feat(digg): rename to 'Digg' and bump per-cluster post limits (#372)

* feat(digg): bump POSTS_PER_CLUSTER to 5 and render limit to 3

Match the per-item enrichment cap and inline-display cap used by the
other sources (Reddit, HN, YouTube, TikTok, GitHub all use 5 fetched /
3 displayed). At the previous 3/2 caps the engine routinely truncated
cluster context — a recent run on cli-printing-press lost the Jason
Calacanis quote tweet entirely because the display cut off after Garry
Tan's first two posts.

* feat(digg): rename 'Digg AI 1000' to 'Digg' in user-facing strings

Drop the 'AI 1000' suffix from the footer line, source label, inline
quote attribution ('via Digg'), why_relevant, container, mock title,
SKILL.md source list, and README sources table. Internal code comments
and docstrings still reference the upstream Digg AI 1000 product.

Bumps version to 3.2.1 and adds a CHANGELOG entry covering this rename
and the POSTS_PER_CLUSTER / render-limit bumps from the prior commit.

---------

Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>

skills/last30days/scripts/lib/digg.py

@@ -44,8 +44,9 @@ ENRICH_CONFIG = {
     "deep": 5,
 }
 
-# X posts pulled per enriched cluster.
-POSTS_PER_CLUSTER = 3
+# X posts pulled per enriched cluster. Matches the 5-comment cap used by
+# Reddit/HN/YouTube/TikTok/GitHub enrichment.
+POSTS_PER_CLUSTER = 5
 
 SEARCH_TIMEOUT = 30
 POSTS_TIMEOUT = 15
@@ -285,9 +286,9 @@ def parse_digg_response(
                 "posts": [],
                 "relevance": round(relevance, 2),
                 "why_relevant": (
-                    f"Digg AI 1000 cluster (rank {rank}, {post_count} posts, {unique_authors} authors)"
+                    f"Digg cluster (rank {rank}, {post_count} posts, {unique_authors} authors)"
                     if rank is not None
-                    else f"Digg AI 1000 cluster ({post_count} posts, {unique_authors} authors)"
+                    else f"Digg cluster ({post_count} posts, {unique_authors} authors)"
                 ),
             }
         )

skills/last30days/scripts/lib/normalize.py

@@ -412,7 +412,7 @@ def _normalize_digg(
     Each cluster is one item. The TLDR carries the most useful body for
     rerank and synthesis. Top-ranked X posts attached at search time are
     passed through under metadata['posts'] so render can emit them as
-    inline 'via Digg AI 1000' quotes.
+    inline 'via Digg' quotes.
     """
     title = str(item.get("title") or "").strip()
     tldr = str(item.get("tldr") or "").strip()
@@ -428,7 +428,7 @@ def _normalize_digg(
         body=body,
         url=str(item.get("url") or f"https://di.gg/ai/{cluster_url_id}"),
         author="",
-        container="Digg AI 1000",
+        container="Digg",
         published_at=item.get("date"),
         date_confidence=_date_confidence(item, from_date, to_date, default="high"),
         engagement=item.get("engagement") or {},

skills/last30days/scripts/lib/pipeline.py

@@ -538,7 +538,7 @@ def _finalize_items_by_source(
         if source == "digg" and items:
             # Pull top-ranked X posts only for the survivors that will appear
             # in the brief. Spending the enrichment budget here (rather than
-            # at retrieval time) keeps the inline 'via Digg AI 1000' quotes
+            # at retrieval time) keeps the inline 'via Digg' quotes
             # paired with the clusters dedupe actually kept.
             digg.enrich_source_items(items, top_k=3)
         finalized[source] = items
@@ -1078,7 +1078,7 @@ def _mock_stream_results(source: str, subquery: schema.SubQuery) -> tuple[list[d
         "digg": [
             {
                 "id": "mock1abc",
-                "title": f"Digg AI 1000 cluster about {subquery.search_query}",
+                "title": f"Digg cluster about {subquery.search_query}",
                 "url": "https://di.gg/ai/mock1abc",
                 "tldr": f"Curated cluster summarizing recent {subquery.search_query} discussion across the AI 1000.",
                 "author": "",

skills/last30days/scripts/lib/render.py

@@ -53,7 +53,7 @@ SOURCE_LABELS = {
     "xiaohongshu": "Xiaohongshu",
     "x": "X",
     "github": "GitHub",
-    "digg": "Digg AI 1000",
+    "digg": "Digg",
     "perplexity": "Perplexity",
 }
 
@@ -853,7 +853,7 @@ def render_full(report: schema.Report) -> str:
                     tc_score = tc.get("score", "")
                     attribution = _comment_attribution(item.source, tc.get("author"))
                     lines.append(f"  Top comment {attribution} ({tc_score} {vote_label}): {excerpt}")
-            # Digg AI 1000: inline X-post quotes attached to the cluster.
+            # Digg: inline X-post quotes attached to the cluster.
             for post in _digg_posts_for(item, limit=3):
                 lines.append(f"  > {_format_digg_quote(post)}")
             # Comment insights for Reddit
@@ -1229,7 +1229,7 @@ _FOOTER_SOURCES: list[tuple[str, str, str, str, list[tuple[str, str]]]] = [
     ("bluesky",     "🦋", "Bluesky",      "post",     [("likes", "likes"), ("reposts", "reposts")]),
     ("truthsocial", "🇺🇸", "Truth Social", "post",     [("likes", "likes"), ("reposts", "reposts")]),
     ("github",      "🐙", "GitHub",       "item",     [("reactions", "reactions"), ("comments", "comments")]),
-    ("digg",        "⛏️", "Digg AI 1000", "cluster",  [("postCount", "posts"), ("uniqueAuthors", "authors")]),
+    ("digg",        "⛏️", "Digg",         "cluster",  [("postCount", "posts"), ("uniqueAuthors", "authors")]),
 ]
 
 
@@ -1684,7 +1684,7 @@ def _comment_insight(item: schema.SourceItem | None) -> str | None:
     return str(insights[0]).strip() or None
 
 
-def _digg_posts_for(item: schema.SourceItem | None, limit: int = 2) -> list[dict]:
+def _digg_posts_for(item: schema.SourceItem | None, limit: int = 3) -> list[dict]:
     """Return up to `limit` parsed Digg posts attached as enrichment to a cluster.
 
     Returns an empty list for non-digg sources or clusters without enrichment.
@@ -1704,17 +1704,17 @@ def _digg_posts_for(item: schema.SourceItem | None, limit: int = 2) -> list[dict
 
 
 def _format_digg_quote(post: dict, body_limit: int = 200) -> str:
-    """Format a Digg-attached X post as an inline 'via Digg AI 1000' quote line."""
+    """Format a Digg-attached X post as an inline 'via Digg' quote line."""
     handle = post.get("username") or ""
     x_url = post.get("x_url") or ""
     body = (post.get("body") or "").replace("\n", " ").strip()
     if len(body) > body_limit:
         body = body[: body_limit - 1].rstrip() + "…"
     if x_url and handle:
-        return f"[@{handle}]({x_url}) via Digg AI 1000: {body}"
+        return f"[@{handle}]({x_url}) via Digg: {body}"
     if handle:
-        return f"@{handle} via Digg AI 1000: {body}"
-    return f"via Digg AI 1000: {body}"
+        return f"@{handle} via Digg: {body}"
+    return f"via Digg: {body}"
 
 
 def _transcript_highlights(item: schema.SourceItem | None) -> list[str]: