I used AI to distill 5,582 tweets from an X blogger into a reusable investment analysis Skill.
Origin There's a trader on X (formerly Twitter) called Serenity (@aleabitoreddit) — 450K followers, focused on AI/semiconductor supply-chain analysis. His tweets are exceptionally high quality: he traces hyperscaler capex (Google, Microsoft, Meta, Amazon) upstream to find single-point bottlenecks, then positions before institutions react. His methodology is clear: Don't buy NVDA. Walk the supply chain upstream and find the one chokepoint. The problem: 5,582 tweets spanning 10 months, extremely high information density but wildly fragmented. Reading them all manually would take days, and key insights are scattered across replies and threads. I thought: can I use AI to distill all these tweets into a structured, reusable analytical framework? Method 1. Data Collection Used agent-reach 's twitter-cli with date-windowed search to work around X's pagination limits. Collected every tweet from 2025-07-02 to 2026-05-28 (~5,582 tweets) plus 4 long-form X Articles. 2....