skills/athina-ai/goose-skills/trending-ad-hook-spotter

trending-ad-hook-spotter

SKILL.md

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?"

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

Run twitter-scraper with multiple queries:

# Industry trending topics
python3 skills/twitter-scraper/scripts/scrape_twitter.py \
  --query "<industry keyword> (viral OR trending OR hot take OR unpopular opinion OR thread)" \
  --max-results 50 \
  --sort top

# Competitor mentions (momentum signals)
python3 skills/twitter-scraper/scripts/scrape_twitter.py \
  --query "<competitor1> OR <competitor2> (raised OR launched OR shut down OR acquired OR outage)" \
  --max-results 30

# Pain/frustration spikes
python3 skills/twitter-scraper/scripts/scrape_twitter.py \
  --query "<category> (broken OR frustrating OR tired of OR switched from)" \
  --max-results 30

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

1B: Reddit Trend Scan

Run reddit-scraper:

python3 skills/reddit-scraper/scripts/scrape_reddit.py \
  --subreddits "<relevant_subreddits>" \
  --sort hot \
  --time week \
  --limit 30

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

Run linkedin-profile-post-scraper for 5-10 KOLs in the space:

python3 skills/linkedin-profile-post-scraper/scripts/scrape_linkedin_posts.py \
  --urls "<kol_profile_urls>" \
  --max-posts 10

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

1D: Hacker News Scan

Run hacker-news-scraper:

python3 skills/hacker-news-scraper/scripts/scrape_hn.py \
  --query "<industry keyword>" \
  --type story \
  --sort points \
  --limit 20

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] | ▲▲▲ | ▲▲ ||| 92 | 24h |
| [Trend 2] | ▲▲ || ▲▲▲ || 78 | 5d |
| [Trend 3] || ▲▲ || ▲▲ | 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 clients/<client-name>/ads/trending-hooks-[YYYY-MM-DD].md.

Scheduling

Run weekly or on-demand when you need fresh hooks:

0 8 * * 1 python3 run_skill.py trending-ad-hook-spotter --client <client-name>

Cost

Component Cost
Twitter scraper (3 queries) ~$0.15-0.30 (Apify)
Reddit scraper ~$0.05-0.10 (Apify)
LinkedIn scraper (5-10 KOLs) ~$0.25-0.50 (Apify)
HN scraper Free
Analysis & hook generation Free (LLM reasoning)
Total ~$0.45-0.90

Tools Required

  • Apify API tokenAPIFY_API_TOKEN env var
  • Upstream skills: twitter-scraper, reddit-scraper, linkedin-profile-post-scraper, hacker-news-scraper

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"
Weekly Installs
12
GitHub Stars
340
First Seen
Mar 14, 2026
Installed on
opencode11
gemini-cli11
antigravity11
github-copilot11
codex11
amp11