Rank meme token launches by lifecycle stage and AI-detected hot topics.
npx skills add https://github.com/nirholas/three-ui --skill binance-meme-rush
Please help me install this Agent Skill. Skill: binance-meme-rush Source: https://github.com/nirholas/three-ui/tree/main/data/skills/trading/binance-meme-rush Command: npx skills add https://github.com/nirholas/three-ui --skill binance-meme-rush
Meme token traders lack a reliable, structured way to discover newly launched, pre-migration, and just-migrated tokens and to connect those listings to AI-detected market hot topics.
Use the binance-meme-rush skill to fetch the Latest or Viral Topic Rush list for your target chain and then extract the associated tokens ranked by net inflow.
High-intent search queries and answers about installing and using this skill.
To find newly launched meme tokens, you can retrieve ranked lists for launchpad stages including New, Finalizing, and Migrated. This tracks bonding-curve progress and liquidity metrics to support early entry decisions before migration windows close.
AI-driven hot topic discovery identifies trending market narratives ranked by net inflow. It connects these narratives to associated meme tokens, allowing traders to group entry decisions by the latest viral market stories rather than manual monitoring.
You can filter meme token launches using chain-specific filters to narrow results by liquidity, volume, holders, and bonding-curve progress. This approach also applies dev and insider wash-trading signals to refine downstream token selection.
Yes, you can track net inflow metrics for trending meme tokens. The process returns token and topic results that include bonding progress, liquidity, volume, holders, and net inflow data specifically for downstream trading selection.
Meme token sniping requires chain-specific API calls with rankType and filters to retrieve lifecycle rankings. These calls return structured metrics like bonding progress and net inflow, enabling traders to snipe new launches and watch migration windows accurately.
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