Agent skill
seomachine
SEO data platform - keyword research, rankings, traffic, competitor analysis, content briefs, and performance reports. Adapts to any SEO research or analysis task.
Install this agent skill to your Project
npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/other/other/seomachine
SKILL.md
SEOMachine
Unified SEO data platform. Use for any SEO-related task - keyword research, performance tracking, competitor analysis, content planning, or reporting.
Credentials
All credentials in vault .env file. Scripts read from environment variables:
DATAFORSEO_LOGIN, DATAFORSEO_PASSWORD
GA4_PROPERTY_ID (451203520)
GOOGLE_SERVICE_ACCOUNT_PATH
GSC_SITE_URL (https://opened.co/)
DO NOT hardcode credentials in scripts.
Tool Map
Scripts (Ready-to-Run)
| Script | Purpose | Command |
|---|---|---|
weekly_seo_report.py |
Full performance report | python3 scripts/weekly_seo_report.py --domain opened.co |
content_brief_generator.py |
Competitor-informed brief | python3 scripts/content_brief_generator.py "keyword" |
competitor_gap_finder.py |
Keywords we're missing | python3 scripts/competitor_gap_finder.py --competitor domain.com |
Modules (Import for Custom Queries)
| Module | Class | Key Methods |
|---|---|---|
dataforseo.py |
DataForSEO |
get_keyword_ideas(), get_serp_data(), get_questions(), analyze_competitor() |
google_analytics.py |
GoogleAnalytics |
get_top_pages(), get_declining_pages(), get_page_trends(), get_traffic_sources() |
google_search_console.py |
GoogleSearchConsole |
get_keyword_positions(), get_quick_wins(), get_low_ctr_pages(), get_page_performance() |
data_aggregator.py |
DataAggregator |
identify_content_opportunities(), generate_performance_report(), get_priority_queue() |
keyword_analyzer.py |
KeywordAnalyzer |
Keyword clustering, difficulty analysis |
search_intent_analyzer.py |
SearchIntentAnalyzer |
Classify search intent |
seo_quality_rater.py |
SEOQualityRater |
Score content for SEO |
content_length_comparator.py |
ContentLengthComparator |
Compare to competitors |
hubspot.py |
HubSpot |
Email/contact data |
meta.py |
Meta |
Facebook/Instagram metrics |
youtube.py |
YouTube |
YouTube analytics |
webflow.py |
Webflow |
CMS publishing |
References
| File | Content |
|---|---|
references/seo-guidelines.md |
SEO best practices |
references/target-keywords.md |
Priority keyword list |
references/internal-links-map.md |
Internal linking structure |
Common Tasks
"What keywords should we target?"
# Generate content brief with keyword cluster
python3 .claude/skills/seomachine/scripts/content_brief_generator.py "homeschool curriculum"
# Find gaps vs competitors
python3 .claude/skills/seomachine/scripts/competitor_gap_finder.py --batch --min-volume 200
"How is our content performing?"
# Full weekly report
python3 .claude/skills/seomachine/scripts/weekly_seo_report.py --domain opened.co --output markdown
"What's ranking/trending?"
# Custom query using modules
import sys
sys.path.insert(0, ".claude/skills/seomachine/modules")
from google_search_console import GoogleSearchConsole
gsc = GoogleSearchConsole()
quick_wins = gsc.get_quick_wins(days=28) # Keywords at position 11-20
trending = gsc.get_trending_queries() # Rising searches
"What content needs refresh?"
from google_analytics import GoogleAnalytics
ga = GoogleAnalytics()
declining = ga.get_declining_pages(comparison_days=30, threshold_percent=-20)
"Combined analysis"
from data_aggregator import DataAggregator
agg = DataAggregator()
opportunities = agg.identify_content_opportunities()
# Returns: quick_wins, declining_content, low_ctr, trending_topics
Script Details
weekly_seo_report.py
Generates comprehensive weekly SEO report.
# Console output
python3 scripts/weekly_seo_report.py --domain opened.co
# Markdown output
python3 scripts/weekly_seo_report.py --domain opened.co --output markdown
# Save to file
python3 scripts/weekly_seo_report.py --domain opened.co --output markdown --save report.md
# Skip history tracking (dry run)
python3 scripts/weekly_seo_report.py --domain opened.co --no-history
Output includes:
- Priority keyword tracking (from PRIORITY_KEYWORDS dict)
- Quick wins (position 11-20)
- Declining content alerts
- Keyword opportunities
- Week-over-week changes
content_brief_generator.py
Generates competitor-informed content brief.
python3 scripts/content_brief_generator.py "keyword phrase"
python3 scripts/content_brief_generator.py "waldorf vs montessori" --scrape-top-n 10
Output includes:
- Primary keyword metrics (volume, CPC, competition)
- Secondary keyword cluster (top 20)
- Top 10 SERP results
- Competitor H2/H3 structure (scraped)
- FAQ questions to answer
- Recommended word count
- Differentiation opportunities
competitor_gap_finder.py
Finds keywords competitors rank for that we don't.
# Single competitor
python3 scripts/competitor_gap_finder.py --competitor cathyduffy.com --min-volume 200
# Batch (default competitor set)
python3 scripts/competitor_gap_finder.py --batch --min-volume 100
# With keyword limit (cost control)
python3 scripts/competitor_gap_finder.py --competitor hslda.org --max-keywords 500
Module API Reference
DataForSEO
from dataforseo import DataForSEO
dfs = DataForSEO()
# Keyword research
ideas = dfs.get_keyword_ideas("homeschool", limit=100)
questions = dfs.get_questions("homeschool curriculum", limit=50)
# SERP analysis
serp = dfs.get_serp_data("best homeschool curriculum")
# Returns: search_volume, cpc, competition, organic_results, features
# Check rankings
rankings = dfs.get_rankings(domain="opened.co", keywords=["homeschool", "virtual school"])
# Competitor analysis
comparison = dfs.analyze_competitor("cathyduffy.com", keywords=["curriculum reviews"])
# Domain metrics
metrics = dfs.get_domain_metrics("opened.co")
GoogleAnalytics
from google_analytics import GoogleAnalytics
ga = GoogleAnalytics()
# Top pages
top = ga.get_top_pages(days=30, limit=20, path_filter="/blog/")
# Traffic trends for specific page
trends = ga.get_page_trends("/blog/waldorf-vs-montessori", days=90)
# Declining content
declining = ga.get_declining_pages(comparison_days=30, threshold_percent=-20)
# Traffic sources
sources = ga.get_traffic_sources(days=30)
GoogleSearchConsole
from google_search_console import GoogleSearchConsole
gsc = GoogleSearchConsole()
# Current rankings
positions = gsc.get_keyword_positions(days=28)
# Quick wins (position 11-20, high impressions)
quick_wins = gsc.get_quick_wins(days=28)
# Low CTR opportunities
low_ctr = gsc.get_low_ctr_pages(days=28)
# Page performance
perf = gsc.get_page_performance("/blog/waldorf-vs-montessori", days=28)
# Trending queries
trending = gsc.get_trending_queries()
DataAggregator
from data_aggregator import DataAggregator
agg = DataAggregator()
# All opportunities in one call
opportunities = agg.identify_content_opportunities(days=30)
# Returns dict with: quick_wins, declining_content, low_ctr, trending_topics
# Full performance report
report = agg.generate_performance_report(days=30)
# Priority task queue
tasks = agg.get_priority_queue(limit=10)
# Comprehensive page analysis
page_data = agg.get_comprehensive_page_performance("/blog/article", days=30)
Notes
- GSC data has ~3 day delay
- DataForSEO costs money per API call - be efficient, cache results
- GA4 property: 451203520 (opened.co)
- Service account: opened-service-account@gen-lang-client-0217199859.iam.gserviceaccount.com
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