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>

.claude-plugin/marketplace.json

@@ -11,7 +11,7 @@
     {
       "name": "last30days",
       "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and 5+ more sources. AI agent scores by upvotes, likes, and real money - not editors.",
-      "version": "3.2.0",
+      "version": "3.2.1",
       "author": {
         "name": "Matt Van Horn",
         "url": "https://github.com/mvanhorn"

.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "last30days",
-  "version": "3.2.0",
+  "version": "3.2.1",
   "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and 5+ more sources. AI agent scores by upvotes, likes, and real money - not editors.",
   "author": {
     "name": "Matt Van Horn",

.codex-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "last30days",
-  "version": "3.2.0",
+  "version": "3.2.1",
   "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, Hacker News, Polymarket, GitHub, and 5+ more sources. AI agent scores by upvotes, likes, and real money - not editors.",
   "author": {
     "name": "Matt Van Horn",

CHANGELOG.md

@@ -7,6 +7,11 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Changed
+
+- Rename "Digg AI 1000" to just "Digg" in user-facing output (footer line, source label, inline-quote suffix, why_relevant, container attribution). Internal references to the upstream Digg AI 1000 product remain in code comments and docstrings.
+- Bump `POSTS_PER_CLUSTER` from 3 to 5 and the render-side display limit from 2 to 3 to match the per-source enrichment caps used by Reddit, HN, YouTube, TikTok, and GitHub. The previous 3/2 caps routinely truncated cluster context (e.g. dropped a Jason Calacanis quote tweet on a `cli-printing-press` run).
+
 ## [3.2.0] - 2026-05-09
 
 ### Added

README.md

@@ -68,7 +68,7 @@ If you're meeting with a CEO, have you read all their tweets and YouTube transcr
 | **Hacker News** | The developer consensus. 825 points, 899 comments. Where technical people actually argue. |
 | **Polymarket** | Not opinions. Odds. Backed by real money. 96% confidence on album sales. 4% on an acquisition. |
 | **GitHub** | For people: PR velocity, top repos by stars, release notes. For topics: issues and discussions. |
-| **Digg AI 1000** | Curated story clusters from ~1000 high-signal AI accounts on X, with attributable inline quotes (no X auth required). Auto-enabled when `digg-pp-cli` is on PATH. |
+| **Digg** | Curated story clusters from Digg's AI 1000 leaderboard (~1000 high-signal AI accounts on X), with attributable inline quotes (no X auth required). Auto-enabled when `digg-pp-cli` is on PATH. |
 | **Threads** | The post-Twitter text layer. Conversations from creators and brands. |
 | **Pinterest** | Visual discovery. Pins, saves, and comments on products and ideas. |
 | **Bluesky** | The decentralized social layer. AT Protocol posts from the post-Twitter migration. |

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "last30days-skill"
-version = "3.2.0"
+version = "3.2.1"
 description = "Multi-source last-30-days research skill"
 readme = "README.md"
 requires-python = ">=3.12"

skills/last30days/SKILL.md

@@ -1,6 +1,6 @@
 ---
 name: last30days
-version: "3.2.0"
+version: "3.2.1"
 description: "Research what people actually say about any topic in the last 30 days. Pulls posts and engagement from Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub, and the web."
 argument-hint: 'last30days nvidia earnings reaction | last30days AI video tools | last30days what users want in react'
 allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
@@ -234,7 +234,7 @@ If your Bash call to `last30days.py` does NOT include the FULL pre-flight checkl
 
 ---
 
-# last30days v3.2.0: Research Any Topic from the Last 30 Days
+# last30days v3.2.1: Research Any Topic from the Last 30 Days
 
 > **Permissions overview:** Reads public web/platform data and optionally saves research briefings to `LAST30DAYS_MEMORY_DIR` (defaults to `~/Documents/Last30Days`). X/Twitter search uses optional user-provided tokens (AUTH_TOKEN/CT0 env vars). Bluesky search uses optional app password (BSKY_HANDLE/BSKY_APP_PASSWORD env vars - create at bsky.app/settings/app-passwords). All credential usage and data writes are documented in the [Security & Permissions](#security--permissions) section.
 
@@ -318,7 +318,7 @@ Common patterns:
 
 - Always active: Reddit, Hacker News, Polymarket
 - If gh CLI is installed (check `which gh`): add GitHub
-- If digg-pp-cli is installed (check `which digg-pp-cli`): add Digg AI 1000
+- If digg-pp-cli is installed (check `which digg-pp-cli`): add Digg
 - If AUTH_TOKEN/CT0 or XAI_API_KEY or FROM_BROWSER is set, or xurl CLI is installed and authenticated: add X
 - If yt-dlp is installed (check `which yt-dlp`): add YouTube
 - If SCRAPECREATORS_API_KEY is set and INCLUDE_SOURCES contains tiktok: add TikTok
@@ -826,7 +826,7 @@ Only show lines for platforms where something was resolved. Skip empty lines. On
 - For how_to: prioritize YouTube (tutorials) and Reddit (guides)
 - Primary subquery weight = 1.0, secondary = 0.6-0.8, peripheral = 0.3-0.5
 
-**Available sources (include ALL in primary subquery):** reddit, x, youtube, tiktok, instagram, hackernews, polymarket. Optional: bluesky, truthsocial, threads, pinterest, grounding (web search - only if user has Brave/Exa/Serper key), digg (Digg AI 1000 clusters - only if `digg-pp-cli` is on PATH)
+**Available sources (include ALL in primary subquery):** reddit, x, youtube, tiktok, instagram, hackernews, polymarket. Optional: bluesky, truthsocial, threads, pinterest, grounding (web search - only if user has Brave/Exa/Serper key), digg (Digg clusters - only if `digg-pp-cli` is on PATH)
 
 **Intent → freshness_mode mapping:**
 - breaking_news, prediction → `strict_recent`

skills/last30days/scripts/lib/digg.py

@@ -44,8 +44,9 @@ ENRICH_CONFIG = {
     "deep": 5,
 }
 
-# X posts pulled per enriched cluster.
+# X posts pulled per enriched cluster. Matches the 5-comment cap used by
-POSTS_PER_CLUSTER = 3
+# 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"@{handle} via Digg: {body}"
-    return f"via Digg AI 1000: {body}"
+    return f"via Digg: {body}"
 
 
 def _transcript_highlights(item: schema.SourceItem | None) -> list[str]: