feat: release v3.0.6 - promote plans 003-009 from private beta to public (#277)

Consolidates seven beta-validated plans into the public release. Validated
on nine+ topics across GENERAL, COMPARISON, RECOMMENDATIONS, and
demographic-shopping classes before ship.

Plans bundled in this release:

- 003 Engine-emitted Pre-Research Status warning + Polymarket summarization
  + VOICE CONTRACT LAW 1-5 + Step 0.55 MANDATORY
- 004 WebSearch deferred-tool loading (ToolSearch STEP 0) + LAW 5 universal
  + top-of-file imperative
- 005 Supplement floor (2-3 minimum) separate from Step 0.55 pre-research
- 006 Step 2.5 MANDATORY raw-file append with canonical format example +
  count-equality self-check
- 007 Restored April 9 canonical comparison template with Quick Verdict,
  per-entity Strengths/Weaknesses, 9-axis Head-to-Head, Bottom Line,
  emerging stack + LAW 2/4 COMPARISON exceptions
- 008 Person-topic GitHub handle resolution MANDATORY + LAW 1 reinforcement
  at Step 2 tail and Step 2.5 entry + RECOMMENDATIONS signal-weighted
  ranking rewrite + Polymarket post-merge topic filter (engine change,
  filter_items_against_topic helper + vs/versus in _NOISE_WORDS)
- 009 Unified pre-flight CHECKLIST + VOICE CONTRACT formatting-authority
  preface + Step 0.45 Query Quality Pre-Flight (4 keyword-trap classes) +
  post-synthesis Sources-block self-check

Beta validation topics (2026-04-18): Kanye West, Matt Van Horn, CLI vs MCP,
OpenClaw vs Paperclip vs Hermes, Paperclip vs Hermes vs Open Claw, Garry
Tan, Israel vs Lebanon, Best programming language for AI agents, Peter
Steinberger post plan 009, Birthday gift for 42 year old man (Class 1
pre-flight fired correctly), Vincent Koc (passed).

No breaking changes. No new CLI flags. No new public API. Plugin name
(last30days) and marketplace name (last30days-skill) unchanged.

Co-authored-by: Matt Van Horn <455140+mvanhorn@users.noreply.github.com>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.claude-plugin/marketplace.json

@@ -10,7 +10,7 @@
     {
       "name": "last30days",
       "description": "Research any topic across Reddit, X, YouTube, TikTok, Instagram, HN, Polymarket, GitHub, and 5+ more sources.",
-      "version": "3.0.5",
+      "version": "3.0.6",
       "author": {
         "name": "Matt Van Horn",
         "url": "https://github.com/mvanhorn"

.claude-plugin/plugin.json

@@ -1,6 +1,6 @@
 {
   "name": "last30days",
-  "version": "3.0.5",
+  "version": "3.0.6",
   "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",

scripts/last30days.py

@@ -112,16 +112,36 @@ def save_output(report: schema.Report, emit: str, save_dir: str, suffix: str = "
     return out_path
 
 
-def emit_output(report: schema.Report, emit: str, fun_level: str = "medium") -> str:
+def emit_output(report: schema.Report, emit: str, fun_level: str = "medium", save_path: str | None = None) -> str:
     if emit == "json":
         return json.dumps(schema.to_dict(report), indent=2, sort_keys=True)
     if emit in {"compact", "md"}:
-        return render.render_compact(report, fun_level=fun_level)
+        return render.render_compact(report, fun_level=fun_level, save_path=save_path)
     if emit == "context":
         return render.render_context(report)
     raise SystemExit(f"Unsupported emit mode: {emit}")
 
 
+def compute_save_path_display(save_dir: str, topic: str, suffix: str, emit: str) -> str:
+    """Compute the user-friendly save path string that will be shown in the footer.
+
+    Uses ~ for the home directory so the footer reads "~/Documents/Last30Days/slug-raw.md"
+    instead of an absolute machine-local path.
+    """
+    from pathlib import Path as _Path
+    path = _Path(save_dir).expanduser().resolve()
+    slug = slugify(topic)
+    extension = "json" if emit == "json" else "md"
+    suffix_part = f"-{suffix}" if suffix else ""
+    raw = path / f"{slug}-raw{suffix_part}.{extension}"
+    try:
+        home = _Path.home().resolve()
+        relative = raw.relative_to(home)
+        return f"~/{relative}"
+    except ValueError:
+        return str(raw)
+
+
 def persist_report(report: schema.Report) -> dict[str, int]:
     import store
 
@@ -373,7 +393,27 @@ def main() -> int:
         pass
 
     fun_level = config.get("FUN_LEVEL", "medium").lower()
-    rendered = emit_output(report, args.emit, fun_level=fun_level)
+    footer_save_path = None
+    if args.save_dir:
+        footer_save_path = compute_save_path_display(
+            args.save_dir, report.topic, args.save_suffix or "", args.emit
+        )
+
+    # Signal to render_compact whether pre-research flags were supplied.
+    # Used to emit a Pre-Research Status warning when the model skipped
+    # Step 0.5 / 0.55 and invoked the engine bare on an eligible topic.
+    pre_research_flags_present = bool(
+        args.x_handle
+        or args.github_user
+        or args.subreddits
+        or args.plan
+        or args.auto_resolve
+        or args.tiktok_creators
+        or args.ig_creators
+    )
+    report.artifacts["pre_research_flags_present"] = pre_research_flags_present
+
+    rendered = emit_output(report, args.emit, fun_level=fun_level, save_path=footer_save_path)
     if args.save_dir:
         save_path = save_output(report, args.emit, args.save_dir, suffix=args.save_suffix or "")
         sys.stderr.write(f"[last30days] Saved output to {save_path}\n")

scripts/lib/env.py

@@ -278,13 +278,6 @@ def get_config() -> dict[str, Any]:
     else:
         config['_CONFIG_SOURCE'] = 'env_only'
 
-    # Resolve comma-separated SCRAPECREATORS_API_KEY — pick one randomly for load distribution
-    sc_key_raw = config.get('SCRAPECREATORS_API_KEY') or ''
-    if ',' in sc_key_raw:
-        import random
-        sc_keys = [k.strip() for k in sc_key_raw.split(',') if k.strip()]
-        config['SCRAPECREATORS_API_KEY'] = random.choice(sc_keys) if sc_keys else ''
-
     # Extract browser credentials if configured
     browser_creds = extract_browser_credentials(config)
     for key, value in browser_creds.items():

scripts/lib/pipeline.py

@@ -407,7 +407,7 @@ def run(
         if bundle.items_by_source.get(source):
             del bundle.errors_by_source[source]
 
-    items_by_source = _finalize_items_by_source(bundle.items_by_source)
+    items_by_source = _finalize_items_by_source(bundle.items_by_source, topic=topic)
     candidates = weighted_rrf(bundle.items_by_source_and_query, plan, pool_limit=settings["pool_limit"])
     ranked_candidates = rerank.rerank_candidates(
         topic=topic,
@@ -472,11 +472,22 @@ def _normalize_score_dedupe(
     return normalized
 
 
-def _finalize_items_by_source(items_by_source_raw: dict[str, list[schema.SourceItem]]) -> dict[str, list[schema.SourceItem]]:
+def _finalize_items_by_source(
+    items_by_source_raw: dict[str, list[schema.SourceItem]],
+    topic: str = "",
+) -> dict[str, list[schema.SourceItem]]:
     finalized = {}
     for source, items in items_by_source_raw.items():
         items = sorted(items, key=lambda item: item.local_rank_score or 0.0, reverse=True)
-        finalized[source] = dedupe.dedupe_items(items)
+        items = dedupe.dedupe_items(items)
+        # Post-merge topic-relevance filter for Polymarket: comparison queries
+        # fan out into per-entity subqueries ("Hermes", "OpenClaw") whose topic
+        # is too narrow for Gamma API to filter meaningfully. Re-validating the
+        # merged list against the full original topic drops off-topic markets
+        # (e.g., WTI crude oil, Elon tweet counts) before footer emission.
+        if source == "polymarket" and topic:
+            items = polymarket.filter_items_against_topic(topic, items)
+        finalized[source] = items
     return finalized
 
 

scripts/lib/polymarket.py

@@ -117,6 +117,9 @@ _NOISE_WORDS = frozenset({
     "software", "plugin", "skill", "agent", "bot", "search", "research",
     # Generic prediction market terms
     "market", "odds", "prediction", "forecast", "chance", "probability",
+    # Comparison-query conjunctions — should not count as informative filter tokens
+    # when the topic is "X vs Y vs Z"
+    "vs", "versus",
 })
 
 
@@ -165,6 +168,70 @@ def _passes_topic_filter(topic: str, event_title: str) -> bool:
     return match_count >= min_matches
 
 
+def _passes_any_informative_word(topic: str, event_title: str) -> bool:
+    """Looser variant of _passes_topic_filter that keeps an item if ANY
+    informative word from the topic appears in the title.
+
+    Designed for post-merge validation of comparison topics (e.g., "OpenClaw vs
+    Hermes vs Paperclip"), where a market mentioning just one of the entities
+    is still on-topic. The stricter _passes_topic_filter (min_matches=2 for
+    3+ informative words) is correct for single-entity topics like "Mill.com
+    food recycler" but drops legitimate single-entity comparison results.
+    """
+    core = _extract_core_subject(topic).lower()
+    core_words = [w for w in re.sub(r"[^\w\s]", " ", core).split() if len(w) > 1]
+    if not core_words:
+        return True
+    informative = [w for w in core_words if w not in _NOISE_WORDS]
+    if not informative:
+        return True
+
+    title_lower = " ".join(re.sub(r"[^\w\s]", " ", event_title.lower()).split())
+    title_words = set(title_lower.split())
+
+    for word in informative:
+        if word in title_words:
+            return True
+        if len(word) >= 4 and word in title_lower:
+            return True
+    return False
+
+
+def filter_items_against_topic(topic: str, items: List[Any]) -> List[Any]:
+    """Drop items whose title shares no informative word with the original topic.
+
+    Called post-merge from pipeline.py so per-entity subquery results for
+    comparison topics get re-validated against the ORIGINAL full topic before
+    landing in the footer. Prevents noise like WTI crude oil or Elon tweet
+    markets from surviving a loose "Hermes" single-entity subquery match.
+
+    Uses the looser _passes_any_informative_word rule (ANY entity name match
+    is sufficient) so a market mentioning just one of several compared entities
+    still counts as on-topic.
+
+    Accepts a list of either raw dicts (with 'title') or SourceItem-like objects
+    (with .title attribute). Returns the filtered list in the same order.
+    """
+    if not topic:
+        return items
+
+    filtered = []
+    for item in items:
+        title = getattr(item, "title", None)
+        if title is None and isinstance(item, dict):
+            title = item.get("title", "")
+        title = title or ""
+
+        if _passes_any_informative_word(topic, title):
+            filtered.append(item)
+
+    dropped = len(items) - len(filtered)
+    if dropped:
+        _log(f"Post-merge topic filter dropped {dropped} Polymarket items against full topic '{topic}'")
+
+    return filtered
+
+
 def _extract_domain_queries(topic: str, events: List[Dict]) -> List[str]:
     """Extract domain-indicator search terms from first-pass event tags.
 

scripts/lib/render.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 from collections import Counter
+from urllib.parse import urlparse
 
 from . import dates, schema
 
@@ -36,7 +37,7 @@ def _assistant_safety_lines() -> list[str]:
     ]
 
 
-def render_compact(report: schema.Report, cluster_limit: int = 8, fun_level: str = "medium") -> str:
+def render_compact(report: schema.Report, cluster_limit: int = 8, fun_level: str = "medium", save_path: str | None = None) -> str:
     non_empty = [s for s, items in sorted(report.items_by_source.items()) if items]
     lines = [
         f"# last30days v3.0.0: {report.topic}",
@@ -86,9 +87,153 @@ def render_compact(report: schema.Report, cluster_limit: int = 8, fun_level: str
         lines.extend([""] + best_takes)
 
     lines.extend(_render_source_coverage(report))
+
+    pre_research_warning = _render_pre_research_warning(report)
+    if pre_research_warning:
+        lines.append("")
+        lines.extend(pre_research_warning)
+
+    comparison_scaffold = _render_comparison_scaffold(report.topic)
+    if comparison_scaffold:
+        lines.append("")
+        lines.extend(comparison_scaffold)
+
+    footer = _render_emoji_footer(report, save_path)
+    if footer:
+        lines.append("")
+        lines.extend(footer)
+
     return "\n".join(lines).strip() + "\n"
 
 
+def _is_pre_research_eligible(topic: str) -> bool:
+    """Return True if the topic looks like a person, project, brand, or product.
+
+    Heuristic: 1-5 words, AND either at least one word is capitalized OR it is
+    a single word (product names like "nvidia" or "openai" are valid lowercase
+    brand handles). Comparison topics (containing vs/versus) also count as
+    eligible because per-entity resolution is expected.
+
+    Phrases that clearly look abstract (multi-word all-lowercase prose like
+    "best noise cancelling headphones" or "ai regulation") return False.
+
+    False positives are preferable to false negatives here since the warning
+    is only an advisory nudge, not a blocker.
+    """
+    if not topic:
+        return False
+    words = topic.strip().split()
+    # Comparison queries are always eligible (per-entity resolution expected)
+    # Check before the word-count cap since comparisons with 3+ entities can exceed 5 words.
+    lower = topic.lower()
+    if " vs " in lower or " vs. " in lower or " versus " in lower:
+        return True
+    if len(words) < 1 or len(words) > 5:
+        return False
+    # Single-word topics are eligible (product names are often lowercase brand handles)
+    if len(words) == 1:
+        return True
+    # Multi-word topics need at least one capitalized word
+    capitalized = sum(1 for w in words if w and w[0].isupper())
+    return capitalized >= 1
+
+
+def _render_pre_research_warning(report: schema.Report) -> list[str]:
+    """Emit a Pre-Research Status warning block when the engine was called
+    without --x-handle / --github-user / --subreddits / --plan / --auto-resolve
+    on a topic that would benefit from pre-research resolution.
+
+    Returns empty list when flags are present or topic is not eligible.
+    """
+    flags_present = bool(report.artifacts.get("pre_research_flags_present", False))
+    if flags_present:
+        return []
+    if not _is_pre_research_eligible(report.topic):
+        return []
+
+    return [
+        "## Pre-Research Status",
+        "",
+        "⚠️  Step 0.55 pre-research was skipped. The engine ran with keyword search only.",
+        "",
+        "For people, projects, brands, and products this usually misses:",
+        "- Founder and team X timelines (what they post about their own work)",
+        "- GitHub repo activity (issues, PRs, release notes, commit velocity)",
+        "- Subreddit-specific threads on dedicated communities",
+        "- Topic-specific TikTok and Instagram creators",
+        "",
+        "To fix: in a fresh Claude Code window, run `ToolSearch select:WebSearch` first,",
+        f"then rerun `/last30days {report.topic}`. The skill will resolve handles",
+        "and communities before calling the engine this time, producing richer results.",
+        "",
+        "If this topic really is abstract (e.g. \"AI regulation\") and doesn't need",
+        "handle resolution, add `--auto-resolve` to the engine command or ignore this",
+        "warning - the current results are the keyword-search fallback.",
+    ]
+
+
+def _parse_comparison_entities(topic: str) -> list[str] | None:
+    """Return list of entity names if topic is a comparison query, else None.
+
+    Splits on ` vs ` or ` versus ` (case-insensitive). Caps at 4 entities
+    for table readability. Returns None if only one entity or empty input.
+    """
+    if not topic:
+        return None
+    import re
+    parts = re.split(r"\s+(?:vs\.?|versus)\s+", topic.strip(), flags=re.IGNORECASE)
+    parts = [p.strip() for p in parts if p.strip()]
+    if len(parts) < 2:
+        return None
+    return parts[:4]
+
+
+def _render_comparison_scaffold(topic: str) -> list[str]:
+    """Emit a markdown comparison table scaffold for synthesizer to fill.
+
+    Returns empty list if topic is not a comparison query. When present,
+    the block is bracketed so the synthesizer can detect it and pass through.
+
+    Axes match the April 9 launch-video exemplar (9 axes suited to AI-tool
+    comparisons). For non-AI-tool comparisons, the synthesizer writes N/A
+    or topic-appropriate substitutes in irrelevant rows.
+    """
+    entities = _parse_comparison_entities(topic)
+    if not entities:
+        return []
+
+    # Header row - uses "Dimension" per the April 9 exemplar (not "Feature")
+    header = "| Dimension | " + " | ".join(entities) + " |"
+    # Separator row matching column count
+    separator = "|" + "|".join(["---"] * (len(entities) + 1)) + "|"
+    # 9 axes from the April 9 exemplar. Model fills with topic-appropriate
+    # content; irrelevant axes get "N/A" rather than invented data.
+    axes = [
+        "What it is",
+        "GitHub stars",
+        "Philosophy",
+        "Skills",
+        "Memory",
+        "Models",
+        "Security",
+        "Best for",
+        "Install",
+    ]
+    body = [f"| {axis} | " + " | ".join([" "] * len(entities)) + " |" for axis in axes]
+
+    return [
+        "## Head-to-Head",
+        "",
+        "Fill each cell based on the research above. Keep cells short (5-15 words). Use ' - ' (hyphen with spaces) not em-dashes. Write N/A for axes that do not apply to this topic class. This scaffold matches the April 9 launch-video exemplar shape.",
+        "",
+        header,
+        separator,
+        *body,
+        "",
+        "After the table, write the Bottom Line section with one Choose-X-if paragraph per entity, then the emerging stack paragraph. See the comparison template in SKILL.md for the full structure.",
+    ]
+
+
 def render_full(report: schema.Report) -> str:
     """Full data dump: ALL clusters + ALL items by source. For saved files and debugging."""
     # Start with the same header as compact
@@ -301,10 +446,54 @@ def _format_volume_short(volume: float) -> str:
     return ""
 
 
+def _shorten_polymarket_title(title: str) -> str:
+    """Strip boilerplate from a Polymarket question to produce a compact descriptor.
+
+    Examples:
+    - "Will Kanye West visit the UK by June 30?" -> "UK visit"
+    - "Kanye West blocked from entering another country by June 30?" -> "blocked from entering another country"
+    - "Will Bianca and Kanye West separate in 2026?" -> "Bianca and Kanye West separate"
+
+    Falls back to first 3-4 significant words if stripping does not reduce below 40 chars.
+    Never truncates mid-word.
+    """
+    import re
+
+    t = (title or "").strip().rstrip("?").strip()
+
+    # Drop leading "Will "
+    if t.lower().startswith("will "):
+        t = t[5:].strip()
+
+    # Drop "by <Month> <Day>" or "by <Month> <Day>, <Year>" tail
+    t = re.sub(r"\s+by\s+(January|February|March|April|May|June|July|August|September|October|November|December)\s+\d+(?:,\s*\d{4})?$", "", t, flags=re.IGNORECASE)
+    # Drop "in <Year>" tail (e.g. "separate in 2026")
+    t = re.sub(r"\s+in\s+\d{4}$", "", t, flags=re.IGNORECASE)
+    # Drop "by <Year>" tail
+    t = re.sub(r"\s+by\s+\d{4}$", "", t, flags=re.IGNORECASE)
+    # Drop "before <Month> <Day>" tail
+    t = re.sub(r"\s+before\s+(January|February|March|April|May|June|July|August|September|October|November|December)\s+\d+$", "", t, flags=re.IGNORECASE)
+
+    # Pattern: "<Subject> visit <Place>" -> "<Place> visit"
+    m = re.match(r"^(.+?)\s+visit\s+(?:the\s+)?(.+)$", t, flags=re.IGNORECASE)
+    if m:
+        subject, place = m.group(1), m.group(2)
+        t = f"{place} visit"
+
+    t = t.strip()
+
+    # If still too long, fall back to first 6 significant words
+    if len(t) > 40:
+        words = t.split()
+        t = " ".join(words[:6])
+
+    return t
+
+
 def _polymarket_top_markets(items: list[schema.SourceItem], limit: int = 3) -> list[str]:
     """Build short summary strings for the top Polymarket markets by volume.
 
-    Returns list like: ['"BULLY <300k": 96% ($66K)', '"Top Spotify": Kanye 6.5% ($21K)']
+    Returns list like: ['UK visit 5.5%', 'Israel visit 8%', 'blocked from entering 36%']
     """
     # Sort by volume descending
     sorted_items = sorted(
@@ -313,27 +502,28 @@ def _polymarket_top_markets(items: list[schema.SourceItem], limit: int = 3) -> l
         reverse=True,
     )
 
-    summaries = []
+    summaries: list[str] = []
     for item in sorted_items[:limit]:
         outcome_prices = item.metadata.get("outcome_prices") or []
         if not outcome_prices:
             continue
 
-        # Pick the leading outcome (first one, already sorted by relevance in polymarket.py)
         lead_name, lead_price = outcome_prices[0]
-        # For binary Yes/No markets, show "Yes: 96%" format
+        if not isinstance(lead_price, (int, float)):
-        # For multi-outcome, show "OutcomeName: X%"
-        if isinstance(lead_price, (int, float)):
-            pct = f"{lead_price * 100:.0f}%" if lead_price >= 0.1 else f"{lead_price * 100:.1f}%"
-        else:
             continue
 
-        # Short title
+        pct = f"{lead_price * 100:.0f}%" if lead_price >= 0.1 else f"{lead_price * 100:.1f}%"
-        title = item.metadata.get("question") or item.title
-        if len(title) > 30:
-            title = title[:27] + "..."
 
-        summaries.append(f'"{title}": {lead_name} {pct}')
+        descriptor = _shorten_polymarket_title(item.metadata.get("question") or item.title or "")
+        if not descriptor:
+            continue
+
+        # For binary Yes/No markets (lead_name == "Yes"), the "Yes" is implicit - omit it.
+        # For named outcomes (e.g. "Kanye" in a multi-way market), keep the outcome name.
+        if lead_name.lower() == "yes":
+            summaries.append(f"{descriptor} {pct}")
+        else:
+            summaries.append(f"{descriptor}: {lead_name} {pct}")
 
     return summaries
 
@@ -354,6 +544,284 @@ def _render_source_coverage(report: schema.Report) -> list[str]:
     return lines
 
 
+# Known publications for the Web line of the emoji-tree footer.
+# Maps apex domain to a clean display name. Unknown domains fall back to
+# the bare domain string (protocol stripped, www. removed).
+_SITE_NAMES: dict[str, str] = {
+    "later.com": "Later",
+    "buffer.com": "Buffer",
+    "socialbee.com": "SocialBee",
+    "cnn.com": "CNN",
+    "bbc.com": "BBC",
+    "bbc.co.uk": "BBC",
+    "nytimes.com": "NYT",
+    "nypost.com": "NY Post",
+    "wsj.com": "WSJ",
+    "bloomberg.com": "Bloomberg",
+    "reuters.com": "Reuters",
+    "theverge.com": "The Verge",
+    "techcrunch.com": "TechCrunch",
+    "wired.com": "Wired",
+    "arstechnica.com": "Ars Technica",
+    "theguardian.com": "The Guardian",
+    "independent.co.uk": "The Independent",
+    "theatlantic.com": "The Atlantic",
+    "newyorker.com": "The New Yorker",
+    "washingtonpost.com": "Washington Post",
+    "politico.com": "Politico",
+    "axios.com": "Axios",
+    "semafor.com": "Semafor",
+    "theinformation.com": "The Information",
+    "medium.com": "Medium",
+    "substack.com": "Substack",
+    "dev.to": "dev.to",
+    "github.com": "GitHub",
+    "stackoverflow.com": "Stack Overflow",
+    "producthunt.com": "Product Hunt",
+    "variety.com": "Variety",
+    "deadline.com": "Deadline",
+    "rollingstone.com": "Rolling Stone",
+    "complex.com": "Complex",
+    "pbs.org": "PBS",
+    "npr.org": "NPR",
+    "forbes.com": "Forbes",
+    "cnbc.com": "CNBC",
+    "businessinsider.com": "Business Insider",
+    "fortune.com": "Fortune",
+    "vox.com": "Vox",
+    "slate.com": "Slate",
+    "theregister.com": "The Register",
+    "venturebeat.com": "VentureBeat",
+    "hackernoon.com": "HackerNoon",
+    "anthropic.com": "Anthropic",
+    "openai.com": "OpenAI",
+    "aws.amazon.com": "AWS",
+    "9to5mac.com": "9to5Mac",
+    "9to5google.com": "9to5Google",
+    "decrypt.co": "Decrypt",
+    "xda-developers.com": "XDA",
+    "tomshardware.com": "Tom's Hardware",
+    "engadget.com": "Engadget",
+    "mashable.com": "Mashable",
+    "vellum.ai": "Vellum",
+    "helpnetsecurity.com": "Help Net Security",
+    "gizmodo.com": "Gizmodo",
+}
+
+
+def _site_name_for_url(url: str) -> str:
+    """Return a clean publication name for a URL, or a bare domain fallback.
+
+    Strips protocol and ``www.`` from unknowns; checks known publications
+    before falling back. Returns a short readable string, never a raw URL.
+    """
+    if not url:
+        return ""
+    u = url.strip()
+    if not u:
+        return ""
+    # urlparse needs a scheme to resolve the netloc; prepend http:// if missing.
+    parsed = urlparse(u if "://" in u else f"http://{u}")
+    host = (parsed.netloc or parsed.path.split("/", 1)[0]).lower()
+    if host.startswith("www."):
+        host = host[4:]
+    if not host:
+        return u[:40]
+    if host in _SITE_NAMES:
+        return _SITE_NAMES[host]
+    # Try stripping one subdomain level (eu.example.com -> example.com)
+    parts = host.split(".")
+    if len(parts) >= 3:
+        apex = ".".join(parts[-2:])
+        if apex in _SITE_NAMES:
+            return _SITE_NAMES[apex]
+    return host
+
+
+def _format_web_line_sources(items: list[schema.SourceItem], limit: int = 8) -> str:
+    """Return comma-separated clean publication names for the Web line.
+
+    Deduplicates by display name while preserving first-seen order.
+    """
+    seen: list[str] = []
+    for item in items:
+        if not item.url:
+            continue
+        name = _site_name_for_url(item.url)
+        if not name:
+            continue
+        if name not in seen:
+            seen.append(name)
+        if len(seen) >= limit:
+            break
+    return ", ".join(seen)
+
+
+# Per-source line format for the emoji-tree footer.
+# Label in the template, emoji prefix, word for the item count, and which
+# engagement dimensions to show.  Keys are the source names as used in
+# Report.items_by_source.  Order here is the render order.
+_FOOTER_SOURCES: list[tuple[str, str, str, str, list[tuple[str, str]]]] = [
+    # (source_key,  emoji, display_name, item_word_singular, [(engagement_key, word)])
+    ("reddit",      "🟠", "Reddit",       "thread",   [("score", "upvotes"), ("num_comments", "comments")]),
+    ("x",           "🔵", "X",            "post",     [("likes", "likes"), ("reposts", "reposts")]),
+    ("youtube",     "🔴", "YouTube",      "video",    [("views", "views")]),  # transcripts appended below in _build_source_footer_lines
+    ("tiktok",      "🎵", "TikTok",       "video",    [("views", "views"), ("likes", "likes")]),
+    ("instagram",   "📸", "Instagram",    "reel",     [("views", "views"), ("likes", "likes")]),
+    ("threads",     "🧵", "Threads",      "post",     [("likes", "likes"), ("replies", "replies")]),
+    ("pinterest",   "📌", "Pinterest",    "pin",      [("saves", "saves"), ("comments", "comments")]),
+    ("hackernews",  "🟡", "HN",           "story",    [("points", "points"), ("comments", "comments")]),
+    ("bluesky",     "🦋", "Bluesky",      "post",     [("likes", "likes"), ("reposts", "reposts")]),
+    ("truthsocial", "🇺🇸", "Truth Social", "post",     [("likes", "likes"), ("reposts", "reposts")]),
+    ("github",      "🐙", "GitHub",       "item",     [("reactions", "reactions"), ("comments", "comments")]),
+]
+
+
+def _sum_engagement(items: list[schema.SourceItem], key: str) -> int:
+    total = 0
+    for item in items:
+        value = item.engagement.get(key) if item.engagement else None
+        if value in (None, ""):
+            continue
+        try:
+            total += int(value)
+        except (TypeError, ValueError):
+            continue
+    return total
+
+
+def _footer_line_for_source(emoji: str, label: str, count: int, item_word: str, stats: str) -> str:
+    count_str = f"{count:,}" if count >= 1000 else str(count)
+    plural = f"{item_word}s" if count != 1 else item_word
+    if stats:
+        return f"{emoji} {label}: {count_str} {plural} │ {stats}"
+    return f"{emoji} {label}: {count_str} {plural}"
+
+
+def _build_source_footer_lines(report: schema.Report) -> list[str]:
+    """Return emoji-tree body lines (without tree characters) for each populated source.
+
+    The caller adds the tree characters (├─ / └─) after assembling all lines.
+    """
+    out: list[str] = []
+    for source_key, emoji, label, item_word, engagement_fields in _FOOTER_SOURCES:
+        items = report.items_by_source.get(source_key) or []
+        if not items:
+            continue
+        parts: list[str] = []
+        for eng_key, word in engagement_fields:
+            total = _sum_engagement(items, eng_key)
+            if total > 0:
+                total_str = f"{total:,}" if total >= 1000 else str(total)
+                parts.append(f"{total_str} {word}")
+        # YouTube: append "N with transcripts" instead of a third likes-based column.
+        # Transcripts are a more meaningful research-depth signal than likes.
+        if source_key == "youtube":
+            with_transcripts = sum(
+                1 for it in items
+                if (it.metadata.get("transcript_highlights") or it.metadata.get("transcript_snippet"))
+            )
+            if with_transcripts > 0:
+                parts.append(f"{with_transcripts} with transcripts")
+        stats = " │ ".join(parts)
+        out.append(_footer_line_for_source(emoji, label, len(items), item_word, stats))
+
+    # Polymarket (special: count + odds string from existing helper)
+    polymarket_items = report.items_by_source.get("polymarket") or []
+    if polymarket_items:
+        odds = _polymarket_top_markets(polymarket_items, limit=3)
+        odds_str = ", ".join(odds) if odds else ""
+        count = len(polymarket_items)
+        count_str = f"{count:,}" if count >= 1000 else str(count)
+        plural = "markets" if count != 1 else "market"
+        if odds_str:
+            out.append(f"📊 Polymarket: {count_str} {plural} │ {odds_str}")
+        else:
+            out.append(f"📊 Polymarket: {count_str} {plural}")
+
+    # Web (sources from grounding)
+    web_items = report.items_by_source.get("grounding") or []
+    if web_items:
+        names = _format_web_line_sources(web_items)
+        count = len(web_items)
+        count_str = f"{count:,}" if count >= 1000 else str(count)
+        plural = "pages" if count != 1 else "page"
+        if names:
+            out.append(f"🌐 Web: {count_str} {plural} - {names}")
+        else:
+            out.append(f"🌐 Web: {count_str} {plural}")
+
+    return out
+
+
+def _top_voices_footer_line(report: schema.Report) -> str | None:
+    """Return the 🗣️ Top voices line or None if no meaningful voices exist.
+
+    Combines top handles (X, Bluesky, Truth Social, YouTube, TikTok, Instagram)
+    and top subreddits, separated by │.
+    """
+    handle_items = {
+        source: report.items_by_source.get(source) or []
+        for source in ("x", "bluesky", "truthsocial", "youtube", "tiktok", "instagram", "threads")
+    }
+    handle_counts: Counter[str] = Counter()
+    for items in handle_items.values():
+        for item in items:
+            actor = _stats_actor(item)
+            if actor and actor.startswith("@"):
+                handle_counts[actor] += 1
+
+    subreddit_counts: Counter[str] = Counter()
+    for item in report.items_by_source.get("reddit") or []:
+        if item.container:
+            subreddit_counts[f"r/{item.container}"] += 1
+
+    top_handles = [h for h, _ in handle_counts.most_common(3)]
+    top_subs = [s for s, _ in subreddit_counts.most_common(3)]
+    if not top_handles and not top_subs:
+        return None
+    parts: list[str] = []
+    if top_handles:
+        parts.append(", ".join(top_handles))
+    if top_subs:
+        parts.append(", ".join(top_subs))
+    return f"🗣️ Top voices: {' │ '.join(parts)}"
+
+
+def _render_emoji_footer(report: schema.Report, save_path: str | None) -> list[str]:
+    """Produce the deterministic magic footer block.
+
+    Returns a list of markdown lines, including enclosing ``---`` separators.
+    Returns an empty list if no sources are populated.
+    """
+    source_lines = _build_source_footer_lines(report)
+    if not source_lines:
+        return []
+
+    voices_line = _top_voices_footer_line(report)
+    raw_line = f"📎 Raw results saved to {save_path}" if save_path else None
+
+    body: list[str] = []
+    body.extend(source_lines)
+    if voices_line:
+        body.append(voices_line)
+    if raw_line:
+        body.append(raw_line)
+
+    # Apply tree characters: ├─ for all but the last body line, └─ for the last.
+    tree_lines: list[str] = []
+    for i, line in enumerate(body):
+        prefix = "└─" if i == len(body) - 1 else "├─"
+        tree_lines.append(f"{prefix} {line}")
+
+    return [
+        "---",
+        "✅ All agents reported back!",
+        *tree_lines,
+        "---",
+    ]
+
+
 def _render_stats(report: schema.Report) -> list[str]:
     lines = [
         "## Stats",