Agent skill · marketing growth · bradautomates
x-research
Research high-performing X/Twitter content from tracked accounts using Apify's Tweet Scraper V2. Identifies outlier tweets, trending topics, and content patterns to inform content strategy. Use when asked to: - Find trending tweets or content in a niche - Research what's performing on X/Twitter - Identify high-performing tweet patterns - Analyze competitors' X content - Generate content ideas from X trends - Run X/Twitter research Triggers: "x research", "twitter research", "find trending tweets", "analyze x accounts", "what's working on twitter", "content research x", "tweet analysis
Why this skill is useful
Adds specific scripts for fetching and analyzing tweets that the AI wouldn't reliably generate on its own.
What it needs
Requires apify-client, google-genai installed locally. Requires apify account access. About 3k tokens when loaded. Last updated 2026-01-23. 197 stars on the source repository.
What this skill does
X/Twitter Research Research high-performing tweets from tracked accounts, identify outliers, and optionally analyze video content for hooks and structure. Prerequisites APIFYTOKEN environment variable or in .env GEMINIAPIKEY environment variable or in .env (for video analysis) apify-client and google-genai Python packages Accounts configured in .claude/context/x-accounts.md Verify setup: Workflow 1. Create Run Folder 2. Fetch Tweets Parameters: --days: Days back to search (default: 30) --max-items: Max tweets per account (default: 100) --handles: Override accounts file with specific handles API Limits: Minimum 50 tweets per query required. Wait a couple minutes between runs. 3. Identify Outliers Output JSON contains: totalposts: Number of tweets analyzed outliercount: Number of outliers found topics: Top hashtags, mentions, and keywords contentpatterns: Analysis of what formats perform well accounts: List of accounts analyzed outliers: Array of outlier tweets with engagement metrics 4. Analyze Videos with AI (Optional) If outliers contain video content: Note: X/Twitter is primarily text-based. Video analysis is optional and only useful when outliers contain video posts. 5. Generate Report Read {RUNFOLDER}/outliers.json (and optionally {RUNFOLDER}/video-analysis.json), then generate {RUNFOLDER}/report.md. Report Structure: Focus on actionable insights. Content patterns and trending topics are key for X/Twitter research. Quick Reference Full pipeline: With video analysis (optional): Then read JSON files and generate the report. Engagement Metrics Engagement Score (weighted): Bookmarks: 4x (highest signal - saved for reference) Replies: 3x (active conversation) Retweets: 2x (amplification) Quotes: 2x (engagement with commentary) Likes: 1x (passive approval) Outlier Detection: Tweets with engagement rate > mean + (threshold x stddev) Engagement Rate: (score / followers) x 100 Output Location All output goes to timestamped run folders:
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills bradautomates/x-research