analyzing-competitor-tiktok-content-strategy
Analyzes a competitor's TikTok content strategy and top-performing videos using apidojo's scrapers on Apify. Triggers when the user asks to: see what content a competitor posts on TikTok, analyze which TikTok videos perform best for a brand, reverse-engineer a competitor's TikTok strategy, find out how often a competitor posts and what gets the most engagement, study a brand's TikTok presence, compare TikTok content strategies across competing brands, or find out what topics a competitor covers on TikTok. Returns video topics, post frequency, avg views, top hooks, hashtags used, and engagement patterns. Ideal for social media strategists, content teams, and competitive intelligence analysts.
- Installs
- 783
- Last 24h
- +10
- Trending rank
- #8824
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-competitor-tiktok-content-strategy- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads analyzing-competitor-tiktok-content-strategy automatically when relevant.
- 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.
Analyzing Competitor TikTok Content Strategy
Scrapes a competitor's TikTok profile to pull their recent videos, engagement data, posting patterns, and hashtag usage. Reverse-engineers what's working for them so you can learn from it or differentiate against it.
Prerequisites
APIFY_TOKENenvironment variable set- Optional: Apify MCP server installed
Inputs
| Parameter | Type | Required | Default | Notes |
|---|---|---|---|---|
startUrls | array | Optional | [] | TikTok URLs — user profiles, hashtags, music pages, search, locations |
keywords | array | Optional | [] | Search keywords/terms to find posts |
sortType | string | Optional | RELEVANCE | Sort order for keyword results: RELEVANCE, MOST_LIKED, DATE_POSTED |
location | string | Optional | — | ISO 3166-1 alpha-2 country code for regional filtering (e.g. US, GB) |
maxItems | number | Optional | Unlimited | Maximum posts to return across the run |
includeSearchKeywords | boolean | Optional | false | Add the matched search keyword field to each post |
customMapFunction | string | Optional | — | JavaScript function to transform each output object |
Workflow
Progress:
- [ ] Step 1: Get competitor TikTok handle(s)
- [ ] Step 2: Pull profile stats with tiktok-profile-scraper
- [ ] Step 3: Pull recent videos with tiktok-scraper
- [ ] Step 4: Identify top-performing content patterns
- [ ] Step 5: Deliver content strategy analysis
Step 1: Clarify Parameters
Ask the user for:
- Competitor TikTok handle(s) — up to 5 accounts (without @)
- Number of recent videos to analyze (default: 50 — last 2-3 months of content)
- Analysis focus — top content, posting cadence, hashtag strategy, or all three
Step 2: Pull Profile Stats
Recommended — run_actor.js (handles waiting, output, and file saving automatically):
# Quick answer (prints table to chat)
node scripts/run_actor.js \
--actor "apidojo~tiktok-scraper" \
--input '{"param": "value"}'
# Save as CSV
node scripts/run_actor.js \
--actor "apidojo~tiktok-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.csv --format csv
# Save as JSON
node scripts/run_actor.js \
--actor "apidojo~tiktok-scraper" \
--input '{"param": "value"}' \
--output YYYY-MM-DD_results.json --format json
APIFY_TOKENmust be set in environment or.envfile.
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tiktok-profile-scraper"
Input:
{
"usernames": ["[handle1]", "[handle2]"]
}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tiktok-profile-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{"usernames": ["[handle1]", "[handle2]"]}'
Extract: fans, heart (total likes), video count, following, bio text.
Step 3: Pull Recent Videos
If Apify MCP is available:
Tool: apify:run-actor
Actor: "apidojo~tiktok-scraper"
Input:
{
"profiles": ["https://www.tiktok.com/@[handle1]", "https://www.tiktok.com/@[handle2]"]
}
REST API fallback:
curl -X POST \
"https://api.apify.com/v2/acts/apidojo~tiktok-scraper/runs?token=$APIFY_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"profiles": ["https://www.tiktok.com/@[handle1]"]
}'
Step 4: Analyze Content Patterns
From the video dataset, extract:
Top 10 videos by play count:
- Title/caption, views, likes, comments, shares, date, hashtags
Posting frequency:
dates = [video.createTimeISO for each video]
days_covered = max(dates) - min(dates)
posting_rate = total_videos / days_covered (posts per day)
Hashtag analysis: Extract all hashtags across videos. Count frequency. Top 10 = their core hashtag strategy.
Content format patterns: From captions, classify videos into buckets:
- Educational/Tutorial (contains: "how to", "tips", "learn", numbers like "5 ways")
- Entertainment/Humor (reactions, trends, dances)
- Promotional (product mentions, CTAs, "link in bio")
- Behind-the-scenes (BTS, day-in-life, founder story)
- UGC/Response (reply to comment format)
Hook patterns in top videos: Look at the first line of captions in the top 10 videos. What do they have in common?
Step 5: Format Analysis
Output Format
# TikTok Content Strategy Analysis: @[COMPETITOR]
Videos analyzed: [N] | Period: [start]–[end] | Date: [DATE]
## Account Overview
Followers: [N] | Total likes: [N] | Videos posted: [N] | Avg likes per video: [N]
Overall engagement rate: [X.X%]
## Posting Pattern
- Frequency: [X] videos per week
- Best-performing days: [day], [day] (based on publish date of top videos)
- Average video length: [X] seconds (if available)
## Top 5 Videos (by Views)
| # | Caption Excerpt | Views | Likes | Comments | Hashtags | Date |
|---|----------------|-------|-------|----------|----------|------|
| 1 | "[caption]" | [N] | [N] | [N] | [tags] | [date] |
## Content Mix
| Format | % of Videos | Avg Views |
|--------|-------------|-----------|
| Educational | [X%] | [N] |
| Entertainment | [X%] | [N] |
| Promotional | [X%] | [N] |
| BTS/Story | [X%] | [N] |
## Top Hashtags Used
1. #[tag] — used in [N] of [total] videos
2. #[tag] — [N] videos
3. #[tag] — [N] videos
## Hook Patterns in Top Content
- Top performing hooks start with: [pattern — e.g., questions, numbers, bold claims]
- Example: "[first line of top video]"
## What's Working for Them
1. [Insight 1 — specific, actionable]
2. [Insight 2]
3. [Insight 3]
## Gap / Differentiation Opportunity
[What they're NOT doing that could be a strategic opening]
Troubleshooting
Profile scraper returns no videos: The account may have few posts or have gone inactive. Check the handle. Top videos all very old: Account may have slowed down. Note this in the analysis. Hashtags missing from results: Some captions don't use hashtags. Analyze caption text for topic signals instead.
FAQ
What does the analyzing-competitor-tiktok-content-strategy skill do?
Analyzes a competitor's TikTok content strategy and top-performing videos using apidojo's scrapers on Apify. Triggers when the user asks to: see what content a competitor posts on TikTok, analyze which TikTok videos perform best for a brand, reverse-engineer a competitor's TikTok strategy, find out how often a competitor posts and what gets the most engagement, study a brand's TikTok presence, compare TikTok content strategies across competing brands, or find out what topics a competitor covers on TikTok. Returns video topics, post frequency, avg views, top hooks, hashtags used, and engagement patterns. Ideal for social media strategists, content teams, and competitive intelligence analysts.
How do I install analyzing-competitor-tiktok-content-strategy?
Run npx skills add https://github.com/apidojo-io/apidojo-skills --skill analyzing-competitor-tiktok-content-strategy 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/analyzing-competitor-tiktok-content-strategy/ (personal) or .claude/skills/analyzing-competitor-tiktok-content-strategy/ (project).
Which AI agents can use analyzing-competitor-tiktok-content-strategy?
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 analyzing-competitor-tiktok-content-strategy 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 analyzing-competitor-tiktok-content-strategy?
It is published by apidojo-io in apidojo-io/apidojo-skills. It has 783 recorded installs.
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