instagram-research | SkillBundle

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SKILL.md

instagram-research | SkillBundle

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.

npx skills add bradautomates/head-of-content --skill instagram-research

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SKILL.mdView source

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

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:

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

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:

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 × std_dev)

Engagement Rate: (score / followers) × 100

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

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

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

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

# 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]({url})
 
[Repeat for each analyzed video]
 
## Content Structure Patterns
 
| Video | Format | Pacing | Key Retention Techniques |
|-------|--------|--------|--------------------------|
| @username | {format} | {pacing} | {techniques} |
 
## CTA Strategies
 
| Video | CTA Type | CTA Text | Placement |
|-------|----------|----------|-----------|
| @username | {type} | "{cta_text}" | {placement} |
 
## All Outliers
 
| Rank | Username | Likes | Comments | Views | Engagement Rate |
|------|----------|-------|----------|-------|-----------------|
[List all outliers with metrics and links]
 
## 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]

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