feat(quality): A3 — L1 LLM judge via API (5 chunks, 0.0003 USD/job)

L1 quality layer — uses a cheap LLM via the OpenAI-compatible API to
validate translation quality. Designed to be the SECOND line of defense
after L0 (script detection, length, pattern).

Architecture:
  - sampler.py — picks 5 representative chunks per job (longest first,
    skips L0-failed indices, skips too-short or identical pairs)
  - llm_judge.py — OpenAI-compatible client, binary verdict per chunk
    (accurate / fluent / correct_language / no_leaks), JSON output,
    hard timeout, defensive (never raises), cost estimation built in
  - pipeline.py — defensive wrapper that integrates both, never breaks
    a translation job, always logs a structured event

Integration:
  - 5 feature flags in config.py (QUALITY_L1_ENABLED, _LOG_ONLY, etc.)
  - QUALITY_L1_LOG_ONLY=true by default: log-only mode, verdict NEVER
    blocks or retries a job
  - Reuses the chunks extracted by L0 (no double work)
  - Passes the set of L0-failed indices so L1 doesn't re-judge them
  - Wrapped in try/except so a misconfigured L1 NEVER breaks a job

Default config: deepseek-chat via DeepSeek API
  - Cost: ~0.0003 USD per job (5 chunks)
  - Speed: typically 1-2s per call, hard ceiling at 8s
  - Easy to swap: just set L1_JUDGE_BASE_URL and L1_JUDGE_MODEL

LLM judge is intentionally a SEPARATE model from the translator
(self-evaluation bias mitigation — Meta/Stanford papers 2024-2025).

Tests:
  test_sampler.py — 9 tests covering the sampling strategy
  test_llm_judge.py — 22 tests covering init, parsing, mocked API,
    cost estimation, env factory
  test_l1_pipeline.py — 6 tests covering the wrapper
  Total new: 37 tests, all pass
  Grand total quality+format: 264 tests passing (0 regression)

  All 36 new tests + 111 L0 tests + 117 existing translator tests = 264

Phase 1 (observation) for 2 weeks. Then QUALITY_L1_LOG_ONLY=false
to enable auto-retry via the fallback chain.

services/quality/__init__.py

@@ -1,17 +1,28 @@
 """
 Quality check layer for translations.
 
-Track A1 — L0 backend (observation only).
-Pure Python, no new dependencies, no network calls.
+Tracks:
+  A1 — L0 backend (pure Python, no API calls). Always available.
+  A2 — L0 frontend JavaScript mirror. Browser/Node compatible.
+  A3 — L1 LLM judge (API-based, opt-in). Cheap model, sampled.
+
 Designed to be ADDITIVE: existing translation flow is untouched.
 
 Public API:
+  L0:
     QualityCheckResult         — per-chunk result dataclass
     DocumentQualityResult      — aggregated result dataclass
     evaluate_chunk(...)        — score a single (source, translation) pair
     evaluate_document(...)     — score a list of pairs and aggregate
     run_l0_check(...)          — defensive wrapper used by the route
     extract_sample(...)        — extract text from a finished file
+  L1:
+    L1Result                   — verdict dataclass
+    LLMJudge                   — judge client
+    run_l1_check(...)          — async wrapper used by the route
+    sample_chunks_for_l1(...)  — pick representative chunks
+  Helpers:
+    get_script, isArabicScriptLang
 """
 
 from .script_detector import (
@@ -21,10 +32,13 @@ from .script_detector import (
     evaluate_document,
     detect_arabic_variant,
 )
-from .pipeline import run_l0_check
+from .pipeline import run_l0_check, run_l1_check
 from .file_extractor import extract_sample
+from .sampler import sample_chunks_for_l1
+from .llm_judge import L1Result, L1ChunkVerdict, LLMJudge
 
 __all__ = [
+    # L0
     "QualityCheckResult",
     "DocumentQualityResult",
     "evaluate_chunk",
@@ -32,4 +46,10 @@ __all__ = [
     "detect_arabic_variant",
     "run_l0_check",
     "extract_sample",
+    # L1
+    "L1Result",
+    "L1ChunkVerdict",
+    "LLMJudge",
+    "run_l1_check",
+    "sample_chunks_for_l1",
 ]