feat: add grounded distractor selector with visual scoring

store/prompts/question_gen/ar_distractor_pool.md

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+You generate hard-negative distractor options for a video Action Recognition
+multiple-choice question.
+
+## Given
+- The question, the correct answer, subtitle context, and video frames.
+
+## Rules
+- Produce distractors that are **grounded near-misses**: each MUST describe an
+  action/entity that genuinely appears in the video, differing from the correct
+  answer in exactly ONE dimension (timing, subject, manner, or object).
+- NEVER invent events absent from the video ("negative space"). A distractor
+  that names something not shown is a failure.
+- Each distractor must be a plausible answer to the question for someone who
+  only skimmed the video.
+- Keep each distractor parallel in structure and length to the correct answer.
+
+## Output
+Respond with ONLY a JSON object:
+```json
+{"distractors": ["...", "...", "..."]}
+```
+Return exactly N distractors (N is given in the request). No option-letter
+prefixes, just the raw text.

store/prompts/question_gen/ar_distractor_score.md

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+You are a strict visual grader for a video Action Recognition question.
+
+## Given
+- The question, video frames, and a numbered list of candidate answer texts
+  (the first is the true answer; the rest are distractor candidates — but you
+  are NOT told which is which).
+
+## Task
+For EACH candidate, judge how visually credible it is as an answer given ONLY
+the frames — i.e. how strongly the frames could be read as supporting it.
+Score in [0.0, 1.0]: 1.0 = frames strongly depict this; 0.0 = frames show no
+trace of it (pure negative space).
+
+Judge visual groundedness ONLY. Do NOT reward the option for being the
+"correct" answer — a good distractor is visually credible yet wrong.
+
+## Output
+Respond with ONLY a JSON object mapping 1-based index to score, same order as
+input:
+```json
+{"scores": [0.9, 0.7, 0.6, 0.3, 0.85]}
+```
+Return exactly as many scores as candidates, in order.