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.
- Installs
- 809
- Last 24h
- +12
- Trending rank
- #8010
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-trending-twitter-topics-for-content- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads finding-trending-twitter-topics-for-content automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| 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 |
Workflow
Progress:
- [ ] Step 1: Search niche keywords + trending signals
- [ ] Step 2: Extract high-engagement tweet clusters
- [ ] Step 3: Identify topic themes and their velocity
- [ ] Step 4: Score content opportunity per topic
- [ ] Step 5: Deliver trending topic brief
Step 1: Search Tweets
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~tweet-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format json
APIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tweet-scraper"
Input:
{
"searchTerms": ["[NICHE]", "#[niche]", "[NICHE] [current_year]"],
"maxItems": 500,
"tweetLanguage": "en",
"since": "[7 days ago]"
}
REST API fallback:
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"
}'
Step 2: Identify Trending Topics
Group tweets by topic cluster using keyword co-occurrence. For each cluster:
topic_velocity = count_of_tweets_in_cluster
topic_engagement = sum(likeCount + replyCount * 3 + retweetCount * 2) / topic_velocity
Topic freshness:
freshness = proportion of cluster tweets from last 48 hours
Step 3: Score Content Opportunity
opportunity_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
Content angle recommendation by freshness:
- Freshness > 0.7 → "Timely reaction piece / hot take"; publish within 24h
- Freshness 0.3–0.7 → "Analysis / deep dive"; publish within 72h
- Freshness < 0.3 → "Evergreen explainer"; no urgency
Step 4: Edge Cases
- Topic is news event, not evergreen: Flag as
NEWS_REACTIVE— good for social media posts but risky for long-form content investment - Trending topic is negative controversy: Flag as
RISK_TOPIC; joining controversy can be brand-damaging; present option to "inform from a distance" - Niche too broad (returns unrelated topics): Add second qualifier — "B2B SaaS growth" not just "SaaS"
- Trending terms are abbreviations or jargon: Define them in output for non-native audience clarity
Output Format
# 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 |
|---------|------------|-----------|-------------|
Troubleshooting
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").
FAQ
What does the finding-trending-twitter-topics-for-content skill do?
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.
How do I install finding-trending-twitter-topics-for-content?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill finding-trending-twitter-topics-for-content in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finding-trending-twitter-topics-for-content/ (personal) or .claude/skills/finding-trending-twitter-topics-for-content/ (project).
Which AI agents can use finding-trending-twitter-topics-for-content?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is finding-trending-twitter-topics-for-content safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains finding-trending-twitter-topics-for-content?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 809 recorded installs.
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