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kg-core

Knowledge Graph — persistent, granular, evolving memory. Part of memory initially arrives preloaded: a "KG MEMORY PRELOADED" block carrying the session_id and most important portion of memory. Then the full read is on you: kg_read(cwd="<project root>") comes before any work, whatever the task. Full read still brings up only most important memories. Memory is highly optimised and served in layers. Recall: if needed details are not in preload/read and there is a gist that points in the right direction, recall full node; Capture: at the moment of learning, once the dots connect; Connect rather than duplicate — an edge beats a new node; Search for more — the read/sync shows the top of graph, not all of it. In a rich graph the fact you need is often buried under fresher work; search reaches every tier. Endorse at wrap-up: kg_useful(ids) on the ≤5 nodes that demonstrably changed this session's outcome. That credit is what keeps a node alive — it is never earned by being read, only by being named at the end. Mech

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