add 800-char memory limit and optimize for focused recall

- Add MAX_MEMORY_CHARS=800 Pydantic validation on MemoryStore.content
- Update auto-learn judge prompts: "ONE topic per event", 100-500 chars,
  include the WHY not just the WHAT
- Split 9 mega-memories (800-2400ch) into 70 focused memories (100-500ch)
  via migration script

Before: median 331ch, 11 memories >800ch, recall wastes 84% of returned tokens
After: median 213ch, 2 memories >800ch (dense single-topic refs), recall returns
only the relevant knowledge

Trade-off research: PostgreSQL ts_rank gives the same score regardless of
document size, so a 2400-char memory with 12 topics gets recalled for any
of its 12 topics but wastes context with the other 11. Focused memories
(100-500ch) give higher signal-to-noise per recall.

src/claude_memory/api/models.py

@@ -3,8 +3,11 @@ from typing import Any, Optional
 from pydantic import BaseModel, Field
 
 
+MAX_MEMORY_CHARS = 800
+
+
 class MemoryStore(BaseModel):
-    content: str
+    content: str = Field(..., max_length=MAX_MEMORY_CHARS)
     category: str = "facts"
     tags: str = ""
     expanded_keywords: str = ""