Learning Log

Description

For each substantial AI conversation, this Claude Skill generates a recap:

• The problem - What was I trying to solve or understand?
• What I didn’t know
• What I learned
• My thinking pattern - What does this conversation reveal about how I approach problems?

Then, at the end of each day, it generates a topline report that synthesizes these recaps to tell me:

• Concepts encountered
• Strengths and watchouts - patterns observed
• Three pieces of advice - specific to that day’s work

The thinking patterns are useful and actionable, and I will often act on the advice before wrapping up for the day.

Intent

As I grapped with technical concepts and command line tools that had stumped me in days long past, I was confronted with the question what did I actually do vs. what did Claude do for me?. Slipping into a lazy, shallow, and fast mode of development would be too easy, and highly regrettable.

But once I realized the extent to which work was happening in a closed system - chat conversations with Claude Code - I saw an opportunity to insert a compounding intelligence loop with simple scripts.

I looked at the following and began using this tool as part of my workflow.

• where feedback can be captured
• how outputs can improve future actions
• how system capability can compound

Examples of call-outs that I’ve gotten

• She’s thinking across phases — recognising when a fix belongs in the current prototype versus the upcoming Astro migration — which is a significant shift toward architectural thinking.
• She used documentation (CLAUDE.md) as a forward-looking tool — converting a frustrating gotcha into a reusable system guard. That’s exactly the AI workflow designer instinct.
• Several of today’s concepts were encountered but not built: Vercel deploy hooks, cron jobs, popstate double-firing, CSS order. These are real-world infrastructure concerns that will surface again in the Astro migration. The fact that they were explained but not owned means there’s a gap to close when implementation time comes.
• She asks UX-clarifying questions upfront (dismiss vs. delete, stay in list vs. remove) that prevent rework — design-trained instincts actively improving build quality.
• The vercel dev / .env.local issue suggests some environment-layer mental models are still fuzzy — she knew something was wrong but needed help diagnosing the exact mechanism. This is a layer worth revisiting deliberately.

(And yes, it’s designed to speak in third-person. Neutral and judgement-free, of course.)