Merge origin/main into feat/xiaohongshu-reddit-public-fallback

Resolve conflicts between ScrapeCreators Reddit (main) and
public Reddit fallback (PR #48). Priority: ScrapeCreators ->
OpenAI -> public Reddit fallback.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

scripts/lib/env.py

@@ -221,6 +221,30 @@ def config_exists() -> bool:
     return CONFIG_FILE.exists()
 
 
+def is_reddit_available(config: Dict[str, Any]) -> bool:
+    """Check if Reddit search is available.
+
+    Reddit can use either ScrapeCreators (preferred) or OpenAI.
+    """
+    has_sc = bool(config.get('SCRAPECREATORS_API_KEY'))
+    has_openai = bool(config.get('OPENAI_API_KEY')) and config.get('OPENAI_AUTH_STATUS') == AUTH_STATUS_OK
+    return has_sc or has_openai
+
+
+def get_reddit_source(config: Dict[str, Any]) -> Optional[str]:
+    """Determine which Reddit backend to use.
+
+    Priority: ScrapeCreators (cheaper, faster) > OpenAI (legacy)
+
+    Returns: 'scrapecreators', 'openai', or None
+    """
+    if config.get('SCRAPECREATORS_API_KEY'):
+        return 'scrapecreators'
+    if config.get('OPENAI_API_KEY') and config.get('OPENAI_AUTH_STATUS') == AUTH_STATUS_OK:
+        return 'openai'
+    return None
+
+
 def get_available_sources(config: Dict[str, Any]) -> str:
     """Determine which sources are available.
 
@@ -260,7 +284,7 @@ def get_web_search_source(config: Dict[str, Any]) -> Optional[str]:
 
 
 def get_missing_keys(config: Dict[str, Any]) -> str:
-    """Determine which sources are missing (accounting for Bird).
+    """Determine which sources are missing (accounting for Bird and ScrapeCreators).
 
     Returns: 'all', 'both', 'reddit', 'x', 'web', or 'none'
     """

scripts/lib/reddit.py

@@ -0,0 +1,603 @@
+"""Reddit search via ScrapeCreators API for /last30days.
+
+Uses ScrapeCreators REST API to search Reddit globally, discover relevant
+subreddits, run targeted subreddit searches, and fetch comment trees.
+
+Replaces openai_reddit.py as the primary Reddit search backend.
+Falls back to openai_reddit.py if SCRAPECREATORS_API_KEY is missing but
+OPENAI_API_KEY is present.
+
+Requires SCRAPECREATORS_API_KEY in config (same key as TikTok + Instagram).
+API docs: https://scrapecreators.com/docs
+"""
+
+import re
+import sys
+from collections import Counter
+from datetime import datetime, timezone
+from typing import Any, Dict, List, Optional, Set
+
+try:
+    import requests as _requests
+except ImportError:
+    _requests = None
+
+from . import http
+
+SCRAPECREATORS_BASE = "https://api.scrapecreators.com/v1/reddit"
+
+# Depth configurations: how many API calls per phase
+DEPTH_CONFIG = {
+    "quick": {
+        "global_searches": 1,
+        "subreddit_searches": 2,
+        "comment_enrichments": 3,
+        "timeframe": "week",
+    },
+    "default": {
+        "global_searches": 2,
+        "subreddit_searches": 3,
+        "comment_enrichments": 5,
+        "timeframe": "month",
+    },
+    "deep": {
+        "global_searches": 3,
+        "subreddit_searches": 5,
+        "comment_enrichments": 8,
+        "timeframe": "month",
+    },
+}
+
+# Stopwords for query extraction
+NOISE_WORDS = frozenset({
+    'best', 'top', 'good', 'great', 'awesome', 'killer',
+    'latest', 'new', 'news', 'update', 'updates',
+    'trending', 'hottest', 'popular',
+    'practices', 'features', 'tips',
+    'recommendations', 'advice',
+    'prompt', 'prompts', 'prompting',
+    'methods', 'strategies', 'approaches',
+    'how', 'to', 'the', 'a', 'an', 'for', 'with',
+    'of', 'in', 'on', 'is', 'are', 'what', 'which',
+    'guide', 'tutorial', 'using',
+})
+
+
+def _log(msg: str):
+    """Log to stderr."""
+    sys.stderr.write(f"[Reddit] {msg}\n")
+    sys.stderr.flush()
+
+
+def _sc_headers(token: str) -> Dict[str, str]:
+    """Build ScrapeCreators request headers."""
+    return {
+        "x-api-key": token,
+        "Content-Type": "application/json",
+    }
+
+
+def _extract_core_subject(topic: str) -> str:
+    """Extract core subject from verbose query.
+
+    Strips meta/research words to keep only the core product/concept name.
+    """
+    text = topic.lower().strip()
+
+    # Strip multi-word prefixes
+    prefixes = [
+        'what are the best', 'what is the best', 'what are the latest',
+        'what are people saying about', 'what do people think about',
+        'how do i use', 'how to use', 'how to',
+        'what are', 'what is', 'tips for', 'best practices for',
+    ]
+    for p in prefixes:
+        if text.startswith(p + ' '):
+            text = text[len(p):].strip()
+
+    words = text.split()
+    filtered = [w for w in words if w not in NOISE_WORDS]
+
+    result = ' '.join(filtered) if filtered else text
+    return result.rstrip('?!.')
+
+
+def expand_reddit_queries(topic: str, depth: str) -> List[str]:
+    """Generate multiple Reddit search queries from a topic.
+
+    Uses local logic (no LLM call needed):
+    1. Extract core subject (strip noise words)
+    2. Include original topic if different from core
+    3. For default/deep: add casual/review variant
+    4. For deep: add problem/issues variant
+
+    Returns 1-4 query strings depending on depth.
+    """
+    core = _extract_core_subject(topic)
+    queries = [core]
+
+    # Broader variant: include more context from original topic
+    original_clean = topic.strip().rstrip('?!.')
+    if core.lower() != original_clean.lower() and len(original_clean.split()) <= 8:
+        queries.append(original_clean)
+
+    if depth in ("default", "deep"):
+        queries.append(f"{core} worth it OR thoughts OR review")
+
+    if depth == "deep":
+        queries.append(f"{core} issues OR problems OR bug OR broken")
+
+    return queries
+
+
+# Known utility/meta subreddits that match queries but aren't discussion subs.
+# These get a 0.3x penalty (not banned) in subreddit discovery scoring.
+UTILITY_SUBS = frozenset({
+    'namethatsong', 'findthatsong', 'tipofmytongue',
+    'whatisthissong', 'helpmefind', 'whatisthisthing',
+    'whatsthissong', 'findareddit', 'subredditdrama',
+})
+
+
+def discover_subreddits(
+    results: List[Dict[str, Any]],
+    topic: str = "",
+    max_subs: int = 5,
+) -> List[str]:
+    """Extract top subreddits from global search results with relevance weighting.
+
+    Uses frequency + topic-word matching + utility-sub penalties + engagement
+    bonus to find discussion subs rather than utility/meta subs.
+
+    Args:
+        results: List of post dicts from global search
+        topic: Original search topic (for relevance matching)
+        max_subs: Maximum subreddits to return
+
+    Returns:
+        Top subreddit names sorted by weighted score
+    """
+    core = _extract_core_subject(topic) if topic else ""
+    core_words = set(core.lower().split()) if core else set()
+
+    scores = Counter()
+    for post in results:
+        sub = post.get("subreddit", "")
+        if not sub:
+            continue
+
+        # Base: frequency count
+        base = 1.0
+
+        # Bonus: subreddit name contains a core topic word
+        sub_lower = sub.lower()
+        if core_words and any(w in sub_lower for w in core_words if len(w) > 2):
+            base += 2.0
+
+        # Penalty: known utility/meta subreddits
+        if sub_lower in UTILITY_SUBS:
+            base *= 0.3
+
+        # Bonus: post engagement (high-engagement posts = better sub)
+        ups = post.get("ups") or post.get("score", 0)
+        if ups and ups > 100:
+            base += 0.5
+
+        scores[sub] += base
+
+    return [sub for sub, _ in scores.most_common(max_subs)]
+
+
+def _parse_date(created_utc) -> Optional[str]:
+    """Convert Unix timestamp to YYYY-MM-DD."""
+    if not created_utc:
+        return None
+    try:
+        dt = datetime.fromtimestamp(float(created_utc), tz=timezone.utc)
+        return dt.strftime("%Y-%m-%d")
+    except (ValueError, TypeError, OSError):
+        return None
+
+
+def _normalize_post(post: Dict[str, Any], idx: int, source_label: str = "global") -> Dict[str, Any]:
+    """Normalize a ScrapeCreators Reddit post to our internal format."""
+    permalink = post.get("permalink", "")
+    url = f"https://www.reddit.com{permalink}" if permalink else post.get("url", "")
+
+    # Ensure URL looks like a Reddit thread
+    if url and "reddit.com" not in url:
+        url = ""
+
+    return {
+        "id": f"R{idx}",
+        "reddit_id": post.get("id", ""),
+        "title": str(post.get("title", "")).strip(),
+        "url": url,
+        "subreddit": str(post.get("subreddit", "")).strip(),
+        "date": _parse_date(post.get("created_utc")),
+        "engagement": {
+            "score": post.get("ups") or post.get("score", 0),
+            "num_comments": post.get("num_comments", 0),
+            "upvote_ratio": post.get("upvote_ratio"),
+        },
+        "relevance": 0.7,
+        "why_relevant": f"Reddit {source_label} search",
+        "selftext": str(post.get("selftext", ""))[:500],
+    }
+
+
+def _global_search(
+    query: str,
+    token: str,
+    sort: str = "relevance",
+    timeframe: str = "month",
+) -> List[Dict[str, Any]]:
+    """Search across all of Reddit via ScrapeCreators global search.
+
+    Args:
+        query: Search query
+        token: ScrapeCreators API key
+        sort: Sort order (relevance, hot, top, new)
+        timeframe: Time filter (hour, day, week, month, year, all)
+
+    Returns:
+        List of post dicts
+    """
+    if not _requests:
+        _log("requests library not installed, falling back to urllib")
+        # Use stdlib http module as fallback
+        try:
+            from urllib.parse import urlencode
+            params = urlencode({"query": query, "sort": sort, "timeframe": timeframe})
+            url = f"{SCRAPECREATORS_BASE}/search?{params}"
+            headers = _sc_headers(token)
+            headers["User-Agent"] = http.USER_AGENT
+            data = http.get(url, headers=headers, timeout=30, retries=2)
+            return data.get("posts", data.get("data", []))
+        except Exception as e:
+            _log(f"Global search error (urllib): {e}")
+            return []
+
+    try:
+        resp = _requests.get(
+            f"{SCRAPECREATORS_BASE}/search",
+            params={"query": query, "sort": sort, "timeframe": timeframe},
+            headers=_sc_headers(token),
+            timeout=30,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        return data.get("posts", data.get("data", []))
+    except Exception as e:
+        _log(f"Global search error: {e}")
+        return []
+
+
+def _subreddit_search(
+    subreddit: str,
+    query: str,
+    token: str,
+    sort: str = "relevance",
+    timeframe: str = "month",
+) -> List[Dict[str, Any]]:
+    """Search within a specific subreddit via ScrapeCreators.
+
+    Args:
+        subreddit: Subreddit name (without r/)
+        query: Search query
+        token: ScrapeCreators API key
+        sort: Sort order
+        timeframe: Time filter
+
+    Returns:
+        List of post dicts
+    """
+    if not _requests:
+        try:
+            from urllib.parse import urlencode
+            params = urlencode({
+                "subreddit": subreddit, "query": query,
+                "sort": sort, "timeframe": timeframe,
+            })
+            url = f"{SCRAPECREATORS_BASE}/subreddit/search?{params}"
+            headers = _sc_headers(token)
+            headers["User-Agent"] = http.USER_AGENT
+            data = http.get(url, headers=headers, timeout=30, retries=2)
+            return data.get("posts", data.get("data", []))
+        except Exception as e:
+            _log(f"Subreddit search error (urllib) for r/{subreddit}: {e}")
+            return []
+
+    try:
+        resp = _requests.get(
+            f"{SCRAPECREATORS_BASE}/subreddit/search",
+            params={
+                "subreddit": subreddit,
+                "query": query,
+                "sort": sort,
+                "timeframe": timeframe,
+            },
+            headers=_sc_headers(token),
+            timeout=30,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        return data.get("posts", data.get("data", []))
+    except Exception as e:
+        _log(f"Subreddit search error for r/{subreddit}: {e}")
+        return []
+
+
+def fetch_post_comments(
+    url: str,
+    token: str,
+) -> List[Dict[str, Any]]:
+    """Fetch comments for a Reddit post via ScrapeCreators.
+
+    Args:
+        url: Reddit post URL or permalink
+        token: ScrapeCreators API key
+
+    Returns:
+        List of comment dicts with score, author, body, etc.
+    """
+    if not _requests:
+        try:
+            from urllib.parse import urlencode
+            params = urlencode({"url": url})
+            api_url = f"{SCRAPECREATORS_BASE}/post/comments?{params}"
+            headers = _sc_headers(token)
+            headers["User-Agent"] = http.USER_AGENT
+            data = http.get(api_url, headers=headers, timeout=30, retries=2)
+            return data.get("comments", data.get("data", []))
+        except Exception as e:
+            _log(f"Comment fetch error (urllib): {e}")
+            return []
+
+    try:
+        resp = _requests.get(
+            f"{SCRAPECREATORS_BASE}/post/comments",
+            params={"url": url},
+            headers=_sc_headers(token),
+            timeout=30,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+        return data.get("comments", data.get("data", []))
+    except Exception as e:
+        _log(f"Comment fetch error: {e}")
+        return []
+
+
+def _dedupe_posts(posts: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Deduplicate posts by reddit_id, keeping first occurrence."""
+    seen_ids = set()
+    seen_urls = set()
+    unique = []
+    for post in posts:
+        rid = post.get("reddit_id", "")
+        url = post.get("url", "")
+        if rid and rid in seen_ids:
+            continue
+        if url and url in seen_urls:
+            continue
+        if rid:
+            seen_ids.add(rid)
+        if url:
+            seen_urls.add(url)
+        unique.append(post)
+    return unique
+
+
+def search_reddit(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: str = None,
+) -> Dict[str, Any]:
+    """Full Reddit search: multi-query global discovery + subreddit drill-down.
+
+    This is the main entry point. Replaces openai_reddit.search_reddit().
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+        token: ScrapeCreators API key
+
+    Returns:
+        Dict with 'items' list and optional 'error'.
+    """
+    if not token:
+        return {"items": [], "error": "No SCRAPECREATORS_API_KEY configured"}
+
+    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    timeframe = config["timeframe"]
+
+    # === Phase 1: Query Expansion ===
+    queries = expand_reddit_queries(topic, depth)
+    _log(f"Expanded '{topic}' into {len(queries)} queries: {queries}")
+
+    # === Phase 2: Global Discovery ===
+    all_raw_posts = []
+    max_global = config["global_searches"]
+
+    for i, query in enumerate(queries[:max_global]):
+        sort = "relevance" if i == 0 else "top"
+        _log(f"Global search {i+1}/{max_global}: '{query}' (sort={sort})")
+        posts = _global_search(query, token, sort=sort, timeframe=timeframe)
+        _log(f"  -> {len(posts)} results")
+        all_raw_posts.extend(posts)
+
+    # Normalize all posts
+    all_items = []
+    for i, post in enumerate(all_raw_posts):
+        item = _normalize_post(post, i + 1, "global")
+        all_items.append(item)
+
+    # === Phase 3: Subreddit Discovery + Targeted Search ===
+    discovered_subs = discover_subreddits(all_raw_posts, topic=topic, max_subs=config["subreddit_searches"])
+    _log(f"Discovered subreddits: {discovered_subs}")
+
+    core = _extract_core_subject(topic)
+    for sub in discovered_subs[:config["subreddit_searches"]]:
+        _log(f"Subreddit search: r/{sub} for '{core}'")
+        sub_posts = _subreddit_search(sub, core, token, sort="relevance", timeframe=timeframe)
+        _log(f"  -> {len(sub_posts)} results from r/{sub}")
+        for j, post in enumerate(sub_posts):
+            item = _normalize_post(post, len(all_items) + j + 1, f"r/{sub}")
+            all_items.append(item)
+
+    # === Phase 4: Deduplicate ===
+    all_items = _dedupe_posts(all_items)
+    _log(f"After dedup: {len(all_items)} unique posts")
+
+    # === Phase 5: Date filter ===
+    in_range = []
+    out_of_range = 0
+    for item in all_items:
+        if item["date"] and from_date <= item["date"] <= to_date:
+            in_range.append(item)
+        elif item["date"] is None:
+            in_range.append(item)  # Keep unknown dates
+        else:
+            out_of_range += 1
+
+    if in_range:
+        all_items = in_range
+        if out_of_range:
+            _log(f"Filtered {out_of_range} posts outside date range")
+    else:
+        _log(f"No posts within date range, keeping all {len(all_items)}")
+
+    # === Phase 6: Sort by engagement ===
+    all_items.sort(
+        key=lambda x: (x.get("engagement", {}).get("score", 0) or 0),
+        reverse=True,
+    )
+
+    # Re-index IDs
+    for i, item in enumerate(all_items):
+        item["id"] = f"R{i+1}"
+
+    _log(f"Final: {len(all_items)} Reddit posts")
+    return {"items": all_items}
+
+
+def enrich_with_comments(
+    items: List[Dict[str, Any]],
+    token: str,
+    depth: str = "default",
+) -> List[Dict[str, Any]]:
+    """Enrich top items with comment data from ScrapeCreators.
+
+    Args:
+        items: Reddit items from search_reddit()
+        token: ScrapeCreators API key
+        depth: Depth for comment limit
+
+    Returns:
+        Items with top_comments and comment_insights added.
+    """
+    config = DEPTH_CONFIG.get(depth, DEPTH_CONFIG["default"])
+    max_comments = config["comment_enrichments"]
+
+    if not items or not token:
+        return items
+
+    top_items = items[:max_comments]
+    _log(f"Enriching comments for {len(top_items)} posts")
+
+    for item in top_items:
+        url = item.get("url", "")
+        if not url:
+            continue
+
+        raw_comments = fetch_post_comments(url, token)
+        if not raw_comments:
+            continue
+
+        # Parse comments into our format
+        top_comments = []
+        insights = []
+
+        for ci, c in enumerate(raw_comments[:10]):  # Take top 10 comments
+            body = c.get("body", "")
+            if not body or body in ("[deleted]", "[removed]"):
+                continue
+
+            score = c.get("ups") or c.get("score", 0)
+            author = c.get("author", "[deleted]")
+            permalink = c.get("permalink", "")
+            comment_url = f"https://reddit.com{permalink}" if permalink else ""
+
+            # Top comment gets more room (400 chars) — funny/clever comments need it
+            max_excerpt = 400 if ci == 0 else 300
+            top_comments.append({
+                "score": score,
+                "date": _parse_date(c.get("created_utc")),
+                "author": author,
+                "excerpt": body[:max_excerpt],
+                "url": comment_url,
+            })
+
+            # Extract insights from substantive comments
+            if len(body) >= 30 and author not in ("[deleted]", "[removed]", "AutoModerator"):
+                insight = body[:150]
+                if len(body) > 150:
+                    for i, char in enumerate(insight):
+                        if char in '.!?' and i > 50:
+                            insight = insight[:i+1]
+                            break
+                    else:
+                        insight = insight.rstrip() + "..."
+                insights.append(insight)
+
+        # Sort comments by score
+        top_comments.sort(key=lambda c: c.get("score", 0), reverse=True)
+
+        item["top_comments"] = top_comments[:10]
+        item["comment_insights"] = insights[:10]
+
+    return items
+
+
+def search_and_enrich(
+    topic: str,
+    from_date: str,
+    to_date: str,
+    depth: str = "default",
+    token: str = None,
+) -> Dict[str, Any]:
+    """Full Reddit pipeline: search + comment enrichment.
+
+    This is the convenience function that does everything.
+
+    Args:
+        topic: Search topic
+        from_date: Start date (YYYY-MM-DD)
+        to_date: End date (YYYY-MM-DD)
+        depth: 'quick', 'default', or 'deep'
+        token: ScrapeCreators API key
+
+    Returns:
+        Dict with 'items' list. Items include top_comments and comment_insights.
+    """
+    result = search_reddit(topic, from_date, to_date, depth, token)
+    items = result.get("items", [])
+
+    if items and token:
+        items = enrich_with_comments(items, token, depth)
+        result["items"] = items
+
+    return result
+
+
+def parse_reddit_response(response: Dict[str, Any]) -> List[Dict[str, Any]]:
+    """Parse ScrapeCreators response to item list.
+
+    Compatibility shim matching openai_reddit.parse_reddit_response() signature.
+    """
+    return response.get("items", [])

scripts/lib/reddit_enrich.py

@@ -1,4 +1,9 @@
-"""Reddit thread enrichment with real engagement metrics."""
+"""Reddit thread enrichment with real engagement metrics.
+
+Supports two backends:
+1. ScrapeCreators API (preferred) - no rate limits, 1 credit/call
+2. reddit.com/.json (fallback) - free but 429-prone
+"""
 
 import re
 from typing import Any, Dict, List, Optional
@@ -254,3 +259,67 @@ def enrich_reddit_item(
     item["comment_insights"] = extract_comment_insights(top_comments)
 
     return item
+
+
+def enrich_reddit_item_sc(
+    item: Dict[str, Any],
+    token: str,
+    timeout: int = 30,
+) -> Dict[str, Any]:
+    """Enrich a Reddit item using ScrapeCreators comment API.
+
+    No rate limit risk. Uses 1 credit per call.
+
+    Args:
+        item: Reddit item dict (already has engagement from search)
+        token: ScrapeCreators API key
+        timeout: HTTP timeout
+
+    Returns:
+        Enriched item with top_comments and comment_insights
+    """
+    from . import reddit as reddit_mod
+
+    url = item.get("url", "")
+    if not url:
+        return item
+
+    raw_comments = reddit_mod.fetch_post_comments(url, token)
+    if not raw_comments:
+        return item
+
+    top_comments = []
+    for c in raw_comments[:10]:
+        body = c.get("body", "")
+        if not body or body in ("[deleted]", "[removed]"):
+            continue
+
+        score = c.get("ups") or c.get("score", 0)
+        author = c.get("author", "[deleted]")
+        permalink = c.get("permalink", "")
+        comment_url = f"https://reddit.com{permalink}" if permalink else ""
+
+        top_comments.append({
+            "score": score,
+            "date": dates.timestamp_to_date(c.get("created_utc")) if c.get("created_utc") else None,
+            "author": author,
+            "body": body[:300],
+            "excerpt": body[:200],
+            "url": comment_url,
+        })
+
+    top_comments.sort(key=lambda c: c.get("score", 0), reverse=True)
+
+    item["top_comments"] = []
+    for c in top_comments:
+        item["top_comments"].append({
+            "score": c.get("score", 0),
+            "date": c.get("date"),
+            "author": c.get("author", ""),
+            "excerpt": c.get("excerpt", ""),
+            "url": c.get("url", ""),
+        })
+
+    item["comment_insights"] = extract_comment_insights(top_comments)
+
+    return item

scripts/lib/render.py

@@ -108,11 +108,11 @@ def render_compact(report: schema.Report, limit: int = 15, missing_keys: str = "
         lines.append("**🌐 WEB SEARCH MODE** - assistant will search blogs, docs & news")
         lines.append("")
         lines.append("---")
-        lines.append("**⚡ Want better results?** Add API keys or sign in to Codex to unlock Reddit & X data:")
-        lines.append("- `OPENAI_API_KEY` or `codex login` → Reddit threads with real upvotes & comments")
+        lines.append("**⚡ Want better results?** Add API keys to unlock Reddit, TikTok, Instagram & X data:")
+        lines.append("- `SCRAPECREATORS_API_KEY` → Reddit + TikTok + Instagram (one key, all three!) — real upvotes, comments, views")
         lines.append("- `XAI_API_KEY` → X posts with real likes & reposts")
+        lines.append("- `OPENAI_API_KEY` (legacy) → Reddit threads (slower, higher cost)")
         lines.append("- Edit `~/.config/last30days/.env` to add keys")
-        lines.append("- If already signed in but still seeing this, re-run `codex login`")
         lines.append("---")
         lines.append("")
 
@@ -137,7 +137,7 @@ def render_compact(report: schema.Report, limit: int = 15, missing_keys: str = "
         lines.append("*💡 Tip: Add an xAI key (`XAI_API_KEY`) for X/Twitter data and better triangulation.*")
         lines.append("")
     elif report.mode == "x-only" and missing_keys in ("reddit", "none"):
-        lines.append("*💡 Tip: Add OPENAI_API_KEY or run `codex login` for Reddit data and better triangulation. If already signed in, re-run `codex login`.*")
+        lines.append("*💡 Tip: Add `SCRAPECREATORS_API_KEY` for Reddit + TikTok + Instagram data (one key, all three) and better triangulation.*")
         lines.append("")
 
     # Reddit items
@@ -174,7 +174,15 @@ def render_compact(report: schema.Report, limit: int = 15, missing_keys: str = "
             lines.append(f"  {item.url}")
             lines.append(f"  *{item.why_relevant}*")
 
-            # Top comment insights
+            # Top comment (elevated — Reddit's value IS the comments)
+            if item.top_comments and item.top_comments[0].score >= 10:
+                tc = item.top_comments[0]
+                excerpt = tc.excerpt[:200]
+                if len(tc.excerpt) > 200:
+                    excerpt = excerpt.rstrip() + "..."
+                lines.append(f'  \U0001f4ac Top comment ({tc.score} upvotes): "{excerpt}"')
+
+            # Comment insights
             if item.comment_insights:
                 lines.append("  Insights:")
                 for insight in item.comment_insights[:3]:
@@ -636,6 +644,15 @@ def render_full_report(report: schema.Report) -> str:
                 eng = item.engagement
                 lines.append(f"- **Engagement:** {eng.score or '?'} points, {eng.num_comments or '?'} comments")
 
+            if item.top_comments and item.top_comments[0].score >= 10:
+                tc = item.top_comments[0]
+                excerpt = tc.excerpt[:200]
+                if len(tc.excerpt) > 200:
+                    excerpt = excerpt.rstrip() + "..."
+                lines.append("")
+                lines.append(f'**\U0001f4ac Top Comment** ({tc.score} upvotes, u/{tc.author}):')
+                lines.append(f'> {excerpt}')
+
             if item.comment_insights:
                 lines.append("")
                 lines.append("**Key Insights from Comments:**")

scripts/lib/score.py

@@ -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)