Adds the full computed_slot_name CASE (EquippableSlots decode, jewelry by
wielded-location, weapons, cloak) and the remaining SQL filters: weapon_type
(skill-id EXISTS), slot_names (per-slot OR clauses), item_set/item_sets
(translate_equipment_set_id, bug-for-bug).
Validated vs Python (total_count EXACT): weapon_type heavy/bow/caster (473/138/
474), slot_names ring/neck/cloak (1286/1428/220), item_set 13 (526). The
computed_slot_name VALUES match exactly (slot distribution identical: Head 721,
Hands 458, Feet 403, Chest 376, ...).
Two documented edge-case discrepancies, both Python main-vs-count CTE
inconsistencies (Python's count query uses a SIMPLIFIED slot CASE where armor ->
'Armor', so its own total_count disagrees with its item list): slot_names with
armor slot names, and sort_by=slot_name empty-string ordering. Our consistent
single-CASE implementation is arguably more correct; reconcile to Python's count
CTE later if strict parity on those is required.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Ports the search CTE (items_with_slots: combat/req/enh joins + the rating
extractions from item_raw_data.int_values JSONB via GREATEST/COALESCE, coverage
mask, computed_spell_names), the SQL filters, sort mapping, pagination, and the
DISTINCT count query. Returns each row's direct DB columns + computed booleans
(is_equipped/bonded/attuned/rare, condition_percent).
Validated vs the Python service on the production DB: total_count EXACT across
13 filter combinations (armor/jewelry/min_armor/min_damage/text/material/
min_value/is_rare/rating/equipped/character/workmanship), and 50-row alignment
with 0 direct-column mismatches (same SQL sort order, same rows).
Deferred to later slices: deep per-row enrichment (extract_item_properties:
material_name/spells/slot_name/object_class_name/...), and the enum-dependent
filters (has_spell/spell_contains/legendary_cantrips, slot_names, item_set,
weapon_type, underwear).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
