I turned 1,870 JSONL files into six new user-level skills for my AI platform in a single session. Here’s how I built a repeatable pipeline for skills-updater.

The Problem

I had a one-off question: 'Look through all my Claude Code JSONL files and recommend new skills.' This meant walking through ~904 MB of data across 45 project directories, filtering down to 2,752 real user-typed prompts, and cross-referencing against an existing skill set of 56 (9 user + 47 plugin). The manual deep-dive was expensive—too expensive to redo from scratch. So, I built skills-updater to automate it.

The Pipeline

The repo lives at the heart of my AI ecosystem, tied to Nexus and ARIA. I wrote scripts to parse the JSONL files, extract meaningful user interactions, and rank skill gaps.…