How to use
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npx skills add https://github.com/bradautomates/head-of-content --skill instagram-researchBefore you start
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Original skill document
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
Instagram Research
Research high-performing Instagram posts and reels, identify outliers, and analyze top video content for hooks and structure.
Prerequisites
APIFY_TOKENenvironment variable or in.envGEMINI_API_KEYenvironment variable or in.envapify-clientandgoogle-genaiPython packages- Accounts configured in
.claude/context/instagram-accounts.md
Verify setup:
bash
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
bash
RUN_FOLDER="instagram-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"2. Fetch Content
bash
python3 .claude/skills/instagram-research/scripts/fetch_instagram.py \
--type reels \
--days 30 \
--limit 50 \
--output {RUN_FOLDER}/raw.jsonParameters:
--type: "posts", "reels", or "stories"--days: Days back to search (default: 30)--limit: Max items per account (default: 50)
3. Identify Outliers
bash
python3 .claude/skills/instagram-research/scripts/analyze_posts.py \
--input {RUN_FOLDER}/raw.json \
--output {RUN_FOLDER}/outliers.json \
--threshold 2.0Output JSON contains:
total_posts: Number of posts analyzedoutlier_count: Number of outliers foundtopics: Top hashtags and keywordsaccounts: List of accounts analyzedoutliers: Array of outlier posts with engagement metrics
4. Analyze Top Videos with AI
bash
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 5Extracts 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:
markdown
# 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]Focus on actionable insights. The "Top Performing Hooks" section with replicable formulas should be prominent.
Quick Reference
Full pipeline:
bash
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 instagramThen 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
