MCPInstall

tiktok-research

by bradautomatesMarketing & SEO

Research high-performing TikTok videos from tracked accounts using Apify's TikTok Scraper. Identifies outlier content, analyzes top 5 videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending TikTok content in a niche - Research what's performing on TikTok - Identify high-performing video patterns - Analyze competitors' TikTok content - Generate content ideas from TikTok trends - Run TikTok research - Find viral TikToks - Analyze hooks and content structure Triggers: "tiktok research", "tt research", "find trending tiktoks", "analyze tiktok accounts", "what's working on tiktok", "content research tiktok", "tiktok analysis", "tiktok trends"

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1.2K
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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/bradautomates/head-of-content --skill tiktok-research
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads tiktok-research automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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

FAQ

What does the tiktok-research skill do?

Research high-performing TikTok videos from tracked accounts using Apify's TikTok Scraper. Identifies outlier content, analyzes top 5 videos with AI, and generates reports with actionable hook formulas. Use when asked to: - Find trending TikTok content in a niche - Research what's performing on TikTok - Identify high-performing video patterns - Analyze competitors' TikTok content - Generate content ideas from TikTok trends - Run TikTok research - Find viral TikToks - Analyze hooks and content structure Triggers: "tiktok research", "tt research", "find trending tiktoks", "analyze tiktok accounts", "what's working on tiktok", "content research tiktok", "tiktok analysis", "tiktok trends"

How do I install tiktok-research?

Run npx skills add https://github.com/bradautomates/head-of-content --skill tiktok-research in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/tiktok-research/ (personal) or .claude/skills/tiktok-research/ (project).

Which AI agents can use tiktok-research?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is tiktok-research safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains tiktok-research?

It is published by bradautomates in bradautomates/head-of-content. It has 1,216 recorded installs.

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