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trending-ad-hook-spotter趋势广告挂钩发现者

Agent Skill

trending-ad-hook-spotter 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

188

周安装

8

GitHub Stars

630

下载量

66
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:trending-ad-hook-spotter(趋势广告挂钩发现者)
来源仓库:https://github.com/gooseworks-ai/goose-skills
仓库路径:skills/trending-ad-hook-spotter
安装命令:
npx skills add https://github.com/gooseworks-ai/goose-skills --skill trending-ad-hook-spotter
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/gooseworks-ai/goose-skills --skill trending-ad-hook-spotter

简介

trending-ad-hook-spotter 用于查找、检索和筛选相关信息。

  • 适合根据关键词或任务场景快速定位候选结果。
  • 通过 npx skills add 命令安装,需结合来源仓库和 README 核验用法。
  • 安装前建议确认权限范围、维护状态及是否会触发联网或文件读写。
  • 当前介绍未说明是否分析广告文案效果,需进一步核实用途。

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?"

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

  1. 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

FactorWeightDescription
Recency25%How fresh? (< 24h = max, > 7 days = low)
Velocity25%Is engagement accelerating or decelerating?
Cross-platform20%Appearing on multiple platforms?
ICP relevance20%Does your target buyer care about this?
Product fit10%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")

  1. Recommended format — Static / video / carousel / search ad
  2. Recommended platform — Where the trend is hottest
  3. 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

ComponentCost
Twitter/X (web_search)Free
Reddit scraper (Apify)~$0.05-0.10
LinkedIn (web_search)Free
Hacker News (Algolia API)Free
Analysis & hook generationFree (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"

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

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