β list of prior transactions between these wallets, each with seller, buyer, timestamp buyer_wallet_created_at (ISO 8601) β wallet creation timestamp buyer_incoming_transfers (array) β fund transfers received by buyer wallet in the 72h before purchase, each with from_wallet, amount, timestamp floor_price (number) β current collection floor price at time of sale same_pair_trade_count_90d (number) β number of trades between this wallet pair in last 90 days known_auction_house (boolean) β 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 β 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 β₯ 60 β wash_trade_suspected Combined confidence < 60 β wash_trade_possible weight_applied = the lowest value multiplier among all matched patterns wash_trade_pattern = comma-separated list of all matched pattern names 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. 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) 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.
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md