Add frame dedup and Whisper auto-chunking

Frame extraction now runs a perceptual dedup pass (default on, --no-dedup
to disable) that drops near-identical frames before the budget cap, so the
budget goes to distinct content instead of held slides/static recordings.
The Frames report line notes how many near-duplicates were dropped.

Whisper transcription splits audio over the 25 MB upload cap into evenly
sized chunks, transcribes each, shifts segment timestamps back into source
time, and tolerates partial failures (only fails if every chunk fails) —
length alone no longer fails transcription.

token-burner is exempt from the long-video sparse-scan warning since it
keeps every scene-change frame. README/SKILL.md updated. Adds test_dedup.py and test_whisper.py.

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

skills/watch/SKILL.md

@@ -149,6 +149,7 @@ Optional flags:
 - `--out-dir DIR` — keep working files somewhere specific (default: an auto-generated tmp dir)
 - `--whisper groq|openai` — force a specific Whisper backend (default: prefer Groq if both keys exist)
 - `--no-whisper` — disable the Whisper fallback entirely (frames-only if no captions)
+- `--no-dedup` — keep near-duplicate frames. By default a frame-delta pass drops frames that are visually near-identical to the previous kept one (held slides, static screen recordings, paused video) so the frame budget goes to distinct content; the report's **Frames** line notes how many were dropped. Pass this only if the user needs every sampled frame (e.g. judging subtle frame-to-frame motion).
 
 ### Focusing on a section (higher frame rate)
 
@@ -234,7 +235,7 @@ Both keys live in `~/.config/watch/.env`. The script prefers Groq when both are
 - **No transcript available** → captions missing AND (no Whisper key OR Whisper API failed). Script prints a hint pointing to setup. Proceed frames-only and tell the user.
 - **Long video warning printed** → acknowledge it in your answer. Offer to re-run focused on a specific section via `--start`/`--end` rather than a sparse full-video scan.
 - **Download fails** → yt-dlp's error goes to stderr. If it's a login-required or region-locked video, tell the user plainly; do not keep retrying.
-- **Whisper request fails** → the error is printed to stderr (likely: invalid key, rate limit, or 25 MB upload limit on a very long video). The report will say "none available" for transcript. You can retry with `--whisper openai` if Groq failed (or vice versa).
+- **Whisper request fails** → the error is printed to stderr (likely: invalid key or rate limit). Audio over the API's 25 MB upload cap is split into chunks and transcribed automatically, so length alone won't fail it; if some chunks fail the transcript is partial and the dropped chunks are noted on stderr. The report will say "none available" only if every chunk fails. You can retry with `--whisper openai` if Groq failed (or vice versa).
 
 ## Token efficiency