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x-collect

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更新时间2026年2月11日 02:33

X/Twitter topic intelligence tool. Two search modes: Grok (quick AI-powered search) and Actionbook DOM scraping (detailed multi-round collection with search operators). Outputs a structured intel report (JSONL + Markdown) with content opportunity analysis. Use when the user wants to research a topic on X/Twitter, find what's performing, or scout trending discussions before creating content. Triggers on: /x-collect, requests to research X/Twitter trends for a topic.

安装

用 Codex 或 Claude 帮你安装复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。

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SamCuipogobongo

SamCuipogobongo/x-collect

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X Topic Intelligence

Research what's trending and performing on X/Twitter for any topic. Two search backends available — pick based on the task complexity.

Usage

/x-collect [topic]        # auto-select mode based on topic complexity
/x-collect                # interactive mode - asks for topic

When no topic is provided, use AskUserQuestion: "What topic do you want to research on X/Twitter? (e.g., 'AI agents', 'vibe coding', 'indie hacking')"

Search Mode Selection

	Grok Mode (quick)	Actionbook Scrape Mode (full)
Speed	~60s single query	~5-10 min multi-round
Best for	Quick overview, trend check, "what's hot"	Systematic collection, precise metrics, dataset building
Data quality	AI-summarized, may miss some posts	Raw DOM data, exact engagement numbers, includes views
Search operators	No (natural language only)	Yes (`min_faves:`, `filter:blue_verified`, date ranges, etc.)
Output	5-15 posts per query	10-15 posts per round, 3-5 rounds
Views count	Not available	Available

Auto-selection rules

Use Grok Mode when:

User asks a simple question ("what's trending about X?", "find popular posts about Y")
Quick exploration / first look at a topic
Single broad query is sufficient
User explicitly says "quick" or "fast"

Use Actionbook Scrape Mode when:

User wants systematic multi-round collection (viral + trending + KOL + hook study)
Need precise engagement thresholds (min_faves:100, filter:blue_verified)
Need views count
Building a JSONL dataset for later analysis
User explicitly says "detailed", "full", or "all rounds"

When unsure, default to Grok Mode — it's faster and sufficient for most requests. Escalate to Actionbook Scrape if Grok results are insufficient or user asks for more.

Prerequisites

Dependency	Purpose	Required
Claude Code	Runtime	Yes
Actionbook CLI	Browser automation (`actionbook` command)	Yes
Actionbook Extension	Chrome extension + bridge for controlling user's browser	Yes
Logged-in X/Twitter session	User's Chrome must have an active x.com session	Yes
X Premium (for Grok)	Grok search requires Premium subscription	Only for Grok Mode

Pre-flight Check

Before starting any round, verify the extension bridge is running:

actionbook extension ping

If not connected, inform the user:

"Actionbook extension bridge is not running. Please run actionbook extension serve in a terminal and ensure the Chrome extension is connected."

Grok Mode (Quick Search)

Ask Grok on x.com to search X and return structured results. One query, ~60 seconds.

Grok Workflow

1. Navigate to Grok

actionbook --extension browser open "https://x.com/i/grok"

Wait 4 seconds for page load, then verify textarea exists:

actionbook --extension browser eval "document.querySelector('textarea') ? 'ready' : 'not_ready'"

Use browser open (new tab) instead of browser goto to avoid "Cannot access chrome:// URL" errors.

2. Input the Question

Craft a Grok prompt that returns structured data. Template:

Search X for the most popular posts about [TOPIC] in the past [TIME_RANGE]. Show me the top [N] posts with the most engagement (likes + retweets). For each post, include: the author handle, post text, likes count, retweets count, and the post URL.

Set the value via JS (React state-safe):

actionbook --extension browser eval "
  var textarea = document.querySelector('textarea');
  if (textarea) {
    var setter = Object.getOwnPropertyDescriptor(window.HTMLTextAreaElement.prototype, 'value').set;
    setter.call(textarea, 'YOUR GROK PROMPT HERE');
    textarea.dispatchEvent(new Event('input', { bubbles: true }));
    'ok'
  } else { 'no_textarea' }
"

3. Send the Question

actionbook --extension browser eval "
  var btns = document.querySelectorAll('button');
  var sent = false;
  for (var i = 0; i < btns.length; i++) {
    var svg = btns[i].querySelector('svg');
    var rect = btns[i].getBoundingClientRect();
    if (svg && rect.width > 20 && rect.width < 60 && rect.height > 20 && rect.height < 60 && rect.x > 600) {
      btns[i].click();
      sent = true;
      break;
    }
  }
  sent ? 'sent' : 'not_found'
"

If not_found, take a screenshot (actionbook --extension browser screenshot) and debug visually.

4. Wait for Response

Grok takes 10-60 seconds (longer when searching X). Poll every 8 seconds:

actionbook --extension browser eval "document.querySelector('main') ? document.querySelector('main').innerText.length : 0"

Response is ready when length > 500. Timeout after 90 seconds and extract whatever is available.

5. Extract Response

actionbook --extension browser eval "document.querySelector('main').innerText"

6. Parse into JSONL Schema

Parse Grok's text response and map each post to the standard JSONL schema. For fields Grok doesn't provide:

views: set to "0" (Grok doesn't return views)
replies: set to 0 if not provided
posted_at: set to null if not provided
angle and key_takeaway: analyze and fill in yourself
format: infer from text length and content (thread if mentions thread/continues, else single)

Actionbook Scrape Mode (Full Collection)

Use Actionbook Extension mode to control the user's Chrome browser, navigate x.com search, and extract tweet data directly from the DOM.

General Steps (repeat for each search round)

Navigate — actionbook --extension browser goto "<search-url>" to the x.com search URL
Wait — sleep 3 then actionbook --extension browser wait "article[data-testid='tweet']" until tweets appear
Extract — actionbook --extension browser eval "<JS>" to run extraction script
Scroll + Extract more — if fewer than 10 tweets, scroll and extract again

Tweet Extraction Script

Use actionbook --extension browser eval with this JS to extract tweets:

actionbook --extension browser eval "
(function() {
  var tweets = document.querySelectorAll('article[data-testid=\"tweet\"]');
  return Array.from(tweets).slice(0, 20).map(function(t) {
    var nameEl = t.querySelector('[data-testid=\"User-Name\"]');
    var handleLink = nameEl ? nameEl.querySelector('a[href^=\"/\"][tabindex=\"-1\"]') : null;
    var handle = handleLink ? handleLink.getAttribute('href').replace('/', '') : null;
    var displayName = nameEl ? (nameEl.querySelector('a span') || {}).innerText || null : null;
    var textEl = t.querySelector('[data-testid=\"tweetText\"]');
    var text = textEl ? textEl.innerText : '';
    var timeEl = t.querySelector('time');
    var time = timeEl ? timeEl.getAttribute('datetime') : null;
    var linkEl = timeEl ? timeEl.closest('a') : null;
    var tweetLink = linkEl ? linkEl.getAttribute('href') : null;
    var replyCount = (t.querySelector('[data-testid=\"reply\"] span') || {}).innerText || '0';
    var retweetCount = (t.querySelector('[data-testid=\"retweet\"] span') || {}).innerText || '0';
    var likeCount = (t.querySelector('[data-testid=\"like\"] span') || {}).innerText || '0';
    var viewsEl = t.querySelector('a[href*=\"/analytics\"]');
    var viewsLabel = viewsEl ? viewsEl.getAttribute('aria-label') || '' : '';
    var viewsMatch = viewsLabel.match(/([\d,.]+[KMB]?)\s*view/i);
    var views = viewsMatch ? viewsMatch[1] : '0';
    return {
      handle: handle ? '@' + handle : null,
      display_name: displayName,
      text: text.substring(0, 200),
      posted_at: time,
      url: tweetLink ? 'https://x.com' + tweetLink : null,
      replies: replyCount, retweets: retweetCount, likes: likeCount, views: views
    };
  }).filter(function(t) { return t.text && t.handle; });
})()
"

Scrolling to Load More Tweets

# Scroll down 1500px, wait, repeat 3 times
actionbook --extension browser eval "window.scrollBy(0, 1500)"
sleep 2
actionbook --extension browser eval "window.scrollBy(0, 1500)"
sleep 2
actionbook --extension browser eval "window.scrollBy(0, 1500)"
sleep 2
# Then re-run extraction script above and deduplicate by URL

Note: X's DOM structure may change. If selectors break, use actionbook --extension browser snapshot to read the accessibility tree, or actionbook --extension browser screenshot to visually inspect and adjust selectors.

Search Operators Quick Reference

Use these operators in the search URL q= parameter (URL-encoded). Combine freely.

Noise Filters (always recommended)

Operator	Effect
`-filter:replies`	Exclude replies — show only original posts
`-filter:nativeretweets`	Exclude retweets — show only original content
`lang:en`	Limit to English (use `lang:zh` for Chinese, etc.)

Engagement Thresholds

Operator	Effect
`min_faves:N`	Minimum N likes
`min_retweets:N`	Minimum N reposts
`min_replies:N`	Minimum N replies

Content Format Filters

Operator	Effect
`-filter:media -filter:links`	Pure text only (best for hook/copy study)
`filter:images`	Posts with images
`filter:videos`	Posts with video
`-filter:links`	Exclude posts with outbound links
`filter:blue_verified`	Only X Premium (blue checkmark) accounts

Time Filters

Operator	Effect
`within_time:24h`	Last 24 hours only
`within_time:7d`	Last 7 days
`since:YYYY-MM-DD`	After specific date
`until:YYYY-MM-DD`	Before specific date

Other Useful Operators

Operator	Effect
`?`	Posts containing a question
`from:username`	Posts from a specific account
`to:username`	Replies to a specific account
`"exact phrase"`	Exact phrase match
`(A OR B)`	Either term (OR must be uppercase)
`-keyword`	Exclude posts containing keyword
`conversation_id:ID`	All posts in a thread (use tweet ID)

Intelligence Gathering Rounds (Actionbook Scrape Mode)

Run 3 core rounds + optional bonus rounds. All queries include -filter:replies -filter:nativeretweets by default to eliminate noise and surface only original content.

Grok Mode alternative: If using Grok Mode, skip these rounds entirely. Instead, craft 1-3 targeted Grok prompts covering the same ground (e.g., "top viral posts about [topic]", "trending [topic] posts in last 24h", "which verified accounts are posting about [topic]").

Core Rounds

Round 1: Top / Viral Posts

Query: [topic] -filter:replies -filter:nativeretweets lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

X's "Top" tab — algorithmically ranked popular tweets. Extract 10-15 tweets.

Focus: Who posted, what angle, engagement numbers, the hook (first 1-2 sentences).

Round 2: Trending Posts (Last 24h)

Query: [topic] -filter:replies -filter:nativeretweets min_faves:50 since:[YESTERDAY] lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

The hottest posts from the last 24 hours. Use since:YYYY-MM-DD (yesterday's date) combined with min_faves:50 and the Top tab sort — this returns only posts with proven engagement, ranked by X's algorithm. Extract 10-15 tweets.

Focus: Fresh angles, emerging debates, which posts are accelerating right now.

Adjust min_faves: threshold: use min_faves:10 for niche topics, min_faves:50 for mainstream. Do NOT use the "Latest" tab for this round — it returns zero-engagement noise.

Round 3: KOL / Verified High-Engagement Posts

Query: [topic] filter:blue_verified min_faves:100 -filter:replies -filter:nativeretweets lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

Only X Premium (blue checkmark) accounts with 100+ likes. Premium accounts get 10x more reach, so their content patterns are the most worth studying.

Focus: Who the KOLs are, what positions they hold, their framing style.

Adjust threshold: min_faves:50 for niche topics, min_faves:500 for mainstream. If filter:blue_verified returns too few results, drop it and keep only min_faves:.

Bonus Rounds (Optional — pick based on goal)

Round 4: Hook Study — Pure Text Viral Posts

Query: [topic] -filter:media -filter:links -filter:replies -filter:nativeretweets min_faves:500 lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

Pure text posts with 500+ likes — no images, no videos, no links. These succeed entirely on copy quality. Text posts have the highest average engagement rate (3.24%) on X.

Focus: Hook patterns (first line), sentence structure, use of numbers, storytelling format. Extract 10 tweets.

When to use: Before writing content, to study what copy patterns perform best for this topic.

Round 5: Conversation Starters — High-Reply Posts

Query: [topic] min_replies:30 -filter:replies -filter:nativeretweets lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

Posts that generated the most replies. In X's algorithm, a reply that gets an author reply back is weighted 75x a like — making conversation-driving posts the highest-signal content.

Focus: What questions or takes drive discussion, common debate points, audience pain points. Extract 10 tweets.

When to use: To understand what the audience cares about and what angles spark debate.

Data Extraction

For each tweet, extract and then analyze to determine:

Field	Type	Description
`url`	string	`https://x.com/[user]/status/[id]` (unique key)
`text`	string	First 200 chars of tweet text
`intel_type`	enum	`viral_post` \| `trending_post` \| `kol_post` \| `hook_study` \| `conversation_starter`
`topic`	string	The search topic
`account`	string	`@handle`
`display_name`	string	Display name
`angle`	string	1-sentence summary of the angle/framing (your analysis)
`format`	string	`thread` \| `single` \| `quote_tweet` \| `poll`
`likes`	number	Like count
`retweets`	number	Retweet count
`replies`	number	Reply count
`views`	string	View count (may include K/M suffix)
`posted_at`	string	ISO timestamp
`key_takeaway`	string	Why this is noteworthy for content creation (your analysis)
`collected`	string	Today's date (YYYY-MM-DD)

Output

All output goes to ./x-collect-data/ in the current working directory (created on first run).

JSONL — `./x-collect-data/intel.jsonl`

One JSON object per line:

{"url":"https://x.com/bcherny/status/123","text":"I'm Boris and I created Claude Code...","intel_type":"viral_post","topic":"Claude Code","account":"@bcherny","display_name":"Boris Cherny","angle":"Creator shares vanilla setup with 15+ parallel sessions","format":"thread","likes":5800,"retweets":1200,"replies":340,"views":"6.5M","posted_at":"2026-01-15T10:30:00Z","key_takeaway":"Behind-the-scenes from creator + actionable tips = mega engagement","collected":"2026-02-08"}

Dedup: Before appending, read existing JSONL and build a set of existing URLs. Skip duplicates.

Markdown — `./x-collect-data/intel.md`

Re-render after each run. Group by intel_type, sort by likes descending.

Count each group's entries carefully — header count must exactly match row count.

# X Intelligence: [topic]

> Last updated: YYYY-MM-DD | Total: N tweets

## Top / Viral Posts (N)

| Account | Text (truncated) | Likes | RTs | Replies | Views | Format |
|---------|-----------------|-------|-----|---------|-------|--------|
| [@user](tweet_url) | First 80 chars... | 5.8K | 1.2K | 340 | 6.5M | thread |

## Trending Posts — 24h (N)

| Account | Text (truncated) | Likes | RTs | Replies | Views | Format |
|---------|-----------------|-------|-----|---------|-------|--------|
| [@user](tweet_url) | ... | ... | ... | ... | ... | ... |

## KOL / Verified Posts (N)

| Account | Text (truncated) | Likes | RTs | Replies | Views | Format |
|---------|-----------------|-------|-----|---------|-------|--------|
| [@user](tweet_url) | ... | ... | ... | ... | ... | ... |

## Hook Study — Pure Text (N) *(if collected)*

| Account | Text (truncated) | Likes | RTs | Replies | Views |
|---------|-----------------|-------|-----|---------|-------|
| [@user](tweet_url) | First 120 chars... | ... | ... | ... | ... |

## Conversation Starters (N) *(if collected)*

| Account | Text (truncated) | Likes | RTs | Replies | Views |
|---------|-----------------|-------|-----|---------|-------|
| [@user](tweet_url) | ... | ... | ... | ... | ... |

---

## Content Opportunity Summary

Based on the N tweets above:

1. **Hot angles**: [2-3 angles getting the most likes/RTs — cite top posts as: @handle — "first 60 chars of tweet text" ([link](tweet_url))]
2. **Content gaps**: [angles with engagement but few posts — underserved demand — cite examples as: @handle — "first 60 chars" ([link](tweet_url))]
3. **Recommended format**: [thread / single / hot-take based on what's performing — cite top-performing examples as: @handle — "first 60 chars" ([link](tweet_url))]
4. **Top KOLs**: [5 accounts with the most engagement — each as: @handle — "first 60 chars" ([link](tweet_url)) (likes)]
5. **Hook patterns**: [common first-line patterns — cite examples as: @handle — "first 60 chars" ([link](tweet_url))]
6. **Conversation drivers**: [questions/takes that generate the most replies — cite as: @handle — "first 60 chars" ([link](tweet_url))]
7. **Avoid**: [overdone angles with declining engagement]

> **Rule**: Every reference to a specific tweet MUST use the three-part format: `@handle — "tweet text excerpt" ([link](tweet_url))` — showing the username, the reference post content, and a clickable link separately.

Output Message

After collection, display:

X Intelligence for "[topic]" — [N] tweets collected (via [Grok / Actionbook Scrape]):
  Top / viral:          X
  Trending (24h):       Y
  KOL / verified:       Z
  Hook study:           A  (if collected)
  Conversation starters: B  (if collected)

Top post: [@handle] — [first 60 chars of text] (likes: N)

Data saved to:
  ./x-collect-data/intel.jsonl
  ./x-collect-data/intel.md

name	x-collect
description	X/Twitter topic intelligence tool. Two search modes: Grok (quick AI-powered search) and Actionbook DOM scraping (detailed multi-round collection with search operators). Outputs a structured intel report (JSONL + Markdown) with content opportunity analysis. Use when the user wants to research a topic on X/Twitter, find what's performing, or scout trending discussions before creating content. Triggers on: /x-collect, requests to research X/Twitter trends for a topic.
allowed-tools	["Read","Write","Edit","Bash","Glob","Grep","AskUserQuestion"]

X Topic Intelligence

Research what's trending and performing on X/Twitter for any topic. Two search backends available — pick based on the task complexity.

Usage

/x-collect [topic]        # auto-select mode based on topic complexity
/x-collect                # interactive mode - asks for topic

When no topic is provided, use AskUserQuestion: "What topic do you want to research on X/Twitter? (e.g., 'AI agents', 'vibe coding', 'indie hacking')"

Search Mode Selection

	Grok Mode (quick)	Actionbook Scrape Mode (full)
Speed	~60s single query	~5-10 min multi-round
Best for	Quick overview, trend check, "what's hot"	Systematic collection, precise metrics, dataset building
Data quality	AI-summarized, may miss some posts	Raw DOM data, exact engagement numbers, includes views
Search operators	No (natural language only)	Yes (`min_faves:`, `filter:blue_verified`, date ranges, etc.)
Output	5-15 posts per query	10-15 posts per round, 3-5 rounds
Views count	Not available	Available

Auto-selection rules

Use Grok Mode when:

User asks a simple question ("what's trending about X?", "find popular posts about Y")
Quick exploration / first look at a topic
Single broad query is sufficient
User explicitly says "quick" or "fast"

Use Actionbook Scrape Mode when:

User wants systematic multi-round collection (viral + trending + KOL + hook study)
Need precise engagement thresholds (min_faves:100, filter:blue_verified)
Need views count
Building a JSONL dataset for later analysis
User explicitly says "detailed", "full", or "all rounds"

When unsure, default to Grok Mode — it's faster and sufficient for most requests. Escalate to Actionbook Scrape if Grok results are insufficient or user asks for more.

Prerequisites

Dependency	Purpose	Required
Claude Code	Runtime	Yes
Actionbook CLI	Browser automation (`actionbook` command)	Yes
Actionbook Extension	Chrome extension + bridge for controlling user's browser	Yes
Logged-in X/Twitter session	User's Chrome must have an active x.com session	Yes
X Premium (for Grok)	Grok search requires Premium subscription	Only for Grok Mode

Pre-flight Check

Before starting any round, verify the extension bridge is running:

actionbook extension ping

If not connected, inform the user:

"Actionbook extension bridge is not running. Please run actionbook extension serve in a terminal and ensure the Chrome extension is connected."

Grok Mode (Quick Search)

Ask Grok on x.com to search X and return structured results. One query, ~60 seconds.

Grok Workflow

1. Navigate to Grok

actionbook --extension browser open "https://x.com/i/grok"

Wait 4 seconds for page load, then verify textarea exists:

actionbook --extension browser eval "document.querySelector('textarea') ? 'ready' : 'not_ready'"

Use browser open (new tab) instead of browser goto to avoid "Cannot access chrome:// URL" errors.

2. Input the Question

Craft a Grok prompt that returns structured data. Template:

Search X for the most popular posts about [TOPIC] in the past [TIME_RANGE]. Show me the top [N] posts with the most engagement (likes + retweets). For each post, include: the author handle, post text, likes count, retweets count, and the post URL.

Set the value via JS (React state-safe):

actionbook --extension browser eval "
  var textarea = document.querySelector('textarea');
  if (textarea) {
    var setter = Object.getOwnPropertyDescriptor(window.HTMLTextAreaElement.prototype, 'value').set;
    setter.call(textarea, 'YOUR GROK PROMPT HERE');
    textarea.dispatchEvent(new Event('input', { bubbles: true }));
    'ok'
  } else { 'no_textarea' }
"

3. Send the Question

actionbook --extension browser eval "
  var btns = document.querySelectorAll('button');
  var sent = false;
  for (var i = 0; i < btns.length; i++) {
    var svg = btns[i].querySelector('svg');
    var rect = btns[i].getBoundingClientRect();
    if (svg && rect.width > 20 && rect.width < 60 && rect.height > 20 && rect.height < 60 && rect.x > 600) {
      btns[i].click();
      sent = true;
      break;
    }
  }
  sent ? 'sent' : 'not_found'
"

If not_found, take a screenshot (actionbook --extension browser screenshot) and debug visually.

4. Wait for Response

Grok takes 10-60 seconds (longer when searching X). Poll every 8 seconds:

actionbook --extension browser eval "document.querySelector('main') ? document.querySelector('main').innerText.length : 0"

Response is ready when length > 500. Timeout after 90 seconds and extract whatever is available.

5. Extract Response

actionbook --extension browser eval "document.querySelector('main').innerText"

6. Parse into JSONL Schema

Parse Grok's text response and map each post to the standard JSONL schema. For fields Grok doesn't provide:

views: set to "0" (Grok doesn't return views)
replies: set to 0 if not provided
posted_at: set to null if not provided
angle and key_takeaway: analyze and fill in yourself
format: infer from text length and content (thread if mentions thread/continues, else single)

Actionbook Scrape Mode (Full Collection)

Use Actionbook Extension mode to control the user's Chrome browser, navigate x.com search, and extract tweet data directly from the DOM.

General Steps (repeat for each search round)

Navigate — actionbook --extension browser goto "<search-url>" to the x.com search URL
Wait — sleep 3 then actionbook --extension browser wait "article[data-testid='tweet']" until tweets appear
Extract — actionbook --extension browser eval "<JS>" to run extraction script
Scroll + Extract more — if fewer than 10 tweets, scroll and extract again

Tweet Extraction Script

Use actionbook --extension browser eval with this JS to extract tweets:

actionbook --extension browser eval "
(function() {
  var tweets = document.querySelectorAll('article[data-testid=\"tweet\"]');
  return Array.from(tweets).slice(0, 20).map(function(t) {
    var nameEl = t.querySelector('[data-testid=\"User-Name\"]');
    var handleLink = nameEl ? nameEl.querySelector('a[href^=\"/\"][tabindex=\"-1\"]') : null;
    var handle = handleLink ? handleLink.getAttribute('href').replace('/', '') : null;
    var displayName = nameEl ? (nameEl.querySelector('a span') || {}).innerText || null : null;
    var textEl = t.querySelector('[data-testid=\"tweetText\"]');
    var text = textEl ? textEl.innerText : '';
    var timeEl = t.querySelector('time');
    var time = timeEl ? timeEl.getAttribute('datetime') : null;
    var linkEl = timeEl ? timeEl.closest('a') : null;
    var tweetLink = linkEl ? linkEl.getAttribute('href') : null;
    var replyCount = (t.querySelector('[data-testid=\"reply\"] span') || {}).innerText || '0';
    var retweetCount = (t.querySelector('[data-testid=\"retweet\"] span') || {}).innerText || '0';
    var likeCount = (t.querySelector('[data-testid=\"like\"] span') || {}).innerText || '0';
    var viewsEl = t.querySelector('a[href*=\"/analytics\"]');
    var viewsLabel = viewsEl ? viewsEl.getAttribute('aria-label') || '' : '';
    var viewsMatch = viewsLabel.match(/([\d,.]+[KMB]?)\s*view/i);
    var views = viewsMatch ? viewsMatch[1] : '0';
    return {
      handle: handle ? '@' + handle : null,
      display_name: displayName,
      text: text.substring(0, 200),
      posted_at: time,
      url: tweetLink ? 'https://x.com' + tweetLink : null,
      replies: replyCount, retweets: retweetCount, likes: likeCount, views: views
    };
  }).filter(function(t) { return t.text && t.handle; });
})()
"

Scrolling to Load More Tweets

# Scroll down 1500px, wait, repeat 3 times
actionbook --extension browser eval "window.scrollBy(0, 1500)"
sleep 2
actionbook --extension browser eval "window.scrollBy(0, 1500)"
sleep 2
actionbook --extension browser eval "window.scrollBy(0, 1500)"
sleep 2
# Then re-run extraction script above and deduplicate by URL

Note: X's DOM structure may change. If selectors break, use actionbook --extension browser snapshot to read the accessibility tree, or actionbook --extension browser screenshot to visually inspect and adjust selectors.

Search Operators Quick Reference

Use these operators in the search URL q= parameter (URL-encoded). Combine freely.

Noise Filters (always recommended)

Operator	Effect
`-filter:replies`	Exclude replies — show only original posts
`-filter:nativeretweets`	Exclude retweets — show only original content
`lang:en`	Limit to English (use `lang:zh` for Chinese, etc.)

Engagement Thresholds

Operator	Effect
`min_faves:N`	Minimum N likes
`min_retweets:N`	Minimum N reposts
`min_replies:N`	Minimum N replies

Content Format Filters

Operator	Effect
`-filter:media -filter:links`	Pure text only (best for hook/copy study)
`filter:images`	Posts with images
`filter:videos`	Posts with video
`-filter:links`	Exclude posts with outbound links
`filter:blue_verified`	Only X Premium (blue checkmark) accounts

Time Filters

Operator	Effect
`within_time:24h`	Last 24 hours only
`within_time:7d`	Last 7 days
`since:YYYY-MM-DD`	After specific date
`until:YYYY-MM-DD`	Before specific date

Other Useful Operators

Operator	Effect
`?`	Posts containing a question
`from:username`	Posts from a specific account
`to:username`	Replies to a specific account
`"exact phrase"`	Exact phrase match
`(A OR B)`	Either term (OR must be uppercase)
`-keyword`	Exclude posts containing keyword
`conversation_id:ID`	All posts in a thread (use tweet ID)

Intelligence Gathering Rounds (Actionbook Scrape Mode)

Run 3 core rounds + optional bonus rounds. All queries include -filter:replies -filter:nativeretweets by default to eliminate noise and surface only original content.

Grok Mode alternative: If using Grok Mode, skip these rounds entirely. Instead, craft 1-3 targeted Grok prompts covering the same ground (e.g., "top viral posts about [topic]", "trending [topic] posts in last 24h", "which verified accounts are posting about [topic]").

Core Rounds

Round 1: Top / Viral Posts

Query: [topic] -filter:replies -filter:nativeretweets lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

X's "Top" tab — algorithmically ranked popular tweets. Extract 10-15 tweets.

Focus: Who posted, what angle, engagement numbers, the hook (first 1-2 sentences).

Round 2: Trending Posts (Last 24h)

Query: [topic] -filter:replies -filter:nativeretweets min_faves:50 since:[YESTERDAY] lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

Focus: Fresh angles, emerging debates, which posts are accelerating right now.

Adjust min_faves: threshold: use min_faves:10 for niche topics, min_faves:50 for mainstream. Do NOT use the "Latest" tab for this round — it returns zero-engagement noise.

Round 3: KOL / Verified High-Engagement Posts

Query: [topic] filter:blue_verified min_faves:100 -filter:replies -filter:nativeretweets lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

Only X Premium (blue checkmark) accounts with 100+ likes. Premium accounts get 10x more reach, so their content patterns are the most worth studying.

Focus: Who the KOLs are, what positions they hold, their framing style.

Adjust threshold: min_faves:50 for niche topics, min_faves:500 for mainstream. If filter:blue_verified returns too few results, drop it and keep only min_faves:.

Bonus Rounds (Optional — pick based on goal)

Round 4: Hook Study — Pure Text Viral Posts

Query: [topic] -filter:media -filter:links -filter:replies -filter:nativeretweets min_faves:500 lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

Pure text posts with 500+ likes — no images, no videos, no links. These succeed entirely on copy quality. Text posts have the highest average engagement rate (3.24%) on X.

Focus: Hook patterns (first line), sentence structure, use of numbers, storytelling format. Extract 10 tweets.

When to use: Before writing content, to study what copy patterns perform best for this topic.

Round 5: Conversation Starters — High-Reply Posts

Query: [topic] min_replies:30 -filter:replies -filter:nativeretweets lang:en URL: https://x.com/search?q=[URL-encoded query]&src=typed_query&f=top

Posts that generated the most replies. In X's algorithm, a reply that gets an author reply back is weighted 75x a like — making conversation-driving posts the highest-signal content.

Focus: What questions or takes drive discussion, common debate points, audience pain points. Extract 10 tweets.

When to use: To understand what the audience cares about and what angles spark debate.

Data Extraction

For each tweet, extract and then analyze to determine:

Field	Type	Description
`url`	string	`https://x.com/[user]/status/[id]` (unique key)
`text`	string	First 200 chars of tweet text
`intel_type`	enum	`viral_post` \| `trending_post` \| `kol_post` \| `hook_study` \| `conversation_starter`
`topic`	string	The search topic
`account`	string	`@handle`
`display_name`	string	Display name
`angle`	string	1-sentence summary of the angle/framing (your analysis)
`format`	string	`thread` \| `single` \| `quote_tweet` \| `poll`
`likes`	number	Like count
`retweets`	number	Retweet count
`replies`	number	Reply count
`views`	string	View count (may include K/M suffix)
`posted_at`	string	ISO timestamp
`key_takeaway`	string	Why this is noteworthy for content creation (your analysis)
`collected`	string	Today's date (YYYY-MM-DD)

Output

All output goes to ./x-collect-data/ in the current working directory (created on first run).

JSONL — `./x-collect-data/intel.jsonl`

One JSON object per line:

{"url":"https://x.com/bcherny/status/123","text":"I'm Boris and I created Claude Code...","intel_type":"viral_post","topic":"Claude Code","account":"@bcherny","display_name":"Boris Cherny","angle":"Creator shares vanilla setup with 15+ parallel sessions","format":"thread","likes":5800,"retweets":1200,"replies":340,"views":"6.5M","posted_at":"2026-01-15T10:30:00Z","key_takeaway":"Behind-the-scenes from creator + actionable tips = mega engagement","collected":"2026-02-08"}

Dedup: Before appending, read existing JSONL and build a set of existing URLs. Skip duplicates.

Markdown — `./x-collect-data/intel.md`

Re-render after each run. Group by intel_type, sort by likes descending.

Count each group's entries carefully — header count must exactly match row count.

# X Intelligence: [topic]

> Last updated: YYYY-MM-DD | Total: N tweets

## Top / Viral Posts (N)

| Account | Text (truncated) | Likes | RTs | Replies | Views | Format |
|---------|-----------------|-------|-----|---------|-------|--------|
| [@user](tweet_url) | First 80 chars... | 5.8K | 1.2K | 340 | 6.5M | thread |

## Trending Posts — 24h (N)

| Account | Text (truncated) | Likes | RTs | Replies | Views | Format |
|---------|-----------------|-------|-----|---------|-------|--------|
| [@user](tweet_url) | ... | ... | ... | ... | ... | ... |

## KOL / Verified Posts (N)

| Account | Text (truncated) | Likes | RTs | Replies | Views | Format |
|---------|-----------------|-------|-----|---------|-------|--------|
| [@user](tweet_url) | ... | ... | ... | ... | ... | ... |

## Hook Study — Pure Text (N) *(if collected)*

| Account | Text (truncated) | Likes | RTs | Replies | Views |
|---------|-----------------|-------|-----|---------|-------|
| [@user](tweet_url) | First 120 chars... | ... | ... | ... | ... |

## Conversation Starters (N) *(if collected)*

| Account | Text (truncated) | Likes | RTs | Replies | Views |
|---------|-----------------|-------|-----|---------|-------|
| [@user](tweet_url) | ... | ... | ... | ... | ... |

---

## Content Opportunity Summary

Based on the N tweets above:

1. **Hot angles**: [2-3 angles getting the most likes/RTs — cite top posts as: @handle — "first 60 chars of tweet text" ([link](tweet_url))]
2. **Content gaps**: [angles with engagement but few posts — underserved demand — cite examples as: @handle — "first 60 chars" ([link](tweet_url))]
3. **Recommended format**: [thread / single / hot-take based on what's performing — cite top-performing examples as: @handle — "first 60 chars" ([link](tweet_url))]
4. **Top KOLs**: [5 accounts with the most engagement — each as: @handle — "first 60 chars" ([link](tweet_url)) (likes)]
5. **Hook patterns**: [common first-line patterns — cite examples as: @handle — "first 60 chars" ([link](tweet_url))]
6. **Conversation drivers**: [questions/takes that generate the most replies — cite as: @handle — "first 60 chars" ([link](tweet_url))]
7. **Avoid**: [overdone angles with declining engagement]

> **Rule**: Every reference to a specific tweet MUST use the three-part format: `@handle — "tweet text excerpt" ([link](tweet_url))` — showing the username, the reference post content, and a clickable link separately.

Output Message

After collection, display:

X Intelligence for "[topic]" — [N] tweets collected (via [Grok / Actionbook Scrape]):
  Top / viral:          X
  Trending (24h):       Y
  KOL / verified:       Z
  Hook study:           A  (if collected)
  Conversation starters: B  (if collected)

Top post: [@handle] — [first 60 chars of text] (likes: N)

Data saved to:
  ./x-collect-data/intel.jsonl
  ./x-collect-data/intel.md

x-collect

X Topic Intelligence

Usage

Search Mode Selection

Auto-selection rules

Prerequisites

Pre-flight Check

Grok Mode (Quick Search)

Grok Workflow

1. Navigate to Grok

2. Input the Question

3. Send the Question

4. Wait for Response

5. Extract Response

6. Parse into JSONL Schema

Actionbook Scrape Mode (Full Collection)

General Steps (repeat for each search round)

Tweet Extraction Script

Scrolling to Load More Tweets

Search Operators Quick Reference

Noise Filters (always recommended)

Engagement Thresholds

Content Format Filters

Time Filters

Other Useful Operators

Intelligence Gathering Rounds (Actionbook Scrape Mode)

Core Rounds

Round 1: Top / Viral Posts

Round 2: Trending Posts (Last 24h)

Round 3: KOL / Verified High-Engagement Posts

Bonus Rounds (Optional — pick based on goal)

Round 4: Hook Study — Pure Text Viral Posts

Round 5: Conversation Starters — High-Reply Posts

Data Extraction

Output

JSONL — ./x-collect-data/intel.jsonl

Markdown — ./x-collect-data/intel.md

Output Message

X Topic Intelligence

Usage

Search Mode Selection

Auto-selection rules

Prerequisites

Pre-flight Check

Grok Mode (Quick Search)

Grok Workflow

1. Navigate to Grok

2. Input the Question

3. Send the Question

4. Wait for Response

5. Extract Response

6. Parse into JSONL Schema

Actionbook Scrape Mode (Full Collection)

General Steps (repeat for each search round)

Tweet Extraction Script

Scrolling to Load More Tweets

Search Operators Quick Reference

Noise Filters (always recommended)

Engagement Thresholds

Content Format Filters

Time Filters

Other Useful Operators

Intelligence Gathering Rounds (Actionbook Scrape Mode)

Core Rounds

Round 1: Top / Viral Posts

Round 2: Trending Posts (Last 24h)

Round 3: KOL / Verified High-Engagement Posts

Bonus Rounds (Optional — pick based on goal)

Round 4: Hook Study — Pure Text Viral Posts

Round 5: Conversation Starters — High-Reply Posts

Data Extraction

Output

JSONL — ./x-collect-data/intel.jsonl

Markdown — ./x-collect-data/intel.md

Output Message

JSONL — `./x-collect-data/intel.jsonl`

Markdown — `./x-collect-data/intel.md`

JSONL — `./x-collect-data/intel.jsonl`

Markdown — `./x-collect-data/intel.md`