Research high-performing Instagram posts and reels, identify outliers, and analyze top video content for hooks and structure.
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AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioninstagram-researchExecute the skills CLI command in your project's root directory to begin installation:
Fetches instagram-research from bradautomates/head-of-content and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate instagram-research. Access via /instagram-research in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Research high-performing Instagram posts and reels, identify outliers, and analyze top video content for hooks and structure.
APIFY_TOKEN environment variable or in .envGEMINI_API_KEY environment variable or in .envapify-client and google-genai Python packages.claude/context/instagram-accounts.mdVerify 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"
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
Parameters:
--type: "posts", "reels", or "stories"--days: Days back to search (default: 30)--limit: Max items per account (default: 50)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 analyzedoutlier_count: Number of outliers foundtopics: Top hashtags and keywordsaccounts: List of accounts analyzedoutliers: Array of outlier posts with engagement metricspython3 .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:
See the video-content-analyzer skill for full output schema and hook/format types.
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]({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.
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 Score: likes + (3 × comments) + (0.1 × views)
Outlier Detection: Posts with engagement rate > mean + (threshold × std_dev)
Engagement Rate: (score / followers) × 100
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
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cursor/plugins
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ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
instagram-research reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend instagram-research for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in instagram-research — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
instagram-research fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
instagram-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for instagram-research matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: instagram-research is focused, and the summary matches what you get after install.
instagram-research has been reliable in day-to-day use. Documentation quality is above average for community skills.
We added instagram-research from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: instagram-research is focused, and the summary matches what you get after install.
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