Vibeleaderboard
← Back to Tools
Index / tool

SEO Machine

github.com/thecraighewitt/seomachine
Visit github.com
Category
AI Tools
Rank
No. 420Tools index
Pricing
Open Source
Type
TOOL
Use case
Business & Commerce
Interfaces
Agent Skill / Plugin
Builder
@TheCraigHewitt
GitHub
7.5k stars
Date

About

A specialized Claude Code workspace for creating long-form, SEO-optimized blog content. Provides custom commands, specialized agents, and data integrations to help research, write, analyze, and optimize content that ranks well.

What it does

SEO Machine turns a configured brand context into a repeatable content pipeline. Its workspace commands research topics, draft articles, refresh existing posts, audit optimization, and prepare WordPress publishing. Python analysis scripts add live search, traffic, competitor, readability, intent, and topic-cluster signals when the required services are connected.

Why it's ranked here

The strongest case is workflow breadth backed by concrete analysis code. It joins writing guidance with Search Console, Analytics, and DataForSEO signals, then turns findings into prioritized reports. The trade is operational weight: useful results depend on careful context setup, external credentials, paid or restricted data access, and human judgment about generated recommendations.

What's good

Brand voice, examples, keyword targets, style rules, and internal links are first-class inputs rather than afterthoughts. Research covers SERP patterns, quick wins, trends, competitors, buyer-intent terms, and topic clusters. Drafting automatically invokes focused checks for metadata, links, keyword placement, readability, and human-sounding prose. WordPress publishing closes the loop.

Tradeoffs

This is a Claude Code workspace, not a standalone editor or hosted service. Setup requires an Anthropic account, Python dependencies, company context, and credentials for useful data integrations. Several analysis scripts stop or degrade when Search Console or DataForSEO is unavailable. Heuristic thresholds and estimated traffic assumptions still require editorial and SEO scrutiny.

How to use it well

It best suits a content or SEO team already working in Claude Code and willing to maintain rich brand context. Use it to connect research, drafting, refresh decisions, optimization, and WordPress handoff. Keep a human editor responsible for claims, voice, strategic choices, and final publication. It does not replace analytics accounts, keyword data providers, or a general content management system.

Technical notes+

README.md defines the Claude Code command surface, context templates, Docker workflow, mounted content directories, integrations, and WordPress publishing path. research_quick_wins.py combines Google Search Console, DataForSEO, Google Analytics, OpportunityScorer, and SearchIntentAnalyzer, with optional fallbacks for unavailable services. research_trending.py weights growth, demand, and current visibility before writing Markdown reports. research_serp_analysis.py inspects result types, word counts, freshness, intent, and competitive difficulty. research_topic_clusters.py uses TF-IDF and KMeans when scikit-learn is present, otherwise falling back to term matching. seo_baseline_analysis.py, seo_bofu_rankings.py, and seo_competitor_analysis.py provide buyer-intent and competitor reporting. test_dataforseo.py is a credential-dependent integration smoke test rather than an isolated unit test.

Observed

Primary language
Python is the implementation language shown across the supplied analysis and integration scripts.
Installation surface
The README documents repository cloning, pip dependency installation, Claude Code startup, and an optional Docker Compose workflow.
Primary interface
Claude Code workspace commands cover research, writing, rewriting, auditing, prioritization, landing pages, and publishing.
External integrations
Google Analytics 4, Google Search Console, DataForSEO, and WordPress REST publishing are documented.
Configuration model
Users supply brand voice, writing examples, features, internal links, style guidance, target keywords, competitor analysis, and SEO rules.
Testing structure
The supplied repository text includes a credential-dependent DataForSEO smoke-test script.

Read from README.md, test_dataforseo.py, research_trending.py, seo_bofu_rankings.py, research_quick_wins.py, seo_baseline_analysis.py, research_serp_analysis.py, research_topic_clusters.py, seo_competitor_analysis.py.

What it can do

  • Research SEO keywords and topics

    Target keywords or topic ideas → SEO keyword data and content recommendations

  • Generate long-form SEO-optimized blog posts

    Topic, keywords, and content requirements → Complete blog article optimized for search engines

  • Analyze website performance data

    Google Analytics and Search Console data → SEO performance insights and recommendations

  • Optimize existing content for search rankings

    Existing blog content and target keywords → Revised content with SEO improvements

  • Retrieve competitive SEO data

    Domain names or competitor URLs → Competitive analysis and ranking data via DataForSEO

  • Execute specialized SEO commands

    Custom command prompts and parameters → Targeted SEO analysis or content generation results

Tags

seocontentwritingblogoptimizationresearchmarketingclaude

Tech Stack

Docker

Comments (0)

No comments yet

Editorially curated, with community endorsements as a secondary signal. Corrections welcome.