lorekeeper-link-memorieslisted

# Link Memories Use `lore_recommend_links` to find high-confidence link candidates between memories, then confirm them via `lore_insert`. ## When to Run - **After inserting a batch of memories** — new memories have few or zero links - **After a session** — `lore_reflect` + `lore_insert` creates new facts; follow up with `lore_recommend_links` on key new memories - **Periodic maintenance** — run `lore_recommend_links` on orphaned memories (those with `links: []` in search results) ## How to Use ```python candidates = mcp_lore_recommend_links(lore_id="<memory-id>") ``` Optional `top_k` parameter overrides the max candidates returned. ## Reading the Output Each candidate has: - `weighted_score` (0.0–1.0): combined score from all 4 signals (cosine, BM25, entity overlap, temporal proximity) - `scores`: per-signal breakdown for transparency The agent evaluates candidates itself — it already has an LLM. `lore_recommend_links` only surfaces the data. ## What Makes a Good Link - **Shared topic/entities**: memories about the same person, project, concept, or codebase - **Causal or structural**: `depends_on` for dependencies, `used_in` for implementation details - **Chronological**: `supersedes` for newer versions replacing older ones - **Conflict**: `contradicts` when two memories make conflicting claims about the same thing ## What to Skip - **Low weighted_score** (< 0.3): weak signals, likely spurious - **Temporal-only matches**: memories created close in time but about