Trending Ad Hook Spotter

Scan social platforms for what's trending in your space right now — viral posts, hot debates, breaking news, memes — and translate each trend into a concrete ad hook you can run while the topic is still hot.

Core principle: The highest-performing ads ride cultural and industry moments. This skill finds those moments before your competitors do and tells you exactly how to capitalize.

When to Use

• "What's trending in our space that we could run ads about?"
• "Find viral hooks for our paid campaigns"
• "What topics are hot in [industry] right now?"
• "I want to ride a trend with a paid campaign"
• "What should we be running ads about this week?"

Prerequisites

• Environment variable: APIFY_API_TOKEN — required for Reddit scraping (optional if using only web_search + HN API)
• Web search access — your AI agent must support web_search or equivalent for Twitter/X and LinkedIn lookups
• No API key needed for Hacker News (Algolia HN API is free and public)

Phase 0: Intake

1. Your product — Name + one-line description
2. Industry/category — What space are you in? (e.g., "AI sales tools", "developer infrastructure")
3. ICP keywords — 5-10 keywords that define your buyer's world
4. Competitor names — So we can spot when they become part of a trend
5. Platforms to scan (default: all):
   • Twitter/X
   • Reddit (specific subreddits if known)
   • LinkedIn
   • Hacker News
6. Content velocity — How fast can you create ads? (Same-day / 2-3 days / Weekly)

Phase 1: Social Scanning

1A: Twitter/X Trend Scan (web_search)

Use web_search with site:x.com or site:twitter.com to find trending posts — no scraper or credentials needed:

# Industry trending topics
web_search: "<industry keyword> (viral OR trending OR hot take OR thread) site:x.com"

# Competitor mentions (momentum signals)
web_search: "<competitor1> OR <competitor2> (raised OR launched OR shut down OR acquired OR outage) site:x.com"

# Pain/frustration spikes
web_search: "<category> (broken OR frustrating OR tired of OR switched from) site:x.com"

Run 3-5 queries to cover:

• Industry trending topics and hot takes
• Competitor momentum signals (launches, outages, funding)
• Pain/frustration spikes in the category
• Viral threads with high engagement

Score each tweet/thread by engagement velocity (likes + retweets relative to account size and age).

1B: Reddit Trend Scan (Apify)

Use the trudax/reddit-scraper-lite actor to scan relevant subreddits for hot posts:

Browse specific subreddits (for trending/hot posts):

POST https://api.apify.com/v2/acts/trudax~reddit-scraper-lite/runs?token=$APIFY_API_TOKEN
Content-Type: application/json

{
  "startUrls": [
    {"url": "https://www.reddit.com/r/SUBREDDIT1/hot/"},
    {"url": "https://www.reddit.com/r/SUBREDDIT2/hot/"}
  ],
  "maxItems": 30
}

Search by keyword (for specific topics):

POST https://api.apify.com/v2/acts/trudax~reddit-scraper-lite/runs?token=$APIFY_API_TOKEN
Content-Type: application/json

{
  "searches": ["<industry keyword> OR <competitor>"],
  "maxItems": 30
}

Poll until the run finishes:

GET https://api.apify.com/v2/acts/trudax~reddit-scraper-lite/runs/{RUN_ID}?token=$APIFY_API_TOKEN

When status is SUCCEEDED, fetch results:

GET https://api.apify.com/v2/datasets/{DATASET_ID}/items?token=$APIFY_API_TOKEN

Output fields: Each item has dataType ("post" or "comment"), title (posts only), body, communityName, upVotes, numberOfComments (posts), url, createdAt.

Look for:

• Posts with unusually high upvote/comment ratios
• "What do you use for [X]?" threads (buying intent)
• Complaint threads about incumbents
• "I just switched from X to Y" posts

1C: LinkedIn Trend Scan (web_search)

Use web_search with site:linkedin.com/posts to find high-engagement KOL posts — no scraper or credentials needed:

web_search: "<industry keyword> site:linkedin.com/posts"
web_search: "<competitor_name> site:linkedin.com/posts"
web_search: "<KOL_name> <industry keyword> site:linkedin.com/posts"
web_search: "<trending topic> site:linkedin.com/pulse"

Run queries for:

• 5-10 key opinion leaders (KOLs) in the space — search their names + topic keywords
• Industry-level keyword searches to find viral posts
• Competitor mentions from thought leaders

Identify high-engagement posts on topics relevant to your product category.

1D: Hacker News Scan (Algolia HN API)

Use the free Algolia HN Search API — no API key needed:

Search for relevant stories:

GET https://hn.algolia.com/api/v1/search?query=KEYWORD&tags=story&hitsPerPage=20

Search for recent stories (past 7 days):

GET https://hn.algolia.com/api/v1/search?query=KEYWORD&tags=story&numericFilters=created_at_i>UNIX_TIMESTAMP_7_DAYS_AGO&hitsPerPage=20

Get front page stories (current trending):

GET https://hn.algolia.com/api/v1/search?tags=front_page&hitsPerPage=30

The response includes points, num_comments, title, url, and created_at for each story. Sort by points to find the highest-engagement discussions.

Run queries for:

• Each ICP keyword
• Each competitor name
• The product category
• Check front page for anything tangentially related

Phase 2: Trend Identification & Scoring

Trend Detection Framework

Group collected signals into trends. A "trend" is:

• A topic appearing across 2+ platforms within the past 7 days
• A single post/thread with exceptional engagement (10x+ the norm)
• A breaking event (funding, acquisition, outage, launch) with cascading conversation

Score Each Trend

Factor	Weight	Description
Recency	25%	How fresh? (< 24h = max, > 7 days = low)
Velocity	25%	Is engagement accelerating or decelerating?
Cross-platform	20%	Appearing on multiple platforms?
ICP relevance	20%	Does your target buyer care about this?
Product fit	10%	Can you credibly connect your product to this trend?

Total score out of 100. Urgency tiers:

• 90-100: Run today — this peaks within 24-48h
• 70-89: Run this week — 3-5 day window
• 50-69: Worth testing — stable trend, less time pressure
• Below 50: Monitor — not actionable yet

Phase 3: Hook Translation

For each trend scoring 50+, generate:

Ad Hook Formula

[Trend reference] + [Your unique angle] + [CTA tied to the moment]

Per Trend, Produce:

1. Trend summary — What's happening in 2 sentences
2. Why it's an ad opportunity — Connection to your product/ICP
3. 3 hook variants:
   • Newsjack hook — Reference the trend directly ("Everyone's talking about X. Here's what they're missing...")
   • Contrarian hook — Take the opposite stance ("Hot take: [trend] doesn't matter. Here's what does...")
   • Practical hook — Offer a solution related to the trend ("[Trend] means you need [your feature] now")
4. Recommended format — Static / video / carousel / search ad
5. Recommended platform — Where the trend is hottest
6. Time window — How long before this trend fades

Phase 4: Output Format

# Trending Ad Hooks — [DATE]

Industry: [category]
Platforms scanned: [list]
Trends identified: [N]
Actionable hooks (score 50+): [N]

---

## Run Today (Score 90+)

### Trend: [Trend Title]
**What's happening:** [2-sentence summary]
**Engagement signal:** [X likes/comments across Y platforms in Z hours]
**Time window:** [Estimated hours/days before this fades]

**Hook 1 (Newsjack):** "[Ad headline]"
> [1-2 sentence body copy]
- Format: [Static/Video/Carousel]
- Platform: [Twitter/Meta/Google/LinkedIn]

**Hook 2 (Contrarian):** "[Ad headline]"
> [Body copy]

**Hook 3 (Practical):** "[Ad headline]"
> [Body copy]

---

## Run This Week (Score 70-89)

[Same format]

---

## Worth Testing (Score 50-69)

[Same format, briefer]

---

## Trend Velocity Dashboard

| Trend | Twitter | Reddit | LinkedIn | HN | Score | Window |
|-------|---------|--------|----------|----|----|--------|
| [Trend 1] | High | Medium | Low | — | 92 | 24h |
| [Trend 2] | Medium | — | High | Low | 78 | 5d |
| [Trend 3] | Low | Medium | — | Medium | 61 | 2w |

---

## Competitor Trend Involvement

| Trend | Competitor Riding It? | Their Angle | Your Counter-Angle |
|-------|----------------------|-------------|-------------------|
| [Trend] | [Y/N — who] | [Their take] | [Your differentiated take] |

Save to trending-hooks-[YYYY-MM-DD].md in the current working directory (or user-specified path).

Cost

Component	Cost
Twitter/X (web_search)	Free
Reddit scraper (Apify)	~$0.05-0.10
LinkedIn (web_search)	Free
Hacker News (Algolia API)	Free
Analysis & hook generation	Free (LLM reasoning)
Total	~$0.05-0.10 (or free if skipping Reddit Apify scraper)

Tools Required

• Environment variable: APIFY_API_TOKEN — for Reddit scraping via Apify (optional — skill works without it using web_search fallback for Reddit)
• Web search — built into your AI agent (for Twitter/X, LinkedIn)
• Hacker News Algolia API — free, no key needed (https://hn.algolia.com/api/v1/)
• No third-party libraries needed. All data collection uses HTTP APIs (requests or equivalent) and web_search.

Trigger Phrases

• "What's trending we could run ads about?"
• "Find viral hooks for our campaigns"
• "What's hot in [space] this week?"
• "Newsjacking opportunities for [client]"
• "Run the trending hook spotter"