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seo-content-marketing-commands

Automate SEO and content marketing workflows with Python libraries.

8|Updated May 16, 2026
Search Tags:#keyword research#content marketing#seo#technical seo#content audit
One-click install
npx skills add https://github.com/Aradotso/marketing-skills --skill seo-content-marketing-commands
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: seo-content-marketing-commands
Source: https://github.com/Aradotso/marketing-skills/tree/main/skills/seo-content-marketing-commands
Command: npx skills add https://github.com/Aradotso/marketing-skills --skill seo-content-marketing-commands

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python, pandas, numpy, scikit-learn, spacy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automates SEO and content marketing workflows, addressing challenges like keyword research, content audits, technical SEO, competitor analysis, and workflow orchestration.

Core Features & Use Cases

  • Keyword Research: Perform deep keyword clustering, opportunity scoring, and SERP intent mapping.
  • Content Audits: Conduct full-site content quality assessments with duplication and cannibalization detection.
  • Technical SEO: Diagnose crawlability, performance, and indexability issues.
  • Competitor Analysis: Identify backlink gaps, topic gaps, and featured snippet opportunities.
  • Content Workflows: Orchestrate pipelines from keyword research to published content.
  • Use Case: Use this Skill to optimize your website's SEO and content strategy, saving time and improving efficiency.

Quick Start

Run the full SEO sprint for your domain by executing the command: /workflows:full-seo-sprint --domain example.com --scope full.

Frequently Asked Questions about seo-content-marketing-commands

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

FAQPage Schema
How do I automate keyword research and content audits for my website?▼

Automate keyword research and content audits by running specialized SEO workflows that use Python and pandas to perform keyword clustering, opportunity scoring, and detect content duplication or cannibalization across your domain.

Do I need Python and specific libraries to run technical SEO and content workflows?▼

Yes, you need Python installed along with libraries like pandas, numpy, scikit-learn, and spacy to analyze data, diagnose crawlability and indexability issues, and orchestrate technical SEO workflows.

What's the best way to run a full SEO analysis for my domain?▼

Run a full SEO analysis by executing the full SEO sprint command with your domain and scope parameters to diagnose technical performance, identify backlink gaps, and map SERP intent automatically.

How does competitor analysis work for identifying backlink and topic gaps?▼

Competitor analysis works by evaluating SERP data to identify backlink gaps, topic gaps, and featured snippet opportunities, allowing you to optimize your content strategy against competing domains.

Can I use scikit-learn and spacy for SERP intent mapping and content quality assessments?▼

Yes, scikit-learn and spacy are utilized to process natural language and apply machine learning for deep keyword clustering, SERP intent mapping, and full-site content quality assessments.

What are the limitations of automating SEO workflows with Python libraries?▼

Limitations include requiring a configured Python environment with dependencies like pandas and scikit-learn, and the need for accurate SERP and crawl data inputs to effectively diagnose technical SEO and indexability issues.

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