Instagram Research

Research high-performing Instagram posts and reels, identify outliers, and analyze top video content for hooks and structure.

Prerequisites

• APIFY_TOKEN environment variable or in .env

• GEMINI_API_KEY environment variable or in .env

• apify-client and google-genai Python packages

• Accounts configured in .claude/context/instagram-accounts.md

Verify setup:

python3 -c " import os try: from dotenv import load_dotenv load_dotenv() except ImportError: pass from apify_client import ApifyClient from google import genai assert os.environ.get('APIFY_TOKEN'), 'APIFY_TOKEN not set' assert os.environ.get('GEMINI_API_KEY'), 'GEMINI_API_KEY not set' " && echo "Prerequisites OK"

Workflow

1. Create Run Folder

RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"

2. Fetch Content

python3 .claude/skills/instagram-research/scripts/fetch_instagram.py
--type reels
--days 30
--limit 50
--output {RUN_FOLDER}/raw.json

Parameters:

• --type : "posts", "reels", or "stories"

• --days : Days back to search (default: 30)

• --limit : Max items per account (default: 50)

3. Identify Outliers

python3 .claude/skills/instagram-research/scripts/analyze_posts.py
--input {RUN_FOLDER}/raw.json
--output {RUN_FOLDER}/outliers.json
--threshold 2.0

Output JSON contains:

• total_posts : Number of posts analyzed

• outlier_count : 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

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py
--input {RUN_FOLDER}/outliers.json
--output {RUN_FOLDER}/video-analysis.json
--platform instagram
--max-videos 5

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 {RUN_FOLDER}/outliers.json and {RUN_FOLDER}/video-analysis.json , then generate {RUN_FOLDER}/report.md .

Report Structure:

Instagram Research Report

Generated: {date}

Top Performing Hooks

Ranked by engagement. Use these formulas for your content.

Hook 1: {technique} - @{username}

• Opening: "{opening_line}"
• Why it works: {attention_grab}
• Replicable Formula: {replicable_formula}
• Engagement: {likes} likes, {comments} comments, {views} views
• Watch Video

[Repeat for each analyzed video]

Content Structure Patterns

CTA Strategies

All Outliers

Trending Topics

Top Hashtags

[From outliers.json topics.hashtags]

Top Keywords

[From outliers.json topics.keywords]

Actionable Takeaways

[Synthesize patterns into 4-6 specific recommendations]

Accounts Analyzed

[List accounts]

Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent.

Quick Reference

Full pipeline:

RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" &&
python3 .claude/skills/instagram-research/scripts/fetch_instagram.py --type reels -o "$RUN_FOLDER/raw.json" &&
python3 .claude/skills/instagram-research/scripts/analyze_posts.py -i "$RUN_FOLDER/raw.json" -o "$RUN_FOLDER/outliers.json" &&
python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py -i "$RUN_FOLDER/outliers.json" -o "$RUN_FOLDER/video-analysis.json" -p instagram

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 × std_dev)

Engagement Rate: (score / followers) × 100