Deep research engine covering the last 30 days across 10+ sources - Reddit, X/Twitter, YouTube, TikTok, Instagram, Hacker News, Polymarket, and the web. AI synthesizes findings into grounded, cited reports.
Permissions overview: Reads public web/platform data and optionally saves research briefings 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 section.
Research ANY topic across Reddit, X, YouTube, and other sources. Surface what people are actually discussing, recommending, betting on, and debating right now.
CRITICAL: ALWAYS execute Step 0 BEFORE Step 1, even if the user provided a topic. If the user typed , you MUST check for FIRST_RUN and present the wizard BEFORE running research. The topic "Mercer Island" is preserved — research runs immediately after the wizard completes. Do NOT skip the wizard because a topic was provided. The wizard takes 10 seconds and only runs once ever.
/last30days Mercer IslandTo detect first run: check if ~/.config/last30days/.env exists. If it does NOT exist, this is a first run. Do NOT run any Bash commands or show any command output to detect this — just check the file existence silently. If the file exists and contains SETUP_COMPLETE=true, skip this section and proceed to Step 1.
When first run is detected, you MUST follow these steps IN ORDER. Do NOT skip ahead to the topic picker or research. The sequence is: (1) welcome text → (2) setup modal → (3) run setup if chosen → (4) ScrapeCreators modal → (5) topic picker. You MUST start at step 1.
Step 1: Display the following welcome text ONCE as a normal message (not blockquoted). Then IMMEDIATELY call AskUserQuestion — do NOT repeat any of the welcome text inside the AskUserQuestion call.
👋 Welcome to /last30days!
I research any topic across Reddit, X, YouTube, and other sources — synthesizing what people are actually saying right now.
To get the best results, I can: 🔍 Scan your browser for X/Twitter cookies (free X search — reads x.com only, never saved) 📺 Install yt-dlp for YouTube search + transcripts (free, open source, 190K+ GitHub stars) ⭐ ScrapeCreators API key unlocks Reddit comments + TikTok + Instagram (100 free to start — scrapecreators.com)
We recommend all 3 before your first run — it's what makes the magic. More community sources available later. We get no kickbacks from any of these.
Then call AskUserQuestion with ONLY this question and these options — no additional text:
Question: "How would you like to set up?" Options:
If the user picks 1 (Auto setup): Run the setup subcommand:
cd {SKILL_DIR} && python3 scripts/last30days.py setup
Show the user the results (what cookies were found, whether yt-dlp was installed).
Then show the ScrapeCreators push (plain text, then modal):
⭐ One more thing — Reddit comments are some of the best content on the internet. The top-voted replies often have sharper insights than the posts themselves. ScrapeCreators unlocks these (plus TikTok + Instagram) — 100 free to start, no credit card.
Call AskUserQuestion: Question: "Want to add Reddit comments to your research?" Options:
open https://scrapecreators.com via Bash to open in the user's browser. Then ask them to paste the API key they get. When they paste it, write SCRAPECREATORS_API_KEY={key} to ~/.config/last30days/.envAfter SC key is saved (not if skipped), show the TikTok/Instagram opt-in:
Your ScrapeCreators key also powers TikTok and Instagram search. Want those on for every research run? (Each additional source uses a ScrapeCreators call per search.)
Call AskUserQuestion: Question: "Also search TikTok and Instagram?" Options:
INCLUDE_SOURCES=tiktok,instagram to ~/.config/last30days/.env. Confirm: "All set — Reddit comments, TikTok, and Instagram will run on every search."After TikTok/Instagram opt-in (or SC skip), show the first research topic modal:
Call AskUserQuestion: Question: "What do you want to research first?" Options:
If user picks an example, run research with that topic. If they pick "Type my own", ask them what they want to research. If the user originally provided a topic with the command (e.g., /last30days Mercer Island), skip this modal and use their topic directly.
END OF FIRST-RUN WIZARD. Everything above in Step 0 ONLY runs on first run. If SETUP_COMPLETE=true exists in .env, skip ALL of Step 0 — no welcome, no setup, no ScrapeCreators modal, no topic picker. Go directly to Step 1 (Parse User Intent). The topic picker is ONLY for first-time users who haven't run /last30days before.
If the user picks 2 (Manual setup): Show them this guide (present as plain text, not blockquoted):
The magic of /last30days is Reddit comments + X posts together. Here's how to unlock each source.
Add these to ~/.config/last30days/.env:
🔍 X/Twitter (pick one — this is the most important):
FROM_BROWSER=auto — easiest, free. Scans your browser cookies for x.com login.AUTH_TOKEN=xxx + CT0=xxx — paste your X cookies manually (x.com → F12 → Application → Cookies)XAI_API_KEY=xxx — use an xAI/Grok API key instead (api.x.ai)⭐ Reddit Comments + TikTok + Instagram + YouTube backup (one key, 5 platforms):
SCRAPECREATORS_API_KEY=xxx — 100 free to start at scrapecreators.com. This is the single most impactful key — Reddit comments are where the sharpest insights live.💡 Reddit discovery fallback (optional):
OPENAI_API_KEY=xxx — ChatGPT web search for Reddit threads. Only used if ScrapeCreators isn't configured — SC is better.📺 YouTube (primary engine):
brew install yt-dlp — free, open source. ScrapeCreators covers YouTube as backup if you skip this.Bonus sources (add anytime):
EXA_API_KEY=xxx — semantic web search, 1K free/month (exa.ai)BSKY_HANDLE=you.bsky.social + BSKY_APP_PASSWORD=xxx — Bluesky (free app password)BRAVE_API_KEY=xxx — Brave web searchOPENROUTER_API_KEY=xxx — Perplexity Sonar Pro searchAlways add this last line: SETUP_COMPLETE=true
You can paste your API keys right here and I'll write the file for you. Some people say giving LLMs your keys is risky, others live in YOLO mode. Your call.
If the user pastes keys, write them to ~/.config/last30days/.env (create the file and parent dirs if needed, append without overwriting existing keys, always include SETUP_COMPLETE=true). Then proceed with research.
If the user says they'll do it themselves, tell them: "Save the file, then run /last30days <topic> to research anything." Then proceed with research using whatever sources are currently available.
If the user picks Skip: Proceed with research immediately using the user's original topic. Note: without setup, sources are limited to Reddit (threads only, no comments), HN, and Polymarket. X/Twitter and YouTube require setup.
When users ask about API keys, setup, or how to unlock more sources, reference this:
You do NOT need API keys to use last30days. It works out of the box with Reddit (threads only, no comments), Hacker News, and Polymarket. Browser cookies for X/Twitter are equivalent to an API key — just log into x.com in any browser and last30days will find your session automatically.
Source unlock progression (all free except ScrapeCreators):
brew install yt-dlp — open source, 190K+ GitHub stars. Enables YouTube search and transcripts.Key comparison: X browser cookies = same access as an API key (free, no signup). ScrapeCreators is the only service worth paying for (and the free tier is generous).
last30days has no affiliation with any API provider — no referrals, no kickbacks.
Before doing anything, parse the user's input for:
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[topic] → "iOS design mockups" → TOOL NOT SPECIFIED, that's OKvs or versus with spaces)IMPORTANT: Do NOT ask about target tool before research.
Store these variables:
TOPIC = [extracted topic]TARGET_TOOL = [extracted tool, or "unknown" if not specified]QUERY_TYPE = [RECOMMENDATIONS | NEWS | HOW-TO | COMPARISON | GENERAL]TOPIC_A = [first item] (only if COMPARISON)TOPIC_B = [second item] (only if COMPARISON)DISPLAY your parsing to the user. Before running any tools, output:
I'll research {TOPIC} across Reddit, X, YouTube, and other sources 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.
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:
x.com/{handle} or twitter.com/{handle}Verify the account is real, not a parody/fan account. Check for:
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:
--quick depthStore: RESOLVED_HANDLE = {handle or empty}
If --agent appears in ARGUMENTS (e.g., /last30days plaud granola --agent):
AskUserQuestion calls - use TARGET_TOOL = "unknown" if not specifiedAgent mode saves raw research data to ~/Documents/Last30Days/ automatically via --save-dir (handled by the script, no extra tool calls).
Agent mode report format:
## Research Report: {TOPIC}
Generated: {date} | Sources: Reddit, X, Bluesky, YouTube, TikTok, HN, Polymarket, Web
### Key Findings
[3-5 bullet points, highest-signal insights with citations]
### What I learned
{The full "What I learned" synthesis from normal output}
### Stats
{The standard stats block}
When the user asks "X vs Y", run THREE research passes in parallel:
Pass 1 + 2 (parallel Bash calls):
# Run BOTH of these as parallel Bash tool calls in a single message:
python3 "${SKILL_ROOT}/scripts/last30days.py" {TOPIC_A} --emit=compact --no-native-web --save-dir=~/Documents/Last30Days
python3 "${SKILL_ROOT}/scripts/last30days.py" {TOPIC_B} --emit=compact --no-native-web --save-dir=~/Documents/Last30Days
Pass 3 (after passes 1+2 complete):
python3 "${SKILL_ROOT}/scripts/last30days.py" "{TOPIC_A} vs {TOPIC_B}" --emit=compact --no-native-web --save-dir=~/Documents/Last30Days
Then do WebSearch for: {TOPIC_A} vs {TOPIC_B} comparison 2026 and {TOPIC_A} vs {TOPIC_B} which is better.
Skip the normal Step 1 below - go directly to the comparison synthesis format (see "If QUERY_TYPE = COMPARISON" in the synthesis section).
Step 1: Run the research script (FOREGROUND — do NOT background this)
CRITICAL: Run this command in the FOREGROUND with a 5-minute timeout. Do NOT use run_in_background. The full output contains Reddit, X, AND YouTube data that you need to read completely.
IMPORTANT: The script handles API key/Codex auth detection automatically. Run it and check the output to determine mode.
# Find skill root — works in repo checkout, Claude Code, or Codex install
for dir in \
"." \
"${CLAUDE_PLUGIN_ROOT:-}" \
"${GEMINI_EXTENSION_DIR:-}" \
"$HOME/.claude/plugins/marketplaces/last30days-skill" \
"$HOME/.gemini/extensions/last30days-skill" \
"$HOME/.gemini/extensions/last30days" \
"$HOME/.claude/skills/last30days" \
"$HOME/.agents/skills/last30days" \
"$HOME/.codex/skills/last30days" \
"$HOME/my-project/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 --no-native-web --save-dir=~/Documents/Last30Days # 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:
Read the ENTIRE output. It contains EIGHT data sections in this order: Reddit items, X items, YouTube items, TikTok items, Instagram Reels 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, transcript highlights (pre-extracted quotable excerpts from the video), and an optional full transcript in a collapsible section. Quote the highlights directly in your synthesis - they are the YouTube equivalent of Reddit top comments. Attribute quotes to the channel name. Count them and include them in your synthesis and stats block.
TikTok items in the output look like: **{TK_id}** (score:N) @{creator} [N views, N likes] followed by a caption, URL, hashtags, and optional caption snippet. Count them and include them in your synthesis and stats block.
Instagram Reels items in the output look like: **{IG_id}** (score:N) @{creator} (date) [N views, N likes] followed by caption text, URL, and optional transcript. Count them and include them in your synthesis and stats block. Instagram provides unique creator/influencer perspective — weight it alongside TikTok.
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"):
best {TOPIC} recommendations{TOPIC} list examplesmost popular {TOPIC}If NEWS ("what's happening with X", "X news"):
{TOPIC} news 2026{TOPIC} announcement updateIf PROMPTING ("X prompts", "prompting for X"):
{TOPIC} prompts examples 2026{TOPIC} techniques tipsIf GENERAL (default):
{TOPIC} 2026{TOPIC} discussionFor ALL query types:
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)--deep → Comprehensive (50-70 Reddit, 40-60 X)After all searches complete, internally synthesize (don't display stats yet):
The Judge Agent must:
Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
Weight YouTube sources HIGH (they have views, likes, and transcript content)
Weight TikTok sources HIGH (they have views, likes, and caption content — viral signal)
Weight WebSearch sources LOWER (no engagement data)
For Reddit: Pay special attention to top comments — they often contain the wittiest, most insightful, or funniest take. When a top comment has high upvotes (shown as 💬 Top comment (N upvotes)), quote it directly in your synthesis. Reddit's value is in the comments.
For YouTube: Quote transcript highlights directly in your synthesis. These are pre-extracted key moments from the video - treat them like Reddit top comments. Attribute to the channel name and include the actual quote. YouTube's value is in what creators SAY, not just their view counts.
Identify patterns that appear across ALL sources (strongest signals)
Note any contradictions between sources
Extract the top 3-5 actionable insights
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.
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:
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.
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.
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."
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."
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:
Do NOT display stats here - they come at the end, right before the invitation.
CRITICAL: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge.
Read the research output carefully. Pay attention to:
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.
CRITICAL: Extract SPECIFIC NAMES, not generic patterns.
When user asks "best X" or "top X", they want a LIST of specific things:
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."
Structure the output as a side-by-side comparison using data from all three research passes:
# {TOPIC_A} vs {TOPIC_B}: What the Community Says (Last 30 Days)
## Quick Verdict
[1-2 sentence data-driven summary: which one the community prefers and why, with source counts]
## {TOPIC_A}
**Community Sentiment:** [Positive/Mixed/Negative] ({N} mentions across {sources})
**Strengths (what people love)**
- [Point 1 with source attribution]
- [Point 2]
**Weaknesses (common complaints)**
- [Point 1 with source attribution]
- [Point 2]
## {TOPIC_B}
**Community Sentiment:** [Positive/Mixed/Negative] ({N} mentions across {sources})
**Strengths (what people love)**
- [Point 1 with source attribution]
- [Point 2]
**Weaknesses (common complaints)**
- [Point 1 with source attribution]
- [Point 2]
## Head-to-Head
[Synthesis from the "A vs B" combined search - what people say when directly comparing]
| Dimension | {TOPIC_A} | {TOPIC_B} |
|-----------|-----------|-----------|
| [Key dimension 1] | [A's position] | [B's position] |
| [Key dimension 2] | [A's position] | [B's position] |
| [Key dimension 3] | [A's position] | [B's position] |
## The Bottom Line
Choose {TOPIC_A} if... Choose {TOPIC_B} if... (based on actual community data, not assumptions)
Then show combined stats from all three passes and the standard invitation section.
Identify from the ACTUAL RESEARCH OUTPUT:
Display in this EXACT sequence:
FIRST - What I learned (based on QUERY_TYPE):
If RECOMMENDATIONS - Show specific things mentioned with sources:
🏆 Most mentioned:
[Tool Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle1, @handle2, r/sub, blog.com
[Tool Name] - {n}x mentions
Use Case: [what it does]
Sources: @handle3, r/sub2, Complex
Notable mentions: [other specific things with 1-2 mentions]
CRITICAL for RECOMMENDATIONS:
If PROMPTING/NEWS/GENERAL - Show synthesis and patterns:
CITATION RULE: Cite sources sparingly to prove research is real.
CITATION PRIORITY (most to least preferred):
The tool's value is surfacing what PEOPLE are saying, not what journalists wrote. When both a web article and an X post cover the same fact, cite the X post.
URL FORMATTING: NEVER paste raw URLs anywhere in the output — not in synthesis, not in stats, not in sources.
🌐 Web: 10 pages — https://later.com/blog/..., https://buffer.com/...🌐 Web: 10 pages — Later, Buffer, CNN, SocialBee
Use the publication/site name, not the URL. The user doesn't need links — they need clean, readable text.BAD: "His album is set for March 20 (per Rolling Stone; Billboard; Complex)." GOOD: "His album BULLY drops March 20 — fans on X are split on the tracklist, per @honest30bgfan_" GOOD: "Ye's apology got massive traction on r/hiphopheads" OK (web, only when Reddit/X don't have it): "The Hellwatt Festival runs July 4-18 at RCF Arena, per Billboard"
Lead with people, not publications. Start each topic with what Reddit/X users are saying/feeling, then add web context only if needed. The user came here for the conversation, not the press release.
What I learned:
**{Topic 1}** — [1-2 sentences about what people are saying, per @handle or r/sub]
**{Topic 2}** — [1-2 sentences, per @handle or r/sub]
**{Topic 3}** — [1-2 sentences, per @handle or r/sub]
KEY PATTERNS from the research:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub
3. [Pattern] — per @handle
THEN - Quality Nudge (if present in the output):
If the research output contains a **🔍 Research Coverage:** block, render it verbatim right before the stats block. This tells the user which core sources are missing and how to unlock them. Do NOT render this block if it is absent from the output (100% coverage = no nudge).
THEN - Stats (right before invitation):
CRITICAL: Calculate actual totals from the research output.
[Xlikes, Yrt] from each X post, [Xpts, Ycmt] from RedditCopy this EXACTLY, replacing only the {placeholders}:
---
✅ All agents reported back!
├─ 🟠 Reddit: {N} threads │ {N} upvotes │ {N} comments
├─ 🔵 X: {N} posts │ {N} likes │ {N} reposts
├─ 🔴 YouTube: {N} videos │ {N} views │ {N} with transcripts
├─ 🎵 TikTok: {N} videos │ {N} views │ {N} likes │ {N} with captions
├─ 📸 Instagram: {N} reels │ {N} views │ {N} likes │ {N} with captions
├─ 🟡 HN: {N} stories │ {N} points │ {N} comments
├─ 🦋 Bluesky: {N} posts │ {N} likes │ {N} reposts
├─ 🇺🇸 Truth Social: {N} posts │ {N} likes │ {N} reposts
├─ 📊 Polymarket: {N} markets │ {short summary of up to 5 most relevant market odds, e.g. "Championship: 12%, #1 Seed: 28%, Big 12: 64%, vs Kansas: 71%"}
├─ 🌐 Web: {N} pages — Source Name, Source Name, Source Name
└─ 🗣️ Top voices: @{handle1} ({N} likes), @{handle2} │ r/{sub1}, r/{sub2}
---
🌐 Web: line — how to extract site names from URLs:
Strip the protocol, path, and www. — use the recognizable publication name:
https://later.com/blog/instagram-reels-trends/ → Laterhttps://socialbee.com/blog/instagram-trends/ → SocialBeehttps://buffer.com/resources/instagram-algorithms/ → Bufferhttps://www.cnn.com/2026/02/22/tech/... → CNNhttps://medium.com/the-ai-studio/... → Mediumhttps://radicaldatascience.wordpress.com/... → Radical Data Science
List as comma-separated plain names: Later, SocialBee, Buffer, CNN, Medium⚠️ WebSearch citation — ALREADY SATISFIED. DO NOT ADD A SOURCES SECTION. The WebSearch tool mandates source citation. That requirement is FULLY satisfied by the source names on the 🌐 Web: line above. Do NOT append a separate "Sources:" section at the end of your response. Do NOT list URLs anywhere. The 🌐 Web: line IS your citation. Nothing more is needed.
CRITICAL: Omit any source line that returned 0 results. Do NOT show "0 threads", "0 stories", "0 markets", or "(no results this cycle)". If a source found nothing, DELETE that line entirely - don't include it at all. NEVER use plain text dashes (-) or pipe (|). ALWAYS use ├─ └─ │ and the emoji.
SELF-CHECK before displaying: Re-read your "What I learned" section. Does it match what the research ACTUALLY says? If you catch yourself projecting your own knowledge instead of the research, rewrite it.
LAST - Invitation (adapt to QUERY_TYPE):
CRITICAL: Every invitation MUST include 2-3 specific example suggestions based on what you ACTUALLY learned from the research. Don't be generic — show the user you absorbed the content by referencing real things from the results.
If QUERY_TYPE = PROMPTING:
---
I'm now an expert on {TOPIC} for {TARGET_TOOL}. What do you want to make? For example:
- [specific idea based on popular technique from research]
- [specific idea based on trending style/approach from research]
- [specific idea riffing on what people are actually creating]
Just describe your vision and I'll write a prompt you can paste straight into {TARGET_TOOL}.
If QUERY_TYPE = RECOMMENDATIONS:
---
I'm now an expert on {TOPIC}. Want me to go deeper? For example:
- [Compare specific item A vs item B from the results]
- [Explain why item C is trending right now]
- [Help you get started with item D]
If QUERY_TYPE = NEWS:
---
I'm now an expert on {TOPIC}. Some things you could ask:
- [Specific follow-up question about the biggest story]
- [Question about implications of a key development]
- [Question about what might happen next based on current trajectory]
If QUERY_TYPE = COMPARISON:
---
I've compared {TOPIC_A} vs {TOPIC_B} using the latest community data. Some things you could ask:
- [Deep dive into {TOPIC_A} alone with /last30days {TOPIC_A}]
- [Deep dive into {TOPIC_B} alone with /last30days {TOPIC_B}]
- [Focus on a specific dimension from the comparison table]
- [Look at a different time period with --days=7 or --days=90]
If QUERY_TYPE = GENERAL:
---
I'm now an expert on {TOPIC}. Some things I can help with:
- [Specific question based on the most discussed aspect]
- [Specific creative/practical application of what you learned]
- [Deeper dive into a pattern or debate from the research]
Example invitations (to show the quality bar):
For /last30days nano banana pro prompts for Gemini:
I'm now an expert on Nano Banana Pro for Gemini. What do you want to make? For example:
- Photorealistic product shots with natural lighting (the most requested style right now)
- Logo designs with embedded text (Gemini's new strength per the research)
- Multi-reference style transfer from a mood board
Just describe your vision and I'll write a prompt you can paste straight into Gemini.
For /last30days kanye west (GENERAL):
I'm now an expert on Kanye West. Some things I can help with:
- What's the real story behind the apology letter — genuine or PR move?
- Break down the BULLY tracklist reactions and what fans are expecting
- Compare how Reddit vs X are reacting to the Bianca narrative
For /last30days war in Iran (NEWS):
I'm now an expert on the Iran situation. Some things you could ask:
- What are the realistic escalation scenarios from here?
- How is this playing differently in US vs international media?
- What's the economic impact on oil markets so far?
STOP and wait for the user to respond. Do NOT call any tools after displaying the invitation. The research script already saved raw data to ~/Documents/Last30Days/ via --save-dir.
Read their response and match the intent:
Only write a prompt when the user wants one. Don't force a prompt on someone who asked "what could happen next with Iran."
When the user wants a prompt, write a single, highly-tailored prompt using your research expertise.
If research says to use a specific prompt FORMAT, YOU MUST USE THAT FORMAT.
ANTI-PATTERN: Research says "use JSON prompts with device specs" but you write plain prose. This defeats the entire purpose of the research.
Here's your prompt for {TARGET_TOOL}:
---
[The actual prompt IN THE FORMAT THE RESEARCH RECOMMENDS]
---
This uses [brief 1-line explanation of what research insight you applied].
Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.
After delivering a prompt, offer to write more:
Want another prompt? Just tell me what you're creating next.
For the rest of this conversation, remember:
CRITICAL: After research is complete, treat yourself as an EXPERT on this topic.
When the user asks follow-up questions:
Only do new research if the user explicitly asks about a DIFFERENT topic.
After delivering a prompt, end with:
---
📚 Expert in: {TOPIC} for {TARGET_TOOL}
📊 Based on: {n} Reddit threads ({sum} upvotes) + {n} X posts ({sum} likes) + {n} YouTube videos ({sum} views) + {n} TikTok videos ({sum} views) + {n} Instagram reels ({sum} views) + {n} HN stories ({sum} points) + {n} web pages
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What this skill does:
api.scrapecreators.com) for Reddit search, subreddit discovery, and comment enrichment (requires SCRAPECREATORS_API_KEY — same key as TikTok + Instagram)api.openai.com) for Reddit discovery (fallback if no SCRAPECREATORS_API_KEY)api.x.ai) for X searchhn.algolia.com) for Hacker News story and comment discovery (free, no auth)gamma-api.polymarket.com) for prediction market discovery (free, no auth)yt-dlp locally for YouTube search and transcript extraction (no API key, public data)api.scrapecreators.com) for TikTok and Instagram search, transcript/caption extraction (same SCRAPECREATORS_API_KEY as Reddit, PAYG after 100 free API calls)reddit.com for engagement metricsWhat this skill does NOT do:
--agent for non-interactive report outputBundled scripts: scripts/last30days.py (main research engine), scripts/lib/ (search, enrichment, rendering modules), scripts/lib/vendor/bird-search/ (vendored X search client, MIT licensed)
Review scripts before first use to verify behavior.