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feat(reddit): elevate top comments, improve subreddit discovery, default to ScrapeCreators

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Three improvements from beta testing:

1. Top comments: 10% scoring weight for comment quality, 💬 top comment
   rendered prominently in compact/full output, increased insight limits
2. Subreddit discovery: relevance-weighted scoring with topic word matching,
   utility sub penalties (UTILITY_SUBS blocklist), engagement bonus
3. Default method: SKILL.md primaryEnv → SCRAPECREATORS_API_KEY, web-only
   banner recommends SC first, security section updated

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
Matt Van Horn
2026-03-05 17:29:18 -08:00
parent 30b973f62e
commit 7048fe7b83
4 changed files with 101 additions and 29 deletions
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scripts/lib/score.py
+17 -5
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@@ -31,10 +31,16 @@ def log1p_safe(x: Optional[int]) -> float:
return math.log1p(x)
def compute_reddit_engagement_raw(engagement: Optional[schema.Engagement]) -> Optional[float]:
def compute_reddit_engagement_raw(
engagement: Optional[schema.Engagement],
top_comment_score: Optional[int] = None,
) -> Optional[float]:
"""Compute raw engagement score for Reddit item.
Formula: 0.55*log1p(score) + 0.40*log1p(num_comments) + 0.05*(upvote_ratio*10)
Formula: 0.50*log1p(score) + 0.35*log1p(num_comments) + 0.05*(upvote_ratio*10) + 0.10*log1p(top_comment_score)
The 10% comment quality weight rewards posts where the community engaged deeply
— a highly upvoted top comment means the thread sparked real discussion.
"""
if engagement is None:
return None
@@ -45,8 +51,9 @@ def compute_reddit_engagement_raw(engagement: Optional[schema.Engagement]) -> Op
score = log1p_safe(engagement.score)
comments = log1p_safe(engagement.num_comments)
ratio = (engagement.upvote_ratio or 0.5) * 10
top_cmt = log1p_safe(top_comment_score)
return 0.55 * score + 0.40 * comments + 0.05 * ratio
return 0.50 * score + 0.35 * comments + 0.05 * ratio + 0.10 * top_cmt
def compute_x_engagement_raw(engagement: Optional[schema.Engagement]) -> Optional[float]:
@@ -113,8 +120,13 @@ def score_reddit_items(items: List[schema.RedditItem]) -> List[schema.RedditItem
if not items:
return items
# Compute raw engagement scores
eng_raw = [compute_reddit_engagement_raw(item.engagement) for item in items]
# Compute raw engagement scores (with top comment quality signal)
eng_raw = []
for item in items:
top_cmt_score = None
if item.top_comments:
top_cmt_score = item.top_comments[0].score
eng_raw.append(compute_reddit_engagement_raw(item.engagement, top_cmt_score))
# Normalize engagement to 0-100
eng_normalized = normalize_to_100(eng_raw)