Mode d’emploi
- Copiez la commande et examinez le paquet avant de l’installer dans votre environnement agent.
- Consultez le document original pour les outils, identifiants et environnements requis.
- Décrivez les entrées et le livrable souhaité ; comparez la sortie aux sources.
npx skills add https://github.com/bradautomates/head-of-content --skill instagram-researchAvant de commencer
- L’installation n’inclut ni abonnement au modèle, ni quota API, ni accès aux fichiers.
- Étoiles et installations reflètent l’activité, pas un test indépendant de qualité ou de sécurité.
Document Skill original
Rendu depuis le dépôt source en conservant titres, exemples, code, tableaux, liens et images.
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
