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    NFT Wash Trading Investigation Agent

    1

    Systematic NFT transaction analysis to detect circular trading, price anomalies, and suspicious wallet clusters.

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    NFT Wash Trading Investigation Agent

    NFT Wash Trading Investigation Agent

    Example session with this skill installed

    Analyze the attached CSV of transaction logs for the 'Neon Ape' collection on Ethereum from Oct 1 to Oct 15. Look for circular trading patterns and repeated buyer-seller pairs.

    • Read your context and instructions
    • Compiled the nft wash trading

    I have analyzed 1,240 transaction records for the 'Neon Ape' collection. The investigation identified three high-priority circular paths and five clusters of repeated wallet interactions.

    NFT Wash Trading Investigation Report

    1. Executive Summary

    The analysis covered transactions between block 18250000 and 18360000. While 85% of volume appears organic, 12% is concentrated within four wallet clusters exhibiting circular behavior.

    2. Candidate Circular Paths

    Path IDWallet SequenceHopsTotal Volume (ETH)Primary Asset
    CP-010x12a... -> 0x45b... -> 0x78c... -> 0x12a...345.5Neon Ape #442
    CP-020x99d... -> 0x11e... -> 0x99d...222.0Neon Ape #108

    3. Repeated Wallet Interactions

    BuyerSellerCountTime WindowEvidence
    0x55f...0x66g...848 HoursReciprocal sales of 4 unique tokens
    0xaa1...0xbb2...512 HoursSequential mint-to-sale transfers

    4. Competing Hypotheses

    Hypothesis A (Suspicious): Cluster CP-01 is engaging in circular trading to inflate the floor price of high-rarity apes. Evidence: 3 sales within 6 hours at 15% above market average.
    *

    Hypothesis B (Organic): The wallets represent a specialized trading desk or liquidity provider. Evidence: Wallets interact with 20+ other collections, though the timing here is unusually tight.

    5. Prioritized Follow-up Actions

    1. Critical: Inspect funding sources for 0x12a and 0x78c to check for common intermediary wallets.

    High: Verify if the 'Neon Ape' marketplace contract has specific volume-based incentives active during this window.

    Next steps

    • Provide funding logs for the flagged wallets to check for common origin.
    • Request trait-rarity data to normalize the price anomaly findings.
    • Extend the time window to see if these wallets have a history of reciprocal trading.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Identifying artificial volume in NFT markets requires more than just spotting high prices. This skill provides a rigorous framework for investigating transaction patterns that suggest wash trading, focusing on data normalization and hypothesis testing. It moves beyond simple heuristics by requiring evidence-linked findings for every flagged anomaly.

    What it does

    • Normalize transactions by standardizing timestamps, prices, and event types across disparate data sources.
    • Map circular paths to identify complex wallet-to-wallet cycles where assets or funds return to the point of origin.
    • Detect repeated interactions between buyer-seller pairs to find high-frequency reciprocal trading.
    • Analyze price anomalies by comparing sale values against collection baselines and trait-based premiums.
    • Assess wallet relationships using funding links and temporal coordination to identify potentially related clusters.

    How it works

    1. Data ingestion where you provide NFT transaction logs, wallet addresses, and timeframes.
    2. Pattern discovery where the agent scans for circularity, reciprocal trades, and price outliers.
    3. Hypothesis generation where the agent weighs suspicious activity against plausible organic explanations like liquidity providing or bundled sales.
    4. Evidence reporting resulting in a structured document linking every finding to specific transaction hashes or row IDs.

    Frameworks & tools

    Works with any structured transaction data from blockchain explorers (Etherscan, Solscan), marketplace APIs (OpenSea, Blur), or custom CSV/JSON exports.

    Why this beats prompting it yourself

    Generic prompts often hallucinate fraudulent intent or miss complex circular paths involving three or more wallets. This skill enforces a strict methodology that separates observed facts from interpretations and requires competing hypotheses for every finding.

    Use cases

    • Audit collection volume to determine if a project's secondary market activity is organic or manufactured.
    • Due diligence for investors looking to verify the liquidity and holder distribution of a high-value NFT.
    • Marketplace integrity checks to identify accounts that may be gaming reward systems or loyalty points.

    Known limitations

    Requires transaction-level data to be provided. Cannot independently verify beneficial ownership or prove intent. Findings are limited by the quality and completeness of the supplied data logs.

    Requires provided transaction logs. Cannot prove intent or beneficial ownership. Limited by data quality and missing fields. Findings are evidence-based leads, not legal proof.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

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    Permissions

    Terminal / Shell
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    Listed22 days ago

    What's inside

    Frequently Asked Questions

    The agent requires structured transaction data in CSV or JSON format, such as logs exported from Etherscan, Solscan, or marketplace APIs (OpenSea/Blur), including timestamps, wallet addresses, and transaction hashes.

    Unlike generic prompts, this skill uses a specialized framework to detect multi-hop circular paths (e.g., Wallet A to B to C to A) and filters out 'noise' like legitimate bundled sales or floor sweeps.

    While optimized for high-activity chains like Ethereum and Solana, the agent is chain-agnostic as long as the transaction data is normalized to the required schema before ingestion.

    You will receive the agent's logic configuration and a standardized reporting template that ensures all flagged anomalies are directly linked to verifiable transaction hashes.

    This agent cannot definitively prove 'intent' or legal wrongdoing; it provides a data-driven report on suspicious patterns and statistical anomalies that suggest non-organic activity.

    The agent can process thousands of transactions in a single session, though performance may vary depending on the context window limits of the underlying LLM you are using.

    New in Web3 & Crypto