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SKILL.md
npx skills add apidojo-io/apidojo-skills --skill finding-trending-twitter-topics-for-content
Finds trending Twitter topics and conversations for content ideation using apidojo's Twitter scrapers on Apify. Triggers when the user asks to: find trending topics on Twitter for content, discover what is being discussed in a niche on X right now, identify Twitter conversations to join with content, find trending hashtags in an industry on Twitter, research what topics are generating engagement in a space on X, discover viral tweet themes for blog or video content, or find what your target audience is talking about on Twitter this week. Returns trending topics, tweet volume signals, top engagement posts, and content angle suggestions. Ideal for content marketers, social media managers, newsletter writers, and real-time content teams.
npx skills add apidojo-io/apidojo-skills --skill finding-trending-twitter-topics-for-content
Identifies trending conversations in a niche on Twitter to inform timely content. Twitter trends are 48–72 hour windows — act fast or pivot to the evergreen angle.
APIFY_TOKEN environment variable set| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
searchTerms | array | ✅ | [] | Twitter advanced search queries (e.g. ["#AI lang:en", "from:NASA"]) |
sort | string | Optional | Top | Sort order: Latest, Top, or Latest+Top |
tweetLanguage | string | Optional | — | ISO 639-1 language code (e.g. en) |
maxItems | number | Optional | Unlimited | Maximum tweets to return |
onlyVerifiedUsers | boolean | Optional | false | Only tweets from verified users |
onlyTwitterBlue | boolean | Optional | false | Only Twitter Blue subscribers |
onlyImage | boolean | Optional | false | Only tweets with images |
onlyVideo | boolean | Optional | false | Only tweets with videos |
onlyQuote | boolean | Optional | false | Only quote tweets |
author | string | Optional | — | Filter to a specific author handle |
inReplyTo | string | Optional | — | Tweets replying to a specific handle |
mentioning | string | Optional | — | Tweets mentioning a specific handle |
geotaggedNear | string | Optional | — | Tweets near a location |
withinRadius | string | Optional | — | Radius around geotaggedNear |
geocode | string | Optional | — | Lat/lng + radius string |
placeObjectId | string | Optional | — | Tweets tagged with a place |
minimumRetweets | number | Optional | — | Minimum retweet count |
minimumFavorites | number | Optional | — | Minimum like count |
minimumReplies | number | Optional | — | Minimum reply count |
start | string | Optional | — | Tweets after this date (YYYY-MM-DD) |
end | string | Optional | — | Tweets before this date (YYYY-MM-DD) |
includeSearchTerms | boolean | Optional | false | Add the matched search term to each tweet |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
APIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
REST API fallback:
Group tweets by topic cluster using keyword co-occurrence. For each cluster:
Topic freshness:
Content angle recommendation by freshness:
NEWS_REACTIVE — good for social media posts but risky for long-form content investmentRISK_TOPIC; joining controversy can be brand-damaging; present option to "inform from a distance"No trending topics (flat distribution): Niche may not be particularly active on Twitter; try extending to 14-day window or switching to Reddit for content research in this niche. All topics are political/news: Add niche qualifier more aggressively in search terms; most general news topics will surface on any broad search. Content idea doesn't fit your format: Trending topics are inputs, not prescriptions — adapt the angle to your format (e.g. a Twitter controversy about pricing → a blog post "How to Communicate Pricing Changes").
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["[NICHE]", "#[niche]", "[NICHE] [current_year]"],
"maxItems": 500,
"tweetLanguage": "en",
"since": "[7 days ago]"
}curl -X POST "https://api.apify.com/v2/acts/apidojo~tweet-scraper/runs?token=$APIFY_TOKEN" -H "Content-Type: application/json" -d '{
"searchTerms": ["B2B SaaS", "#saas", "B2B SaaS 2026"],
"maxItems": 500,
"tweetLanguage": "en"
}'topic_velocity = count_of_tweets_in_cluster
topic_engagement = sum(likeCount + replyCount * 3 + retweetCount * 2) / topic_velocityfreshness = proportion of cluster tweets from last 48 hoursopportunity_score = (topic_velocity / 50, max 1) * 0.30
+ (topic_engagement / 100, max 1) * 0.30
+ freshness * 0.20
+ (top_tweet_by_influencer ? 1 : 0) * 0.20# Trending Twitter Topics: [NICHE]
Period: [DATE_RANGE] | Tweets analyzed: [N] | Topic clusters identified: [N] | Date: [DATE]
## Top Trending Topics
| # | Topic | Tweets | Avg Engagement | Freshness | Type | Score |
|---|-------|--------|---------------|---------|------|-------|
| 1 | [topic] | [N] | [N] | [X%] | [TRENDING/NEWS/EVERGREEN] | [0.XX] |
## Content Opportunities
### 1. [Topic Name] (Score: [X])
Volume: [N] tweets | Avg engagement: [N] | Freshness: [X%]
Angle: [recommended content format and angle]
Top tweet: @[handle] ([N] likes): "[excerpt]"
### 2. [Topic Name] ...
## Hashtag Map
| Hashtag | Usage Count | Avg Likes | Co-used With |
|---------|------------|-----------|-------------|