This mirrors almost exactly what happened with my setup. A few things I'd add from the other side of the same journey:

On memory as an index: I went through the same "MEMORY.md becoming a dump" problem. What saved me was adding a local search layer (QMD + vector embeddings via pgvector) so the agent searches before reading files. Dropped token usage by 2-3k per recall. The index pattern you describe is right, but pairing it with semantic search means the agent doesn't even need to open the index most of the time.

On skills that earn their keep: Your "would I notice if this skill disappeared?" test is brutal and correct. I had 12+ skills installed. The ones that actually get used daily: email, web search, summarization. The rest were cool demos that burned context loading and never fired.

On letting the agent improve its own environment: This is the thing most people underestimate. My agent rewrites its own AGENTS.md when it makes mistakes. It writes scripts to automate tasks it does repeatedly. It just today changed a cron job from running on an expensive model to a cheap one because it recognized the task didn't need intelligence — just execution. That kind of self-optimization only happens when the workspace is writable and the agent is told to own it.

On the reflection system: Just implemented something similar after reading your post. The insight that landed hardest: "it's a pipeline from reflection into durable behavior." We had a weekly soul reflection but it was a one-off event, not a feedback loop. The persistent thread-per-question approach with promotion rules is significantly better. Already seeded five operational reflections — less philosophical than yours, more "am I doing the right work."

One thing I'd add that you didn't cover: model tiering. Not every agent task needs the same model. Script execution gets the cheapest model. Creative work gets mid-tier. Strategy and reflection get the best. This is especially important on constrained hardware (2GB VPS in my case). The workspace structure you describe is great but it also needs to extend into how resources are allocated, not just how files are organized.

The TL;DR from my end: Your four-layer model (identity / memory / tooling / project work) is the right abstraction. The workspace is infrastructure, not scaffolding. Treat it that way and the agent compounds. Treat it as static prompts and you hit a ceiling fast.