feat: Qdrant skills (#1412)

skills/qdrant-scaling/scaling-query-volume/SKILL.md

@@ -0,0 +1,23 @@
+---
+name: qdrant-scaling-query-volume
+description: "Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'."
+---
+
+# Scaling for Query Volume
+
+Problem: When a query has a large limit (e.g. 1000) and there are multiple shards (e.g. 10), naively each shard must return the full 1000 results — totaling 10,000 scored points transferred and merged. This is wasteful since data is randomly distributed across auto-shards.
+
+## Core idea
+
+Instead of asking every shard for the full limit, ask each shard for a smaller limit computed via Poisson distribution statistics, then merge. This is safe because auto-sharding guarantees random, independent data distribution.
+
+## When it activates
+
+- More than 1 shard
+- Auto-sharding is in use (all queried shards share the same shard key)
+- The request's limit + offset >= SHARD_QUERY_SUBSAMPLING_LIMIT (128)
+- The query is not exact
+
+## Key tradeoff
+
+ The strategy trades a small probability of slightly incomplete results for a large reduction in inter-shard data transfer, especially for high-limit queries across many shards. The 1.2x safety factor and the 99.9% Poisson threshold keep the error rate very low — comparable to inaccuracies already introduced by approximate vector indices like HNSW.