[{"data":1,"prerenderedAt":153},["ShallowReactive",2],{"cycle-doc-qmap-c25-initial-prompt.md":3},{"cycle":4,"project":7,"path":10,"title":11,"toc":12,"ast":13},{"id":5,"title":6},"c25","ai-ecosystem-inventory-map",{"id":8,"label":9},"qmap","Qmap","initial-prompt.md","c25. AI Ecosystem Inventory Map",[],{"type":14,"children":15},"root",[16,24,38,98,103,115,120,148],{"type":17,"tag":18,"props":19,"children":20},"element","p",{},[21],{"type":22,"value":23},"text","Create a new qmap cycle for an inventory\u002Findex view of the company-wide AI ecosystem. This is not a single-session surface. The center of the map should be the active agents within a selected timeframe, with surrounding repositories, then files per repository, plus MCP servers, tools, and sub-agents.",{"type":17,"tag":18,"props":25,"children":26},{},[27,29,36],{"type":22,"value":28},"Build a dedicated wrapper component, ",{"type":17,"tag":30,"props":31,"children":33},"code",{"className":32},[],[34],{"type":22,"value":35},"QMapInventoryIndex",{"type":22,"value":37},", and start with qmap's existing actor types rather than inventing new meshes immediately:",{"type":17,"tag":39,"props":40,"children":41},"ul",{},[42,54,65,76,87],{"type":17,"tag":43,"props":44,"children":45},"li",{},[46,48],{"type":22,"value":47},"AI agents -> ",{"type":17,"tag":30,"props":49,"children":51},{"className":50},[],[52],{"type":22,"value":53},"agent",{"type":17,"tag":43,"props":55,"children":56},{},[57,59],{"type":22,"value":58},"repositories -> ",{"type":17,"tag":30,"props":60,"children":62},{"className":61},[],[63],{"type":22,"value":64},"app",{"type":17,"tag":43,"props":66,"children":67},{},[68,70],{"type":22,"value":69},"files -> ",{"type":17,"tag":30,"props":71,"children":73},{"className":72},[],[74],{"type":22,"value":75},"file",{"type":17,"tag":43,"props":77,"children":78},{},[79,81],{"type":22,"value":80},"MCP servers -> ",{"type":17,"tag":30,"props":82,"children":84},{"className":83},[],[85],{"type":22,"value":86},"mcp-server",{"type":17,"tag":43,"props":88,"children":89},{},[90,92],{"type":22,"value":91},"tools and sub-agents -> ",{"type":17,"tag":30,"props":93,"children":95},{"className":94},[],[96],{"type":22,"value":97},"skill",{"type":17,"tag":18,"props":99,"children":100},{},[101],{"type":22,"value":102},"Use tags to distinguish semantic families:",{"type":17,"tag":39,"props":104,"children":105},{},[106],{"type":17,"tag":43,"props":107,"children":108},{},[109],{"type":17,"tag":30,"props":110,"children":112},{"className":111},[],[113],{"type":22,"value":114},"entityType: 'AI Agent' | 'Repository' | 'File' | 'MCP Server' | 'Tool' | 'Sub-Agent'",{"type":17,"tag":18,"props":116,"children":117},{},[118],{"type":22,"value":119},"Prove the view with deterministic synthetic data first. The scenario pack should include both tiny demo conditions and very large enterprise stress cases. Validate at least these grouping modes:",{"type":17,"tag":39,"props":121,"children":122},{},[123,128,133,138,143],{"type":17,"tag":43,"props":124,"children":125},{},[126],{"type":22,"value":127},"Entity Type",{"type":17,"tag":43,"props":129,"children":130},{},[131],{"type":22,"value":132},"Repository",{"type":17,"tag":43,"props":134,"children":135},{},[136],{"type":22,"value":137},"Team",{"type":17,"tag":43,"props":139,"children":140},{},[141],{"type":22,"value":142},"Business Unit",{"type":17,"tag":43,"props":144,"children":145},{},[146],{"type":22,"value":147},"Flat",{"type":17,"tag":18,"props":149,"children":150},{},[151],{"type":22,"value":152},"Create a runnable experiment, add compact scenario stats, and call out whether repository\u002Ffile density forces summarization or filtering before new visual primitives are warranted.",1794599378853]