An automated market intelligence tool that identifies top competitors using a three-tier fallback strategy including search APIs and AI models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install competitor-finder-adarsh
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 competitor-finder-adarsh 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 Competitor Finder Skill?
The Competitor Finder Skill is a specialized data collector designed to populate marketing audit pipelines with high-accuracy competitive landscape data. By integrating multiple search layers, this skill ensures that developers and marketers can consistently identify 3-5 primary competitors for any brand. As a key component of Openclaw Skills, it provides a structured approach to market analysis, delivering competitor names, domains, and strategic justifications for why they are considered rivals.
This skill is built for resilience, utilizing a tiered architecture that prioritizes real-world search data before falling back to sophisticated SEO databases and minimal AI prompts. It handles the complexities of result filtering, domain normalization, and deduplication, ensuring that the output is clean, actionable, and ready for further analysis by downstream AI agents.
Competitor Finder Skill Use Cases
- Automating the competitor analysis phase of a comprehensive Marketing Audit Pipeline.
- Identifying direct and indirect rivals for DTC brands during market entry research.
- Gathering high-intent competitor domains to fuel keyword research and SEO strategy.
- Validating market positioning by cross-referencing search engine results with AI-driven insights.
- Enhancing automated reporting tools with live, verified competitive data through Openclaw Skills.
How Competitor Finder Skill Works
- The skill receives a brand name and an optional domain to provide context for the search.
- It initiates Method 1 by querying SerpAPI to find competitor names and domains within organic Google Search results.
- If SerpAPI results are insufficient, it triggers Method 2 using the DataForSEO Competitor Domain API to find rivals based on shared keyword rankings.
- As a final resort, it employs Method 3, a minimal OpenAI GPT-4.1-mini call, to generate a list based on model knowledge.
- The system automatically filters out the input brand from the results and normalizes all URLs.
- It deduplicates the list and returns a structured object containing 3-5 verified competitors.
Competitor Finder Skill Setup
To use this collector within Openclaw Skills, you must configure the following environment variables for the respective API providers:
# Required for Primary Search
export SERPAPI_KEY="your_serpapi_key"
# Required for SEO Data Fallback
export DATAFORSEO_LOGIN="your_login"
export DATAFORSEO_PASSWORD="your_password"
# Required for AI Fallback
export OPENAI_API_KEY="your_openai_key"
Ensure your project includes the necessary collector files and import the function into your workflow:
import { collectCompetitors } from '../collectors/competitorCollector';
Competitor Finder Skill Data Schema & Taxonomy
The skill returns a strictly typed JSON object. If all methods fail, it returns an empty array with an error flag to prevent pipeline crashes.
Output Schema
| Property | Type | Description |
|---|---|---|
competitors |
Array<CompetitorEntry> |
A list of 3-5 competitor objects |
error |
string |
Optional error message if data collection fails |
CompetitorEntry Object
name: The common brand name of the competitor.website: The normalized root domain (e.g., example.com).reason: A brief explanation of the competitive relationship (optional).
Competitor Finder Skill Advanced Features
- Three-tier fallback logic prioritizing cost-efficiency and data accuracy.
- Automated domain normalization and deduplication to ensure data integrity.
- Graceful degradation that returns valid typed data even during API outages or niche searches.
- Integrated logging using Winston to track success rates across different Openclaw Skills methods.
- Context-aware filtering that adapts search logic based on whether a domain or social handle is provided.
SKILL.md
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