feat: profile layer — semantic memory (consolidation step 2)

Derive a standing profile of the user from session gists and inject it into
every prompt, so identity/abstract questions ("what kind of player am I",
"what are my leaks") are answered from distilled knowledge instead of noisy
single-vector recall (which finds passages, not patterns).

- memory: profile table + get/set_profile, list_summaries
- lyra/profile.py: rebuild_profile map-reduces all gists (batch -> extract
  durable facts -> fold-merge) into one profile doc; `lyra-profile` CLI
- chat.build_messages injects "What you know about Brian" after the persona

Run after lyra-summarize (needs gists). Verified (stubbed): map-reduce, storage,
and prompt injection.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

lyra/memory.py

@@ -43,6 +43,15 @@ CREATE TABLE IF NOT EXISTS summaries (
     last_exchange_id INTEGER NOT NULL,
     created_at TEXT NOT NULL
 );
+
+-- Derived semantic memory: standing facts about the user, distilled from the
+-- session gists by the consolidation pass. Single row (id='self').
+CREATE TABLE IF NOT EXISTS profile (
+    id TEXT PRIMARY KEY,
+    content TEXT NOT NULL,
+    sessions_covered INTEGER NOT NULL,
+    updated_at TEXT NOT NULL
+);
 """
 
 _conn: sqlite3.Connection | None = None
@@ -290,6 +299,44 @@ def unsummarized_count(session_id: str) -> int:
     return int(r["n"])
 
 
+def list_summaries() -> list[Summary]:
+    """Every session gist (for the profile/era consolidation passes)."""
+    conn = _connection()
+    rows = conn.execute(
+        "SELECT session_id, content, last_exchange_id, created_at FROM summaries "
+        "ORDER BY created_at ASC"
+    ).fetchall()
+    return [
+        Summary(
+            session_id=r["session_id"],
+            content=r["content"],
+            last_exchange_id=r["last_exchange_id"],
+            created_at=r["created_at"],
+        )
+        for r in rows
+    ]
+
+
+def set_profile(content: str, sessions_covered: int, profile_id: str = "self") -> None:
+    """Store/replace the derived semantic profile."""
+    now = datetime.now(timezone.utc).isoformat()
+    conn = _connection()
+    with conn:
+        conn.execute(
+            "INSERT INTO profile (id, content, sessions_covered, updated_at) "
+            "VALUES (?, ?, ?, ?) "
+            "ON CONFLICT(id) DO UPDATE SET content=excluded.content, "
+            "sessions_covered=excluded.sessions_covered, updated_at=excluded.updated_at",
+            (profile_id, content, sessions_covered, now),
+        )
+
+
+def get_profile(profile_id: str = "self") -> str | None:
+    conn = _connection()
+    r = conn.execute("SELECT content FROM profile WHERE id = ?", (profile_id,)).fetchone()
+    return r["content"] if r else None
+
+
 def recall_summaries(query: str, k: int = 3, exclude_session: str | None = None) -> list[Summary]:
     """Top-k session summaries most similar to `query` (the long-term gist tier)."""
     [q_vec] = llm.embed([query])