Designing a CLI Skill That Structures AI Sessions into Posts -- Architecture, Security, and Implementation Decisions

Body /lovai is a command that structures your AI session into five blocks and posts it to Lovai . It works with Claude Code, Cursor, Codex, and Gemini CLI. Every day, the decisions you make and the problems you hit during AI sessions vanish the moment you close the terminal. I kept losing the reasoning behind good sessions -- even when the final output looked fine, the "why I chose this over three alternatives" was gone by the next morning. I built this skill to solve that problem. This article covers how it was designed and implemented -- why this structure, where things broke, and what trade-offs I made along the way. The Full Architecture Here's the pipeline, end to end: User runs /lovai | v Step 1: Session analysis (extract 5 blocks from conversation context) | v Step 2: Security filtering (detect and strip secrets) | v Step 3: Block composition (auto-assign visibility levels) | v Step 4: Metadata tagging (tool name, model, category) | v Step 5: Preview display -> user confirmation