Fix v2 output quality: stats format, Reddit results, citations, summary structure

- Stats: replace BAD/GOOD examples with strict fill-in-the-blank template
- Reddit: add subreddit-targeted fallback search, soften scoring penalties
  (engagement -10→-3, date confidence -10→-5), add minimum result guarantee
- Citations: limit to 1 per insight, short format, no engagement metrics
- Summary: add bold topic headers template for structured paragraphs

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

SKILL.md

@@ -157,27 +157,28 @@ Notable mentions: [other specific things with 1-2 mentions]
 
 **If PROMPTING/NEWS/GENERAL** - Show synthesis and patterns:
 
-**CRITICAL: Every insight MUST cite at least one source.** Use @handle for X posts, r/subreddit for Reddit. This proves you're using real research, not making things up.
+CITATION RULE: Cite sources sparingly to prove research is real.
+- In the "What I learned" intro: cite 1-2 top sources total, not every sentence
+- In KEY PATTERNS: cite 1 source per pattern, short format: "per @handle" or "per r/sub"
+- Do NOT include engagement metrics in citations (likes, upvotes) - save those for stats box
+- Do NOT chain multiple citations: "per @x, @y, @z" is too much. Pick the strongest one.
 
-**BAD (no attribution):**
+**BAD:** "His album is set for March 20 (per @cocoabutterbf; Rolling Stone; HotNewHipHop; Complex)."
-```
+**GOOD:** "His album BULLY is set for March 20 via Gamma, per Rolling Stone."
-His 12th studio album is now set for March 20, 2026 via a new deal with Gamma.
-```
-
-**GOOD (cites sources):**
-```
-His 12th studio album BULLY is set for March 20, 2026 via Gamma (per @XXX, 15 likes; r/kanye thread with 200 upvotes).
-```
 
 ```
 What I learned:
 
-[2-4 sentences synthesizing key insights. EVERY claim cites @handle or r/subreddit.]
+**{Topic 1}** — [1-2 sentences about this storyline, per source]
+
+**{Topic 2}** — [1-2 sentences, per source]
+
+**{Topic 3}** — [1-2 sentences, per source]
 
 KEY PATTERNS from the research:
-1. [Pattern from research] - per @handle, r/sub
+1. [Pattern] — per @handle
-2. [Pattern from research] - per @handle
+2. [Pattern] — per r/sub
-3. [Pattern from research] - per r/sub
+3. [Pattern] — per source
 ```
 
 **THEN - Stats (right before invitation):**
@@ -187,34 +188,21 @@ KEY PATTERNS from the research:
 - Sum engagement: parse `[Xlikes, Yrt]` from each X post, `[Xpts, Ycmt]` from Reddit
 - Identify top voices: highest-engagement @handles from X, most active subreddits
 
-**You MUST use this EXACT format with these EXACT emoji characters. Do NOT use markdown tables. Do NOT use plain text dashes. Copy this template character-for-character:**
+**Copy this EXACTLY, replacing only the {placeholders}:**
 
 ```
 ---
 ✅ All agents reported back!
-├─ 🟠 Reddit: {n} threads │ {sum} upvotes │ {sum} comments
+├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
-├─ 🔵 X: {n} posts │ {sum} likes │ {sum} reposts (via Bird/xAI)
+├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts (via Bird/xAI)
-├─ 🌐 Web: {n} pages │ {domains}
+├─ 🌐 Web: {N} pages │ {domain1}, {domain2}, {domain3}
-└─ 🗣️ Top voices: @{handle1} ({n}K likes), @{handle2} │ r/{sub1}, r/{sub2}
+└─ 🗣️ Top voices: @{handle1} ({N} likes), @{handle2} │ r/{sub1}, r/{sub2}
-```
-
-**BAD (DO NOT DO THIS):**
-```
----All agents reported back!
-- Reddit: 10 threads | 54 upvotes
-- X: 3 posts | 21 likes
-```
-
-**GOOD (DO THIS):**
-```
 ---
-✅ All agents reported back!
-├─ 🟠 Reddit: 10 threads │ 54 upvotes │ 144 comments
-├─ 🔵 X: 3 posts │ 21 likes │ 10 reposts (via Bird)
-├─ 🌐 Web: 20 pages │ digitalocean.com, dev.to, medium.com
-└─ 🗣️ Top voices: @yhemi0pe (10 likes, 10rt), @0x1BMW │ r/openclaw, r/AI_Agents
 ```
 
+If Reddit returned 0 threads, write: "├─ 🟠 Reddit: 0 threads (no results this cycle)"
+NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji.
+
 **LAST - Invitation:**
 ```
 ---

docs/plans/2026-02-06-fix-last30days-v2-formatting-reddit-citations-plan.md

@@ -0,0 +1,167 @@
+---
+title: "fix: last30days v2 formatting, Reddit results, and citation verbosity"
+type: fix
+date: 2026-02-06
+---
+
+# fix: last30days v2 Formatting, Reddit Results, and Citation Verbosity
+
+## Overview
+
+Four bugs found during v2 testing across 4 queries (kanye west, howie.ai, nano banana pro prompting, open claw). The skill IS executing (the agent:Explore removal worked) but output quality has regressed from v1.
+
+## Problem Statement
+
+| # | Bug | Severity | Where |
+|---|-----|----------|-------|
+| 1 | Stats emoji tree format ignored 3/4 times - agent renders plain text dashes instead | High | `SKILL.md` |
+| 2 | Reddit returns 0 results for popular topics (kanye west, howie.ai) | High | `scripts/lib/openai_reddit.py` |
+| 3 | Citations too verbose - every sentence has `(per @x, @y, @z; r/sub)` making summary unreadable | Medium | `SKILL.md` |
+| 4 | Kanye summary is wall of text - no bold headers or paragraph breaks like nano banana pro got | Medium | `SKILL.md` |
+
+## Proposed Fixes
+
+### Fix 1: Stats Emoji Format Enforcement
+
+**Root cause:** The agent ignores the emoji tree template even with BAD/GOOD examples. The template uses box-drawing characters (├─ └─) that the agent treats as decorative, not mandatory.
+
+**Approach:** Instead of relying on the agent to copy box-drawing characters, provide the template as a **literal fill-in-the-blank** with placeholders that are impossible to misinterpret.
+
+**File:** `SKILL.md` (stats section, currently around line 190)
+
+**Change:** Replace the current template + BAD/GOOD examples with a single, strict fill-in format:
+
+```
+Copy this EXACTLY, replacing only the {placeholders}:
+
+---
+✅ All agents reported back!
+├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
+├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts (via Bird/xAI)
+├─ 🌐 Web: {N} pages │ {domain1}, {domain2}, {domain3}
+└─ 🗣️ Top voices: @{handle1} ({N} likes), @{handle2} │ r/{sub1}, r/{sub2}
+---
+
+If Reddit returned 0 threads, write: "├─ 🟠 Reddit: 0 threads (no results this cycle)"
+NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji.
+```
+
+Remove the separate BAD/GOOD section (it adds length without helping).
+
+### Fix 2: Reddit Returning 0 Results
+
+**Root cause (from code analysis):**
+
+1. `openai_reddit.py:53-93` - The `REDDIT_SEARCH_PROMPT` instructs the OpenAI model to strip noise words before searching. For "kanye west" this isn't the issue (no noise words), but for "howie.ai" it might strip "ai".
+
+2. `openai_reddit.py:160-166` - The search is restricted to `allowed_domains: ["reddit.com"]` which depends on OpenAI's web_search indexing of Reddit.
+
+3. `last30days.py:474-490` - Post-retrieval filtering: `normalize.filter_by_date_range()` + `score.score_reddit_items()` + `dedupe.dedupe_reddit()` can discard all results if date confidence is low.
+
+4. `score.py:151-157` - Items with no engagement metrics get `-10` penalty, low date confidence gets `-10`. Combined that's `-20` which may push score below threshold.
+
+**Approach (multi-layered):**
+
+**A. Add subreddit-targeted search fallback** in `openai_reddit.py`:
+- When the first search returns < 3 results, add a second search prompt that explicitly queries: `"r/{topic} site:reddit.com"` and `"{topic} subreddit site:reddit.com"`
+- This catches cases where OpenAI's web_search doesn't find the obvious subreddit
+
+**B. Soften post-retrieval scoring** in `score.py`:
+- Change the no-engagement penalty from `-10` to `-3` (missing metrics ≠ irrelevant)
+- Change low date confidence penalty from `-10` to `-5`
+
+**C. Add minimum result guarantee** in `last30days.py`:
+- If scoring filters out ALL results, keep the top 3 by raw relevance regardless of score
+- Log a warning: "All Reddit results scored below threshold, keeping top 3 by relevance"
+
+**Files to change:**
+- `scripts/lib/openai_reddit.py` - Add subreddit fallback search (lines ~160-180)
+- `scripts/lib/score.py` - Soften penalties (lines ~151-157)
+- `scripts/last30days.py` - Add minimum result guarantee (lines ~474-490)
+
+### Fix 3: Citations Too Verbose
+
+**Root cause:** The SKILL.md instruction says "Every insight MUST cite at least one source" with a GOOD example showing `(per @XXX, 15 likes; r/kanye thread with 200 upvotes)` - this is too much detail per citation and the agent over-applies it.
+
+**Approach:** Dial back to "cite 1-2 sources per KEY PATTERN, not per sentence. Use short format."
+
+**File:** `SKILL.md` (citation section, currently around line 158)
+
+**Change the citation rule to:**
+
+```
+CITATION RULE: Cite sources sparingly to prove research is real.
+- In the "What I learned" intro: cite 1-2 top sources total, not every sentence
+- In KEY PATTERNS: cite 1 source per pattern, short format: "per @handle" or "per r/sub"
+- Do NOT include engagement metrics in citations (likes, upvotes) - save those for stats box
+- Do NOT chain multiple citations: "per @x, @y, @z" is too much. Pick the strongest one.
+
+BAD: "His album is set for March 20 (per @cocoabutterbf; Rolling Stone; HotNewHipHop; Complex)."
+GOOD: "His album BULLY is set for March 20 via Gamma, per Rolling Stone."
+```
+
+### Fix 4: Summary Formatting (Wall of Text vs Structured)
+
+**Root cause:** The SKILL.md template for PROMPTING/NEWS/GENERAL shows:
+```
+What I learned:
+[2-4 sentences synthesizing...]
+```
+
+This gives the agent permission to write a dense paragraph. The nano banana pro test got good formatting because PROMPTING queries naturally produce structured patterns. NEWS queries (kanye) produce narratives that become walls of text.
+
+**Approach:** Add explicit structure to the NEWS/GENERAL format with bold topic headers.
+
+**File:** `SKILL.md` (summary display section, around line 158)
+
+**Change the PROMPTING/NEWS/GENERAL template to:**
+
+```
+What I learned:
+
+**{Topic 1}** — [1-2 sentences about this storyline, per source]
+
+**{Topic 2}** — [1-2 sentences, per source]
+
+**{Topic 3}** — [1-2 sentences, per source]
+
+KEY PATTERNS from the research:
+1. [Pattern] — per @handle
+2. [Pattern] — per r/sub
+3. [Pattern] — per source
+```
+
+The bold topic headers force structure. Each topic gets its own paragraph with a line break. No more wall-of-text narratives.
+
+## Acceptance Criteria
+
+- [ ] **Fix 1:** Stats box uses emoji tree format ├─ 🟠 🔵 🌐 └─ 🗣️ in 4/4 test queries
+- [ ] **Fix 2:** "kanye west" returns >0 Reddit threads (r/kanye exists and is active)
+- [ ] **Fix 3:** Summary citations are 1 per insight, short format, no engagement metrics inline
+- [ ] **Fix 4:** NEWS/GENERAL summaries use bold topic headers with paragraph breaks, not wall of text
+
+## Test Plan
+
+Re-run the same 4 queries after fixes:
+1. `/last30days kanye west` — NEWS: should get Reddit results, structured summary, emoji stats
+2. `/last30days howie.ai` — GENERAL: should get Reddit if available, citations not verbose
+3. `/last30days nano banana pro prompting` — PROMPTING: should maintain current good quality, reduce citation density
+4. `/last30days open claw` — GENERAL: should cite @handles in summary, emoji stats
+
+## Files to Modify
+
+| File | Fix | Change |
+|------|-----|--------|
+| `SKILL.md` | 1, 3, 4 | Stats template, citation rules, summary structure |
+| `scripts/lib/openai_reddit.py` | 2 | Add subreddit fallback search |
+| `scripts/lib/score.py` | 2 | Soften scoring penalties |
+| `scripts/last30days.py` | 2 | Add minimum result guarantee |
+
+## References
+
+- Current SKILL.md: `~/.claude/skills/last30days/SKILL.md`
+- Private repo: `/Users/mvanhorn/last30days-skill-private/`
+- Old working SKILL.md: `~/.claude/skills/last30days.backup-v1/SKILL.md`
+- Reddit search module: `scripts/lib/openai_reddit.py:53-93` (prompt), `:160-166` (API call)
+- Scoring module: `scripts/lib/score.py:151-157` (penalties)
+- Main pipeline: `scripts/last30days.py:474-490` (filtering)

scripts/last30days.py

@@ -114,6 +114,25 @@ def _search_reddit(
             except Exception:
                 pass
 
+    # Subreddit-targeted fallback if still < 3 results
+    if len(reddit_items) < 3 and not mock and not reddit_error:
+        sub_query = openai_reddit._build_subreddit_query(topic)
+        try:
+            sub_raw = openai_reddit.search_reddit(
+                config["OPENAI_API_KEY"],
+                selected_models["openai"],
+                sub_query,
+                from_date, to_date,
+                depth=depth,
+            )
+            sub_items = openai_reddit.parse_reddit_response(sub_raw)
+            existing_urls = {item.get("url") for item in reddit_items}
+            for item in sub_items:
+                if item.get("url") not in existing_urls:
+                    reddit_items.append(item)
+        except Exception:
+            pass
+
     return reddit_items, raw_openai, reddit_error
 
 
@@ -488,6 +507,13 @@ def main():
     deduped_reddit = dedupe.dedupe_reddit(sorted_reddit)
     deduped_x = dedupe.dedupe_x(sorted_x)
 
+    # Minimum result guarantee: if all Reddit results were filtered out but
+    # we had raw results, keep top 3 by relevance regardless of score
+    if not deduped_reddit and normalized_reddit:
+        print("[REDDIT WARNING] All results scored below threshold, keeping top 3 by relevance", file=sys.stderr)
+        by_relevance = sorted(normalized_reddit, key=lambda item: item.relevance, reverse=True)
+        deduped_reddit = by_relevance[:3]
+
     progress.end_processing()
 
     # Create report

scripts/lib/openai_reddit.py

@@ -103,6 +103,18 @@ def _extract_core_subject(topic: str) -> str:
     return ' '.join(result[:3]) or topic  # Keep max 3 words
 
 
+def _build_subreddit_query(topic: str) -> str:
+    """Build a subreddit-targeted search query for fallback.
+
+    When standard search returns few results, try searching for the
+    subreddit itself: 'r/kanye', 'r/howie', etc.
+    """
+    core = _extract_core_subject(topic)
+    # Remove dots and special chars for subreddit name guess
+    sub_name = core.replace('.', '').replace(' ', '').lower()
+    return f"r/{sub_name} site:reddit.com"
+
+
 def search_reddit(
     api_key: str,
     model: str,

scripts/lib/score.py

@@ -21,7 +21,7 @@ WEBSEARCH_NO_DATE_PENALTY = 20  # Heavy penalty for no date signals (low confide
 
 # Default engagement score for unknown
 DEFAULT_ENGAGEMENT = 35
-UNKNOWN_ENGAGEMENT_PENALTY = 10
+UNKNOWN_ENGAGEMENT_PENALTY = 3
 
 
 def log1p_safe(x: Optional[int]) -> float:
@@ -152,9 +152,9 @@ def score_reddit_items(items: List[schema.RedditItem]) -> List[schema.RedditItem
 
         # Apply penalty for low date confidence
         if item.date_confidence == "low":
-            overall -= 10
-        elif item.date_confidence == "med":
             overall -= 5
+        elif item.date_confidence == "med":
+            overall -= 2
 
         item.score = max(0, min(100, int(overall)))
 
@@ -212,9 +212,9 @@ def score_x_items(items: List[schema.XItem]) -> List[schema.XItem]:
 
         # Apply penalty for low date confidence
         if item.date_confidence == "low":
-            overall -= 10
-        elif item.date_confidence == "med":
             overall -= 5
+        elif item.date_confidence == "med":
+            overall -= 2
 
         item.score = max(0, min(100, int(overall)))