Agent skill · research science · bradautomates
instagram-research
Research high-performing Instagram content (posts and reels) from tracked accounts using Apify's Instagram Scraper. Identifies outlier content, analyzes top 5 videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending Instagram content in a niche - Research what's performing on Instagram - Identify high-performing reel patterns - Analyze competitors' Instagram content - Generate content ideas from Instagram trends - Run Instagram research - Find viral reels - Analyze hooks and content structure Triggers: "instagram research", "ig research", "find trending reels", "analyze instagram accounts", "what's working on instagram", "content research instagram", "reel analysis", "instagram trends
Why this skill is useful
Adds executable scripts for fetching and analyzing Instagram content that aren't available in public documentation.
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
Instagram Research Research high-performing Instagram posts and reels, identify outliers, and analyze top video content for hooks and structure. Prerequisites APIFYTOKEN environment variable or in .env GEMINIAPIKEY environment variable or in .env apify-client and google-genai Python packages Accounts configured in .claude/context/instagram-accounts.md Verify setup: Workflow 1. Create Run Folder 2. Fetch Content Parameters: --type: "posts", "reels", or "stories" --days: Days back to search (default: 30) --limit: Max items per account (default: 50) 3. Identify Outliers Output JSON contains: totalposts: Number of posts analyzed outliercount: Number of outliers found topics: Top hashtags and keywords accounts: List of accounts analyzed outliers: Array of outlier posts with engagement metrics 4. Analyze Top Videos with AI Extracts from each video: Hook technique and replicable formula Content structure and sections Retention techniques CTA strategy See the video-content-analyzer skill for full output schema and hook/format types. 5. Generate Report Read {RUNFOLDER}/outliers.json and {RUNFOLDER}/video-analysis.json, then generate {RUNFOLDER}/report.md. Report Structure: Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent. Quick Reference Full pipeline: Then read both JSON files and generate the report. Engagement Metrics Engagement Score: likes + (3 × comments) + (0.1 × views) Outlier Detection: Posts with engagement rate > mean + (threshold × stddev) Engagement Rate: (score / followers) × 100
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills bradautomates/instagram-research