Last30days

last30days v2.5: Research Any Topic from the Last 30 Days

Research ANY topic across Reddit, X, YouTube, Hacker News, Polymarket, and the web. Surface what people are actually discussing, recommending, betting on, and debating right now.

CRITICAL: Parse User Intent

Before doing anything, parse the user's input for:

1. TOPIC: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
2. TARGET TOOL (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
3. QUERY TYPE: What kind of research they want:
   • PROMPTING - "X prompts", "prompting for X", "X best practices" → User wants to learn techniques and get copy-paste prompts
   • RECOMMENDATIONS - "best X", "top X", "what X should I use", "recommended X" → User wants a LIST of specific things
   • NEWS - "what's happening with X", "X news", "latest on X" → User wants current events/updates
   • GENERAL - anything else → User wants broad understanding of the topic

Common patterns:

• [topic] for [tool] → "web mockups for Nano Banana Pro" → TOOL IS SPECIFIED
• [topic] prompts for [tool] → "UI design prompts for Midjourney" → TOOL IS SPECIFIED
• Just [topic] → "iOS design mockups" → TOOL NOT SPECIFIED, that's OK
• "best [topic]" or "top [topic]" → QUERY_TYPE = RECOMMENDATIONS
• "what are the best [topic]" → QUERY_TYPE = RECOMMENDATIONS

IMPORTANT: Do NOT ask about target tool before research.

• If tool is specified in the query, use it
• If tool is NOT specified, run research first, then ask AFTER showing results

Store these variables:

• TOPIC = [extracted topic]
• TARGET_TOOL = [extracted tool, or "unknown" if not specified]
• QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | GENERAL]

DISPLAY your parsing to the user. Before running any tools, output:

I'll research {TOPIC} across Reddit, X, and the web to find what's been discussed in the last 30 days.

Parsed intent:
- TOPIC = {TOPIC}
- TARGET_TOOL = {TARGET_TOOL or "unknown"}
- QUERY_TYPE = {QUERY_TYPE}

Research typically takes 2-8 minutes (niche topics take longer). Starting now.

If TARGET_TOOL is known, mention it in the intro: "...to find {QUERY_TYPE}-style content for use in {TARGET_TOOL}."

This text MUST appear before you call any tools. It confirms to the user that you understood their request.

Step 0.5: Resolve X Handle (if topic could have an X account)

If TOPIC looks like it could have its own X/Twitter account - people, creators, brands, products, tools, companies, communities (e.g., "Dor Brothers", "Jason Calacanis", "Nano Banana Pro", "Seedance", "Midjourney"), do ONE quick WebSearch:

WebSearch("{TOPIC} X twitter handle site:x.com")

From the results, extract their X/Twitter handle. Look for:

• Verified profile URLs like x.com/{handle} or twitter.com/{handle}
• Mentions like "@handle" in bios, articles, or social profiles
• "Follow @handle on X" patterns

Verify the account is real, not a parody/fan account. Check for:

• Verified/blue checkmark in the search results
• Official website linking to the X account
• Consistent naming (e.g., @thedorbrothers for "The Dor Brothers", not @DorBrosFan)
• If results only show fan/parody/news accounts (not the entity's own account), skip - the entity may not have an X presence

If you find a clear, verified handle, pass it as --x-handle={handle} (without @). This searches that account's posts directly - finding content they posted that doesn't mention their own name.

Skip this step if:

• TOPIC is clearly a generic concept, not an entity (e.g., "best rap songs 2026", "how to use Docker", "AI ethics debate")
• TOPIC already contains @ (user provided the handle directly)
• Using --quick depth
• WebSearch shows no official X account exists for this entity

Store: RESOLVED_HANDLE = {handle or empty}

Research Execution

Step 1: Run the research script (FOREGROUND — do NOT background this)

CRITICAL: Run this command in the FOREGROUND with a 5-minute timeout. Do NOT use run_in_background. The full output contains Reddit, X, AND YouTube data that you need to read completely.

# Find skill root — works in repo checkout, Claude Code, or Codex install
for dir in \
  "." \
  "${CLAUDE_PLUGIN_ROOT:-}" \
  "$HOME/.claude/skills/last30days" \
  "$HOME/.agents/skills/last30days" \
  "$HOME/.codex/skills/last30days"; do
  [ -n "$dir" ] && [ -f "$dir/scripts/last30days.py" ] && SKILL_ROOT="$dir" && break
done

if [ -z "${SKILL_ROOT:-}" ]; then
  echo "ERROR: Could not find scripts/last30days.py" >&2
  exit 1
fi

python3 "${SKILL_ROOT}/scripts/last30days.py" "$ARGUMENTS" --emit=compact  # Add --x-handle=HANDLE if RESOLVED_HANDLE is set

Use a timeout of 300000 (5 minutes) on the Bash call. The script typically takes 1-3 minutes.

The script will automatically:

• Detect available API keys
• Run Reddit/X/YouTube/Hacker News/Polymarket searches
• Output ALL results including YouTube transcripts, HN comments, and prediction market odds

Read the ENTIRE output. It contains SIX data sections in this order: Reddit items, X items, YouTube items, Hacker News items, Polymarket items, and WebSearch items. If you miss sections, you will produce incomplete stats.

YouTube items in the output look like: **{video_id}** (score:N) {channel_name} [N views, N likes] followed by a title, URL, and optional transcript snippet. Count them and include them in your synthesis and stats block.

STEP 2: DO WEBSEARCH AFTER SCRIPT COMPLETES

After the script finishes, do WebSearch to supplement with blogs, tutorials, and news.

For ALL modes, do WebSearch to supplement (or provide all data in web-only mode).

Choose search queries based on QUERY_TYPE:

If RECOMMENDATIONS ("best X", "top X", "what X should I use"):

• Search for: best {TOPIC} recommendations
• Search for: {TOPIC} list examples
• Search for: most popular {TOPIC}
• Goal: Find SPECIFIC NAMES of things, not generic advice

If NEWS ("what's happening with X", "X news"):

• Search for: {TOPIC} news 2026
• Search for: {TOPIC} announcement update
• Goal: Find current events and recent developments

If PROMPTING ("X prompts", "prompting for X"):

• Search for: {TOPIC} prompts examples 2026
• Search for: {TOPIC} techniques tips
• Goal: Find prompting techniques and examples to create copy-paste prompts

If GENERAL (default):

• Search for: {TOPIC} 2026
• Search for: {TOPIC} discussion
• Goal: Find what people are actually saying

For ALL query types:

• USE THE USER'S EXACT TERMINOLOGY - don't substitute or add tech names based on your knowledge
• EXCLUDE reddit.com, x.com, twitter.com (covered by script)
• INCLUDE: blogs, tutorials, docs, news, GitHub repos
• DO NOT output "Sources:" list - this is noise, we'll show stats at the end

Options (passed through from user's command):

• --days=N → Look back N days instead of 30 (e.g., --days=7 for weekly roundup)
• --quick → Faster, fewer sources (8-12 each)
• (default) → Balanced (20-30 each)
• --deep → Comprehensive (50-70 Reddit, 40-60 X)

Judge Agent: Synthesize All Sources

After all searches complete, internally synthesize (don't display stats yet):

The Judge Agent must:

1. Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)

2. Weight YouTube sources HIGH (they have views, likes, and transcript content)

3. Weight WebSearch sources LOWER (no engagement data)

4. Identify patterns that appear across ALL sources (strongest signals)

5. Note any contradictions between sources

6. Extract the top 3-5 actionable insights

7. Cross-platform signals are the strongest evidence. When items have [also on: Reddit, HN] or similar tags, it means the same story appears across multiple platforms. Lead with these cross-platform findings - they're the most important signals in the research.

Prediction Markets (Polymarket)

CRITICAL: When Polymarket returns relevant markets, prediction market odds are among the highest-signal data points in your research. Real money on outcomes cuts through opinion. Treat them as strong evidence, not an afterthought.

How to interpret and synthesize Polymarket data:

1. Prefer structural/long-term markets over near-term deadlines. Championship odds > regular season title. Regime change > near-term strike deadline. IPO/major milestone > incremental update. Presidency > individual state primary. When multiple markets exist, the bigger question is more interesting to the user.

2. When the topic is an outcome in a multi-outcome market, call out that specific outcome's odds and movement. Don't just say "Polymarket has a #1 seed market" - say "Arizona has a 28% chance of being the #1 overall seed, up 10% this month." The user cares about THEIR topic's position in the market.

3. Weave odds into the narrative as supporting evidence. Don't isolate Polymarket data in its own paragraph. Instead: "Final Four buzz is building - Polymarket gives Arizona a 12% chance to win the championship (up 3% this week), and 28% to earn a #1 seed."

4. Citation format: Always include specific odds AND movement. "Polymarket has Arizona at 28% for a #1 seed (up 10% this month)" - not just "per Polymarket."

5. When multiple relevant markets exist, highlight 3-5 of the most interesting ones in your synthesis, ordered by importance (structural > near-term). Don't just pick the highest-volume one.

Domain examples of market importance ranking:

• Sports: Championship/tournament odds > conference title > regular season > weekly matchup
• Geopolitics: Regime change/structural outcomes > near-term strike deadlines > sanctions
• Tech/Business: IPO, major product launch, company milestones > incremental updates
• Elections: Presidency > primary > individual state

Do NOT display stats here - they come at the end, right before the invitation.

FIRST: Internalize the Research

CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.

Read the research output carefully. Pay attention to:

• Exact product/tool names mentioned (e.g., if research mentions "ClawdBot" or "@clawdbot", that's a DIFFERENT product than "Claude Code" - don't conflate them)
• Specific quotes and insights from the sources - use THESE, not generic knowledge
• What the sources actually say, not what you assume the topic is about

ANTI-PATTERN TO AVOID: If user asks about "clawdbot skills" and research returns ClawdBot content (self-hosted AI agent), do NOT synthesize this as "Claude Code skills" just because both involve "skills". Read what the research actually says.

If QUERY_TYPE = RECOMMENDATIONS

CRITICAL: Extract SPECIFIC NAMES, not generic patterns.

When user asks "best X" or "top X", they want a LIST of specific things:

• Scan research for specific product names, tool names, project names, skill names, etc.
• Count how many times each is mentioned
• Note which sources recommend each (Reddit thread, X post, blog)
• List them by popularity/mention count

BAD synthesis for "best Claude Code skills":

"Skills are powerful. Keep them under 500 lines. Use progressive disclosure."

GOOD synthesis for "best Claude Code skills":

"Most mentioned skills: /commit (5 mentions), remotion skill (4x), git-worktree (3x), /pr (3x). The Remotion announcement got 16K likes on X."

For all QUERY_TYPEs

Identify from the ACTUAL RESEARCH OUTPUT:

• PROMPT FORMAT - Does research recommend JSON, structured params, natural language, keywords?
• The top 3-5 patterns/techniques that appeared across multiple sources
• Specific keywords, structures, or approaches mentioned BY THE SOURCES
• Common pitfalls mentioned BY THE SOURCES