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competitor-profiling

Generates structured competitor profile markdown files from URLs using site scraping and SEO data.

Updated Mar 6, 2026
Search Tags:#firecrawl#positioning#competitive intelligence#competitor analysis#dataforseo#pricing analysis#seo research
One-click install
npx skills add https://github.com/Transform24/THE-QUIET-AUTHORITY --skill competitor-profiling-transform24
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: competitor-profiling
Source: https://github.com/Transform24/THE-QUIET-AUTHORITY/tree/main/content-ops/_factory/skills/competitor-profiling
Command: npx skills add https://github.com/Transform24/THE-QUIET-AUTHORITY --skill competitor-profiling-transform24

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Researching competitors manually means visiting dozens of pages, copying pricing tables, and pulling SEO metrics by hand. This Skill automates that research, turning a list of competitor URLs into consistent, comparable profile documents backed by scraped content and market data. ## Core Features & Use Cases - Three-phase research pipeline: Scrapes key competitor pages with Firecrawl (homepage, pricing, features, customers, integrations, changelog), pulls SEO and backlink intelligence via DataForSEO, then synthesizes everything into a standardized profile template. - Raw data persistence: Saves every scrape, SEO response, and review source to dated folders under competitor-profiles/raw/ so data can be audited, reused, or diffed across snapshots without re-running API calls. - Quick scan vs. deep profile modes: Defaults to a fast homepage-plus-pricing scan for large competitor lists, with full deep profiles (reviews, backlinks, content strategy) for focused research on three or fewer competitors. - Use Case: Give it five competitor URLs before a positioning workshop and receive five comparable profile files plus a _summary.md with a side-by-side comparison table, positioning map, and identified market gaps. ## Quick Start Profile these competitors: https://competitor1.com and https://competitor2.com, using a quick scan focused on pricing and positioning.

Frequently Asked Questions about competitor-profiling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I profile competitors from a list of URLs?▼

Provide the competitor URLs and the skill runs a three-phase process: Firecrawl scraping of key pages, DataForSEO metrics collection, and synthesis into standardized markdown profiles. It finishes with a _summary.md comparing all competitors side by side.

What pages should I scrape for competitor research?▼

Prioritize the homepage, pricing page, features pages, about page, customers page, integrations page, and changelog. Optionally scrape review sites like G2, Capterra, Product Hunt, and TrustRadius for ratings and common praise or complaint themes.

What is the difference between a quick scan and a deep profile?▼

A quick scan covers only the homepage, pricing page, domain rank overview, and ranked keywords summary. A deep profile adds all site pages, review mining, full backlink analysis, technology stack detection, and content strategy analysis. Quick scan is the default for large lists.

Can I skip the SEO data collection in a competitor profile?▼

Yes, you can request only specific dimensions like pricing, positioning, and customer logos. The skill skips the DataForSEO phase and flags the SEO section as not collected rather than leaving placeholders, while still saving raw scrapes to disk.

Should I update an old competitor profile or start fresh?▼

Update the existing profile rather than starting fresh. Re-pull the pricing page, SEO metrics, and changelog, update the generated date, and append a Change Log section. Save new raw data to a new date folder so snapshots can be diffed over time.

How many competitors can be profiled at once?▼

There is no hard limit, but for lists over ten the skill recommends prioritizing the top five by domain overlap or market similarity. Scraping is parallelized across competitors and consistent metrics are pulled so profiles remain comparable.

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