A comprehensive TikTok influencer research tool for identifying creators, auditing performance, and tracking historical growth trends.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install keyapi-tiktok-influencer-discovery
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install keyapi-tiktok-influencer-discovery using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is TikTok Influencer Discovery & Analytics?
This skill provides a powerful integration for end-to-end TikTok influencer research within the Openclaw Skills ecosystem. It leverages the KeyAPI MCP service to enable developers and marketers to search for creators by keyword or region, retrieve detailed profile snapshots, and analyze multi-dimensional performance metrics. By providing access to follower growth trajectories, live-stream GMV history, and competitive rankings, it transforms raw data into actionable intelligence for brand collaborations and affiliate campaigns.
The tool is designed to facilitate deep due diligence, allowing users to move beyond surface-level metrics. It handles complex data workflows such as identifier resolution, historical trend analysis, and content strategy benchmarking, making it an essential asset for any data-driven social media strategy.
TikTok Influencer Discovery & Analytics Use Cases
- Identify high-performing TikTok influencers for brand partnerships or affiliate marketing campaigns based on niche and region.
- Audit creator authenticity by reviewing historical follower growth, engagement rates, and live-stream performance data.
- Build competitive leaderboards and benchmark multiple creators against industry standards and category rankings.
- Monitor content strategy by analyzing video performance history, posting cadence, and promoted product portfolios.
- Perform geo-targeting analysis to find the best influencer fit for specific regional marketing campaigns.
How TikTok Influencer Discovery & Analytics Works
- Identify the target influencer or search parameters based on the specific marketing objective.
- Resolve account identifiers by calling the profile detail node to obtain both the mutable @handle and the permanent numeric user ID.
- Execute targeted API calls for specific analytics, such as trends, livestreams, or video performance, utilizing the local cache to optimize speed.
- Automatically process and convert cover image URLs via a batch download service to ensure media assets are correctly formatted.
- Synthesize the collected data into a structured research report covering profile summaries, performance benchmarks, and actionable recommendations.
TikTok Influencer Discovery & Analytics Setup
To integrate this skill with your Openclaw Skills workflow, follow these steps:
# 1. Install necessary dependencies
npm install
# 2. Configure your KeyAPI environment variable
# Obtain a free token from https://keyapi.ai
export KEYAPI_TOKEN=your_api_token_here
# 3. Verify the connection to the MCP server
node scripts/run.js --list-tools
TikTok Influencer Discovery & Analytics Data Schema & Taxonomy
The skill organizes retrieved data within a local .keyapi-cache/ directory to ensure efficiency and persistence across sessions. Data is categorized by entity type and identifier.
| Directory | Contents |
|---|---|
influencers/{unique_id}/ |
Stores specific creator data including detail.json, analytics.json, and trends.json. |
searches/influencers/ |
Caches search results based on MD5 hashes of query parameters. |
rankings/ |
Contains competitive leaderboard data and category-based rankings. |
milestones/ |
Historical highlights and growth achievements for specific accounts. |
TikTok Influencer Discovery & Analytics Advanced Features
- Multi-dimensional performance auditing covering live commerce GMV and historical growth velocity.
- Automatic pagination handling for large analytics datasets via the --all-pages CLI flag.
- Intelligent identifier resolution between mutable TikTok @handles and permanent numeric UIDs.
- Cache-first policy to reduce API consumption and significantly improve response times for repetitive research tasks.
- Batch processing for image assets to ensure compatibility with local storage and display requirements.
SKILL.md
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