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:

TOPIC: What they want to learn about (e.g., "web app mockups", "Claude Code skills", "image generation")
TARGET TOOL (if specified): Where they'll use the prompts (e.g., "Nano Banana Pro", "ChatGPT", "Midjourney")
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:

Weight Reddit/X sources HIGHER (they have engagement signals: upvotes, likes)
Weight YouTube sources HIGH (they have views, likes, and transcript content)
Weight WebSearch sources LOWER (no engagement data)
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.

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:

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:

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

THEN: Show Summary + Invite Vision

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:

Each item MUST have a "Sources:" line with actual @handles from X posts (e.g., @LONGLIVE47, @ByDobson)
Include subreddit names (r/hiphopheads) and web sources (Complex, Variety)
Parse @handles from research output and include the highest-engagement ones
Format naturally - tables work well for wide terminals, stacked cards for narrow

If PROMPTING/NEWS/GENERAL - Show synthesis and patterns:

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.

CITATION PRIORITY (most to least preferred):

@handles from X — "per @handle" (these prove the tool's unique value)
r/subreddits from Reddit — "per r/subreddit"
YouTube channels — "per [channel name] on YouTube" (transcript-backed insights)
HN discussions — "per HN" or "per hn/username" (developer community signal)
Polymarket — "Polymarket has X at Y% (up/down Z%)" with specific odds and movement
Web sources — ONLY when Reddit/X/YouTube/HN/Polymarket don't cover that specific fact

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 in the output.

BAD: "per https://www.rollingstone.com/music/music-news/kanye-west-bully-1235506094/"
GOOD: "per Rolling Stone"
GOOD: "per Complex" Use the publication 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 - Stats (right before invitation):

CRITICAL: Calculate actual totals from the research output.

Count posts/threads from each section
Sum engagement: parse [Xlikes, Yrt] from each X post, [Xpts, Ycmt] from Reddit
Identify top voices: highest-engagement @handles from X, most active subreddits

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
├─ 🔴 YouTube: {N} videos │ {N} views │ {N} with transcripts
├─ 🟡 HN: {N} stories │ {N} points │ {N} comments
├─ 📊 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 (supplementary)
└─ 🗣️ Top voices: @{handle1} ({N} likes), @{handle2} │ r/{sub1}, r/{sub2}
---

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 = 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?

WAIT FOR USER'S RESPONSE

After showing the stats summary with your invitation, STOP and wait for the user to respond.

WHEN USER RESPONDS

Read their response and match the intent:

If they ask a QUESTION about the topic → Answer from your research (no new searches, no prompt)
If they ask to GO DEEPER on a subtopic → Elaborate using your research findings
If they describe something they want to CREATE → Write ONE perfect prompt (see below)
If they ask for a PROMPT explicitly → Write ONE perfect prompt (see below)

Only write a prompt when the user wants one. Don't force a prompt on someone who asked "what could happen next with Iran."

Writing a Prompt

When the user wants a prompt, write a single, highly-tailored prompt using your research expertise.

CRITICAL: Match the FORMAT the research recommends

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.

Quality Checklist (run before delivering):

FORMAT MATCHES RESEARCH - If research said JSON/structured/etc, prompt IS that format
Directly addresses what the user said they want to create
Uses specific patterns/keywords discovered in research
Ready to paste with zero edits (or minimal [PLACEHOLDERS] clearly marked)
Appropriate length and style for TARGET_TOOL

Output Format:

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].

IF USER ASKS FOR MORE OPTIONS

Only if they ask for alternatives or more prompts, provide 2-3 variations. Don't dump a prompt pack unless requested.

AFTER EACH PROMPT: Stay in Expert Mode

After delivering a prompt, offer to write more:

Want another prompt? Just tell me what you're creating next.

CONTEXT MEMORY

For the rest of this conversation, remember:

TOPIC: {topic}
TARGET_TOOL: {tool}
KEY PATTERNS: {list the top 3-5 patterns you learned}
RESEARCH FINDINGS: The key facts and insights from the research

CRITICAL: After research is complete, you are now an EXPERT on this topic.

When the user asks follow-up questions:

DO NOT run new WebSearches - you already have the research
Answer from what you learned - cite the Reddit threads, X posts, and web sources
If they ask a question - answer it from your research findings
If they ask for a prompt - write one using your expertise

Only do new research if the user explicitly asks about a DIFFERENT topic.

Output Summary Footer (After Each Prompt)

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} HN stories ({sum} points) + {n} web pages

Want another prompt? Just tell me what you're creating next.

Security & Permissions

What this skill does:

Sends search queries to OpenAI's Responses API (api.openai.com) for Reddit discovery
Sends search queries to Twitter's GraphQL API (via browser cookie auth) or xAI's API (api.x.ai) for X search
Sends search queries to Algolia HN Search API (hn.algolia.com) for Hacker News story and comment discovery (free, no auth)
Sends search queries to Polymarket Gamma API (gamma-api.polymarket.com) for prediction market discovery (free, no auth)
Runs yt-dlp locally for YouTube search and transcript extraction (no API key, public data)
Optionally sends search queries to Brave Search API, Parallel AI API, or OpenRouter API for web search
Fetches public Reddit thread data from reddit.com for engagement metrics
Stores research findings in local SQLite database (watchlist mode only)

What this skill does NOT do:

Does not post, like, or modify content on any platform
Does not access your Reddit, X, or YouTube accounts
Does not share API keys between providers (OpenAI key only goes to api.openai.com, etc.)
Does not log, cache, or write API keys to output files
Does not send data to any endpoint not listed above
Hacker News and Polymarket sources are always available (no API key, no binary dependency)
Cannot be invoked autonomously by the agent (disable-model-invocation: true)

Bundled 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.