Detalle del Skill
tiktok-research
Dedicated TikTok trend, competitor, and high-performing-video research workflow.
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SKILL.md
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---
name: tiktok-research
description: |
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"
---
# 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:
```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="tiktok-research/$(date +%Y-%m-%d_%H%M%S)" && mkdir -p "$RUN_FOLDER" && echo "$RUN_FOLDER"
```
### 2. Fetch Content
```bash
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
```bash
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
```bash
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:**
```markdown
# 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 | ComLeer la fuente completa en GitHub (abre una página externa)