feat: add hidden entity traits (Workstream A) + idempotent migration

Entity.traits (alignment/power/volatility/deceptiveness, 0.0-1.0 each) is
rolled once at mint time in entities.py, seeded from the entity's
signature via random.Random(f"traits:{signature}") — a separate rng
namespace from normalize_profile's existing "norm:" rng, and never fed
into mint_prompt, so persona text stays fully decoupled from ground
truth. normalize_profile now includes "traits" in its returned dict;
fallback_profile inherits it for free since it already delegates to
normalize_profile.

Adds the new JSONB column to the Entity model (default {}) and the
idempotent `ALTER TABLE entities ADD COLUMN IF NOT EXISTS traits ...`
migration line to main.py's lifespan, per the live-Postgres migration
convention this spec introduces (no Alembic in this repo).

Tests cover trait value ranges, signature-determinism, and
persona/trait independence (same persona template pairs with a wide
spread of alignment rolls across signatures), plus a regression check
that mint_prompt's signature never grows a traits parameter.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

backend/app/entities.py

@@ -96,6 +96,23 @@ def parse_mint_response(text: str) -> dict | None:
     return profile
 
 
+def roll_traits(signature: str) -> dict:
+    """Roll the four hidden truth-traits for an entity, seeded from its
+    signature. These are never derived from — or fed into — the LLM persona
+    prompt (see `mint_prompt`, which never sees this function's output):
+    persona text must stay fully decoupled from ground truth, so a
+    convincing "sweet old lady" persona can pair with any alignment roll.
+    A separate `random.Random` namespace (`"traits:"` vs. `normalize_profile`'s
+    `"norm:"`) keeps this roll independent of the cosmetic-defaults rng."""
+    rng = random.Random(f"traits:{signature}")
+    return {
+        "alignment": rng.uniform(0.0, 1.0),
+        "power": rng.uniform(0.0, 1.0),
+        "volatility": rng.uniform(0.0, 1.0),
+        "deceptiveness": rng.uniform(0.0, 1.0),
+    }
+
+
 def normalize_profile(profile: dict, signature: str) -> dict:
     """Coerce an LLM (or fallback) profile into the exact shape the DB and
     frontend expect, filling gaps with signature-deterministic defaults."""
@@ -138,6 +155,7 @@ def normalize_profile(profile: dict, signature: str) -> dict:
         },
         "quotes": [str(q)[:200] for q in quotes[:4] if isinstance(q, str)]
         or rng.sample(_QUOTE_BANK, 2),
+        "traits": roll_traits(signature),
     }