feat: nightly agentic pre-generation of memory reels

Implement end-to-end nightly pre-generation of memory reels with agentic
scripting that grounds narration in calendar, location, messages, and RAG.

Sections A-E from the plan:

A. Extract produce_reel pipeline core from run_reel_job with
   ScripterMode::Fast/Agentic and progress callbacks.

B. Agentic scripter: factor run_readonly_tool_loop from the insight
   generator, build read-only tool gate, prompt builder with GPS, and
   generate_script_agentic with fallback to fast path.

C. Precomputed reels ledger (SQLite table + DAO), GET /reels/precomputed
   handler with validity gate, GET /reels/by-key/{key}/video streaming,
   and normalize_library_key helper.

D. Nightly scheduler: spawn_pregen_scheduler with configurable hour,
   run_pregen_batch (day/week/month spans), pregen_one with dedup and
   disk-check, secs_until_next_run_hour time math.

E. user_ai_prefs passive mirror table + DAO for param capture in
   create_reel_handler and replay in the scheduler.

Also fixes resolve_library_param signature to take &[Library] and adds
resolve_library_param_state wrapper for AppState callers.

New files: migrations/2026-06-13-000000_add_precomputed_reels/,
  migrations/2026-06-13-000010_add_user_ai_prefs/,
  src/database/precomputed_reel_dao.rs,
  src/database/user_ai_prefs_dao.rs

src/database/models.rs

@@ -1,6 +1,7 @@
 use crate::database::schema::{
     entities, entity_facts, entity_photo_links, favorites, image_exif, insight_generation_jobs,
-    libraries, personas, photo_insights, users, video_preview_clips,
+    libraries, personas, photo_insights, precomputed_reels, user_ai_prefs, users,
+    video_preview_clips,
 };
 use serde::Serialize;
 
@@ -505,3 +506,56 @@ pub struct InsightGenerationJob {
     pub result_insight_id: Option<i32>,
     pub error_message: Option<String>,
 }
+
+// --- Precomputed reels -------------------------------------------------------
+
+#[derive(Insertable)]
+#[diesel(table_name = precomputed_reels)]
+pub struct InsertablePrecomputedReel {
+    pub span: String,
+    pub library_key: String,
+    pub cache_key: String,
+    pub output_path: String,
+    pub title: String,
+    pub media_count: i32,
+    pub render_version: i32,
+    pub tz_offset_minutes: i32,
+    pub voice: Option<String>,
+    pub generated_at: i64,
+}
+
+#[derive(Serialize, Queryable, Clone, Debug)]
+pub struct PrecomputedReel {
+    pub id: i32,
+    pub span: String,
+    pub library_key: String,
+    pub cache_key: String,
+    pub output_path: String,
+    pub title: String,
+    pub media_count: i32,
+    pub render_version: i32,
+    pub tz_offset_minutes: i32,
+    pub voice: Option<String>,
+    pub generated_at: i64,
+}
+
+// --- User AI preferences (Section E) ----------------------------------------
+
+#[derive(Queryable, Insertable, Debug, Clone, serde::Deserialize, serde::Serialize)]
+#[diesel(table_name = user_ai_prefs)]
+pub struct UserAiPrefs {
+    pub id: i32,
+    pub voice: Option<String>,
+    pub tz_offset_minutes: Option<i32>,
+    pub library: Option<String>,
+    pub updated_at: i64,
+}
+
+#[derive(Insertable, Debug, Clone, serde::Deserialize, serde::Serialize)]
+#[diesel(table_name = user_ai_prefs)]
+pub struct UpsertUserAiPrefs {
+    pub voice: Option<String>,
+    pub tz_offset_minutes: Option<i32>,
+    pub library: Option<String>,
+    pub updated_at: i64,
+}