# instagram-research

Pulls posts and reels from tracked accounts, flags engagement outliers, and breaks down the top 5 videos into hooks and structure.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1500 installs · ★ 285
- Author: bradautomates
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/bradautomates/head-of-content/tree/HEAD/.claude/skills/instagram-research/SKILL.md
- Updated: 2026-01-23
- HTML: https://affpapa.org/en/nevibecode/skills/instagram-research

## What it does

The skill fetches content through Apify's Instagram Scraper, scores engagement as `likes + (3 × comments) + (0.1 × views)`, and marks a post as an outlier when it beats the mean plus the threshold times the standard deviation. Defaults: threshold 2.0, 30 days back, 50 items per account. The top 5 videos then go to `video-content-analyzer`, which extracts hook technique, structure, retention tricks and CTA strategy.

Work runs through three scripts. `fetch_instagram.py` grabs the raw data, `analyze_posts.py` finds outliers and topics, `analyze_videos.py` handles the video breakdown. Output is a `report.md` with ranked hooks and replicable formulas, tables for formats and CTAs, the full outlier list and a set of recommendations. Everything lands in a timestamped `instagram-research/` folder.

You need `APIFY_TOKEN` and `GEMINI_API_KEY` in your environment or `.env`, plus the `apify-client` and `google-genai` Python packages. Accounts to track go in `.claude/context/instagram-accounts.md` — write that file first, because the fetch step has nothing to pull without it. A single `python3 -c` check tells you what's missing before you burn a run.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add bradautomates/head-of-content --skill instagram-research -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/instagram-research/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/instagram-research/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add bradautomates/head-of-content --skill instagram-research -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/instagram-research/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add bradautomates/head-of-content --skill instagram-research -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/instagram-research/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install bradautomates/head-of-content instagram-research
```

2. **Manually: put the files in place**
- File: `.github/skills/instagram-research/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add bradautomates/head-of-content --skill instagram-research -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/bradautomates/head-of-content --path .claude/skills/instagram-research
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/instagram-research/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add bradautomates/head-of-content --skill instagram-research -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/bradautomates/head-of-content/tree/HEAD/.claude/skills/instagram-research/SKILL.md

## When to use it
- find reels that outperformed for competitors this month
- separate real outliers from normal engagement noise
- break down hooks and structure of top videos
- collect trending hashtags and keywords for a niche
- get ready hook formulas to reuse in your own scripts

## Example prompts
- run instagram research on the accounts in instagram-accounts.md for the last 30 days
- find trending reels in my niche and show me the outliers
- analyze the hooks in the top 5 videos and write the report

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 99ea046173d62e33.

BEGIN THIRD-PARTY FILE 99ea046173d62e33
````text
---
name: instagram-research
description: |
  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.

  Use when asked to:
  - Find trending Instagram content in a niche
  - Research what's performing on Instagram
  - Identify high-performing reel patterns
  - Analyze competitors' Instagram content
  - Generate content ideas from Instagram trends
  - Run Instagram research
  - Find viral reels
  - Analyze hooks and content structure

  Triggers: "instagram research", "ig research", "find trending reels", "analyze instagram accounts",
  "what's working on instagram", "content research instagram", "reel analysis", "instagram trends"
---

# 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:
```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.json
```

Parameters:
- `--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 2.0
```

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
````
END THIRD-PARTY FILE 99ea046173d62e33

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