TikTok Research

Research high-performing TikTok videos, 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/tiktok-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

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

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

2. Fetch Content

python3 .claude/skills/tiktok-research/scripts/fetch_tiktok.py \

  --days 30 \

  --limit 50 \

  --sorting latest \

  --output {RUN_FOLDER}/raw.json

Parameters:

• --days: Days back to search (default: 30)

• --limit: Max videos per account (default: 50)

• --sorting: "latest", "popular", or "oldest" (default: latest)

• --usernames: Override accounts file with specific usernames

3. Identify Outliers

python3 .claude/skills/tiktok-research/scripts/analyze_posts.py \

  --input {RUN_FOLDER}/raw.json \

  --output {RUN_FOLDER}/outliers.json \

  --threshold 2.0

Output JSON contains:

• total_videos: Number of videos analyzed

• outlier_count: Number of outliers found

• topics: Top hashtags, sounds, and keywords

• accounts: List of accounts analyzed

• outliers: Array of outlier videos with engagement metrics

4. Analyze Top Videos with AI

python3 .claude/skills/video-content-analyzer/scripts/analyze_videos.py \

  --input {RUN_FOLDER}/outliers.json \

  --output {RUN_FOLDER}/video-analysis.json \

  --platform tiktok \

  --max-videos 5

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:

# TikTok 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**: {diggCount} likes, {commentCount} comments, {playCount} views

- [Watch Video]({webVideoUrl})

[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 | Shares | Views | Engagement Rate |

|------|----------|-------|----------|--------|-------|-----------------|

[List all outliers with metrics and links]

## Trending Topics

### Top Hashtags

[From outliers.json topics.hashtags]

### Top Sounds

[From outliers.json topics.sounds]

### 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:

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

python3 .claude/skills/tiktok-research/scripts/fetch_tiktok.py -o "$RUN_FOLDER/raw.json" && \

python3 .claude/skills/tiktok-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 tiktok

Then read both JSON files and generate the report.

Engagement Metrics

Engagement Score: likes + (3 x comments) + (2 x shares) + (2 x saves) + (0.05 x views)

Outlier Detection: Videos with engagement rate > mean + (threshold x std_dev)

Engagement Rate: (score / followers) x 100

TikTok-Specific Fields

• diggCount: Likes/hearts

• shareCount: Shares

• playCount: Video views

• commentCount: Comments

• collectCount: Saves/bookmarks

• authorFollowers: Creator's follower count

• musicName: Sound used in video

• musicOriginal: Whether sound is original