kst-ai-assets-usagelisted

# kst-ai-assets-usage Kasetto knows what you installed. Each agent knows what it ran. Nothing on the machine joins the two, so installed-and-forgotten assets accumulate silently - skills nobody has ever invoked, MCP servers loading tool definitions into every request for a server the user stopped using months ago. This skill closes that loop: read the lock, read each agent's local session store, join them, and say plainly what is alive and what is dead. ## Run It Two scripts, both standard-library Python 3, both offline. ```bash python3 scripts/collect.py --out usage.json # scan + join python3 scripts/render.py usage.json --out usage.html ``` `collect.py` is the slow one on a cold cache (a few seconds for a large history) and near-instant afterwards - it caches per-file results in `$XDG_CACHE_HOME/kasetto/usage-scan.json` keyed on size and mtime, so a rerun only touches files the agent appended to. Delete that file to force a full rescan. Open the dashboard when it is written, then give the user the short version in the terminal: how many assets are idle, the biggest surprises, and what you would cut. The HTML is the artifact; your reading of it is the value. ## The Rule That Matters: Never Overstate Coverage `collect.py` reports a `coverage` block listing every agent it knows about and whether it found a readable session store. Honour it. An asset the user drives daily from an agent that has no provider **looks completely dead in this report**. If you presen