I’m looking for a lightweight script that lets me paste a single Solana wallet address and instantly tells me whether the wallet looks safe to copy-trade or if it’s more likely being used to dump on followers or drain exit-liquidity. Core analysis logic should combine: • Transaction volume, frequency, and clustering patterns • Age of the wallet and activity bursts • Context from Solana blockchain explorer endpoints, real-time market price feeds, and relevant historical trade data A clear risk score or simple “good vs. flag” verdict is all I need on screen or in the console. Manual input of the wallet ID is fine for now; no CSV or API ingestion is required at this stage. What I expect you to deliver: • Well-commented code (Python preferred, but I’m open to Node.js if you have stronger tooling there) • A concise README with setup steps, dependencies, and example usage • Functions written so they can be expanded later (for batch scans or API endpoints) • Basic unit tests or sample addresses that demonstrate the good/flag logic working Please leverage standard Solana RPC or public explorer APIs, and feel free to suggest reputable price-feed and historical-trade sources. Accuracy matters more than fancy visuals, so keep the UI minimal and focus on solid on-chain analytics. What I need in details is: 1. Open the target wallet’s recent activity (first 50–100 txs). 2. Look for clusters of sells immediately after buys into the same token (many sell txs within seconds/minutes of buy). 3. Check where sells go — many transfers to the same set of small recipient wallets (possible copy-trader proxies) or to known exchange addresses. 4. Look for token approvals & allowance changes right before sells — unusually large allowances created then used quickly. 5. Search for many tiny transfers to many addresses right before a big sell (splitting profits across proxies). 6. Check slippage / router interactions: direct sells to the DEX router in rapid sequence (no OTC/over-the-counter). 7. Check for liquidity pulls or burn/mint anomalies in token contract around wallet sells. 8. Watch for patterned timing: sells that happen at near-identical intervals after buys (copy-trade automation signature). 9. Check ownership/admin settings (if token has owner): renounced status is not proof safe — still inspect transfer pattern. Core signals (compute for last N minutes / last M token buys): • fast_sell_ratio = (# sells executed within T seconds of token buy) / (total buys) -> suspicious if fast_sell_ratio >= 0.4 (40%) for a wallet. • multi_recipient_after_sell = number of distinct recipient wallets receiving transferred funds within X minutes after a sell -> suspicious if >= 3 recipients and total moved > 20% of proceeds. • fragmentation_count = number of micro-transfers (<0.5 SOL / small token amount) sent to distinct addresses within 5 minutes after big sell -> suspicious if >= 6. • approval_spike = allowance created > typical (e.g., > 10× average prior allowance) then spent within 60s. • exchange_sink_ratio = % of token proceeds moved to known exchange addresses within Y minutes -> suspicious if >= 30% immediately after sell. • timing_pattern_score = how close sells are to buys in seconds (standard deviation small → automation/copy-trade signature). Flag a wallet as high-risk if 2 or more of these signals are true.