Agentic projects have a memory problem, so I built a small skill to capture decision context

I built my first agent skill to capture decision context during agentic development. The problem I’m trying to solve is simple. When working with coding agents, many important decisions happen inside the flow of a session: why this approach was chosen why another option was rejected what constraint mattered at that moment what context led to the final implementation A week later, the code diff usually explains what changed, but not always why it changed. With one or two projects, I can still recover this manually from memory, chat history, commits, or notes. But with multiple projects, fast iterations, and agent-driven changes, this context disappears very quickly. So I made a small agent skill that captures raw decision-related data while the development process is happening. Right now it is intentionally only the first layer: raw capture, not “smart memory” yet. The goal is to preserve enough context so later it becomes possible to answer questions like: What decision was made? Why was it made? What alternatives were considered? What was the surrounding context? How does this decision connect to other decisions across the project or even across projects? Later, I want to build a compiler/processing layer on top of these raw traces, so they can become a more useful memory system for both humans and agents. I’ll put the repo link in the comments. Would be interested to hear how others are handling this problem: do you store agent decisions per repo, in a global knowledge base, in session logs, or somewhere else?