什么是 last30days？

last30days 是一个为 Openclaw Skills 框架内的现代开发者和内容创作者设计的先进研究助手。它专注于汇总过去 30 天内来自 Reddit、X（前 T@witter）、YouTube、TikTok、Hacker News 和 Polymarket 等高信号平台的人本数据。通过关注点攒、浏览量和预测市场赔率等互动指标，它绕过了通用的 SEO 内容，挖掘出人们当前实际在构建、讨论和推荐的内容。

该技能不仅是简单的搜索，它还是一个合成引擎。它解析用户意图，以区分新闻采集、产品推荐和提示词工程。无论您是在寻找最新的 Claude Code 技术，还是通过预测市场跟踪地缘z治变化，此工具都能将原始社交数据转化为结构化知识库，并提供针对您的特定工具量身定制的、可直接复制粘贴的提示词。

下载入口:https://github.com/openclaw/skills/tree/main/skills/mvanhorn/last30days-official

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

last30days 应用场景

• 根据热门社区技术，为图像生成器或编码助手生成高性能 AI 提示词。
• 通过分析 Reddit 和 X 上针对特定产品发布的讨论和情绪，进行快速市场调研。
• 利用 Hacker News 评论和 Polymarket 赔率实时跟踪技术和金融领域的动态。
• 通过汇总 YouTube 文本和 TikTok 趋势，为简报或技术报告创建全面的研究简报。

1. 意图解析：助手分析用户请求，以识别特定主题、提及的任何目标工具以及所需的查询类型（提示词、推荐、新闻或常规）。
2. 来源解析：对于品牌或创作者等实体，它执行针对性搜索以解析官方 X 账号，确保直接检索数据。
3. 多平台执行：运行基于 Python 的研究引擎，在前台查询社交 API 并从视频内容中提取文本。
4. 网络补充：该技能执行针对性网络搜索，利用技术文档、博客文章和官方新闻填补空白。
5. 评判代理合成：内部逻辑层根据参与度（点攒/收藏）对来源进行加权，并识别跨平台模式以寻找最强信号。
6. 专家输出：该技能生成发现摘要、参与度统计数据，并邀请创建特定提示词或深入研究子话题。

last30days 配置指南

要将其集成到您的工作流中，请确保已配置必要的环境变量。此技能是 Openclaw Skills 集合的核心部分，需要 ScrapeCreators API 密钥才能访问社交数据。

# 设置您的主 API 密钥
export SCRAPECREATORS_API_KEY='your_api_key_here'

# 可选：增强网络搜索结果
export BRAVE_API_KEY='your_api_key_here'
export OPENROUTER_API_KEY='your_api_key_here'

# 用于 X/T@witter 深度搜索（可选）
export AUTH_TOKEN='your_twitter_auth_token'
export CT0='your_twitter_ct0_token'

该技能需要 Python 3 和 Node.js 才能有效执行捆绑的研究脚本。

last30days 数据架构与分类体系

该技能维护结构化数据层级，以确保研究具有持久性和可访问性。所有研究会话都保存在本地，以便于未来的专家模式交互。

name: last30days
version: "2.9.2"
description: "Research a topic from the last 30 days. Also triggered by 'last30'. Sources: Reddit, X, YouTube, TikTok, In@stagram, Hacker News, Polymarket, web. Become an expert and write copy-paste-ready prompts."
argument-hint: 'last30 AI video tools, last30 best project management tools'
allowed-tools: Bash, Read, Write, AskUserQuestion, WebSearch
homepage: https://github.com/mvanhorn/last30days-skill
repository: https://github.com/mvanhorn/last30days-skill
author: mvanhorn
license: MIT
user-invocable: true
metadata:
  openclaw:
    emoji: "??"
    requires:
      env:
        - SCRAPECREATORS_API_KEY
      optionalEnv:
        - OPENAI_API_KEY
        - XAI_API_KEY
        - OPENROUTER_API_KEY
        - PARALLEL_API_KEY
        - BRAVE_API_KEY
        - APIFY_API_TOKEN
        - AUTH_TOKEN
        - CT0
      bins:
        - node
        - python3
    primaryEnv: SCRAPECREATORS_API_KEY
    files:
      - "scripts/*"
    homepage: https://github.com/mvanhorn/last30days-skill
    tags:
      - research
      - reddit
      - x
      - you@tube
      - tiktok
      - instagram
      - hackernews
      - polymarket
      - trends
      - prompts

last30days v2.9.4: Research Any Topic from the Last 30 Days

Permissions overview: Reads public web/platform data and optionally saves research briefings to ~/Documents/Last30Days/. X/T@witter search uses optional user-provided tokens (AUTH_TOKEN/CT0 env vars) — no browser session access. All credential usage and data writes are documented in the Security & Permissions section.

Research ANY topic across Reddit, X, YouTube, TikTok, 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, TikTok, 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/T@witter 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/T@witter 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}

Agent Mode (--agent flag)

If --agent appears in ARGUMENTS (e.g., /last30days plaud granola --agent):

1. Skip the intro display block ("I'll research X across Reddit...")
2. Skip any AskUserQuestion calls - use TARGET_TOOL = "unknown" if not specified
3. Run the research script and WebSearch exactly as normal
4. Skip the "WAIT FOR USER RESPONSE" pause
5. Skip the follow-up invitation ("I'm now an expert on X...")
6. Output the complete research report and stop - do not wait for further input

Agent 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, 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}

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.

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 r
  "." r
  "${CLAUDE_PLUGIN_ROOT:-}" r
  "$HOME/.claude/skills/last30days" r
  "$HOME/.agents/skills/last30days" r
  "$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 --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:

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

Read the ENTIRE output. It contains EIGHT data sections in this order: Reddit items, X items, YouTube items, TikTok items, In@stagram 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, and optional transcript snippet. 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.

In@stagram 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. In@stagram provides unique creator/influencer perspective — weight it alongside TikTok.

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 a separate "Sources:" block — instead, include the top 3-5 web source names as inline links on the ?? Web: stats line (see stats format below). The WebSearch tool requires citation; satisfy it there, not as a trailing section.

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 TikTok sources HIGH (they have views, likes, and caption content — viral signal)

4. Weight WebSearch sources LOWER (no engagement data)

5. 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.

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

7. Note any contradictions between sources

8. Extract the top 3-5 actionable insights

9. 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

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):

1. @handles from X — "per @handle" (these prove the tool's unique value)
2. r/subreddits from Reddit — "per r/subreddit" (when citing Reddit, prefer quoting top comments over just the thread title)
3. YouTube channels — "per [channel name] on YouTube" (transcript-backed insights)
4. TikTok creators — "per @creator on TikTok" (viral/trending signal)
5. In@stagram creators — "per @creator on In@stagram" (influencer/creator signal)
6. HN discussions — "per HN" or "per hn/username" (developer community signal)
7. Polymarket — "Polymarket has X at Y% (up/down Z%)" with specific odds and movement
8. Web sources — ONLY when Reddit/X/YouTube/TikTok/In@stagram/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 anywhere in the output — not in synthesis, not in stats, not in sources.

• BAD: "per https://www.rollingstone.com/music/music-news/kanye-west-bully-1235506094/"
• GOOD: "per Rolling Stone"
• BAD stats line: ?? Web: 10 pages — https://later.com/blog/..., https://buffer.com/...
• GOOD stats line: ?? 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 - 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
├─ ?? TikTok: {N} videos │ {N} views │ {N} likes │ {N} with captions
├─ ?? In@stagram: {N} reels │ {N} views │ {N} likes │ {N} with captions
├─ ?? 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 — 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/ → Later
• https://socialbee.com/blog/instagram-trends/ → SocialBee
• https://buffer.com/resources/instagram-algorithms/ → Buffer
• https://www.cnn.com/2026/02/22/tech/... → CNN
• https://medium.com/the-ai-studio/... → Medium
• https://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 = 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

**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`.

---

## 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, treat yourself as 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} TikTok videos ({sum} views) + {n} In@stagram reels ({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 ScrapeCreators API (`api.scrapecreators.com`) for Reddit search, subreddit discovery, and comment enrichment (requires SCRAPECREATORS_API_KEY — same key as TikTok + In@stagram)
- Legacy: Sends search queries to OpenAI's Responses API (`api.openai.com`) for Reddit discovery (fallback if no SCRAPECREATORS_API_KEY)
- Sends search queries to T@witter's GraphQL API (via optional user-provided AUTH_TOKEN/CT0 env vars — no browser session access) 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)
- Sends search queries to ScrapeCreators API (`api.scrapecreators.com`) for TikTok and In@stagram search, transcript/caption extraction (same SCRAPECREATORS_API_KEY as Reddit, PAYG after 100 free credits)
- 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)
- Saves research briefings as .md files to ~/Documents/Last30Days/

**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)
- TikTok and In@stagram sources require SCRAPECREATORS_API_KEY (same key covers both; 100 free credits, then PAYG)
- Can be invoked autonomously by agents via the Skill tool (runs inline, not forked); pass `--agent` for non-interactive report output

**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.