Ran 15-way blinded comparison (5 topics x 3 versions). CROSS won all 5 topics
(4.74/5.0 avg vs HN 4.10, Base 3.73). Then improved CROSS further:
- dedupe.py: hybrid similarity (token+trigram Jaccard) at 0.40 threshold,
cross-source links went from 3 to 13 items across 5 topics
- render.py: [xref: HN5, HN4] -> [also on: HN, Reddit] for human-readable tags
- youtube_yt.py: SYNONYMS dict so "hip hop" matches "rap" (0.33 -> 0.71 score)
- SKILL.md: instruction #7 tells Claude to lead with cross-platform signals
Validation: improved CROSS scores 4.38/5.0 vs original 3.98 (+0.40), wins 4/5
topics. Biggest gains in specificity (+0.8) and format compliance (+1.0).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube videos now get real relevance scores based on token overlap
between the search query and video title (was hardcoded at 0.7).
Uses ratio overlap with stopword removal, floored at 0.1.
Cross-source linking annotates items that discuss the same story
across different platforms (e.g., Reddit + HN + X). Items get
bidirectional cross_refs displayed as [xref: R3, HN5] in compact
output so Claude can triangulate multi-platform coverage.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Remove 2>&1 from SKILL.md so stderr doesn't pollute model input
- Run script in foreground (not background) with 5min timeout
- Add explicit YouTube synthesis instruction for Claude
- Remove --flat-playlist which broke date filtering (all dates were None)
- Move date filtering to Python with soft fallback for evergreen topics
- Keep 'tips', 'tutorial', 'review', 'guide' in YouTube search queries
- Increase yt-dlp timeout from 60s to 120s for full metadata fetch
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
YouTube search and transcript extraction runs automatically when yt-dlp
is installed. Searches for topic videos from the last N days, fetches
auto-generated transcripts for top results, and feeds them through the
same scoring pipeline (relevance + recency + engagement) as Reddit/X.
New files:
- youtube_yt.py: search, transcript extraction, VTT cleanup
Modified files:
- schema.py: YouTubeItem dataclass, updated Report
- normalize.py: normalize_youtube_items()
- score.py: YouTube engagement scoring (views-dominated)
- dedupe.py: YouTube deduplication
- render.py: YouTube section in compact output
- env.py: is_ytdlp_available() check
- ui.py: YouTube progress messages
- last30days.py: _search_youtube(), parallel execution with Reddit/X
- SKILL.md: YouTube in stats box, citation priority
- README.md: YouTube docs, yt-dlp requirement, Peter shoutout
Inspired by Peter Steinberger's yt-dlp + summarize toolchain approach.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Matt Van Horn