Fix SEO skill 34 bugs, Korean labels, and transition Ahrefs refs to our-seo-agent (#2)
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@@ -2,7 +2,7 @@
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## Overview
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Content strategy tool for SEO-driven content planning. Performs content inventory via sitemap crawl and Ahrefs top pages, scores content performance, detects content decay, analyzes topic gaps vs competitors, maps topic clusters, and generates content briefs. Supports Korean content patterns (Naver Blog format, review/후기 content).
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Content strategy tool for SEO-driven content planning. Performs content inventory via sitemap crawl and our-seo-agent CLI, scores content performance, detects content decay, analyzes topic gaps vs competitors, maps topic clusters, and generates content briefs. Supports Korean content patterns (Naver Blog format, review/후기 content).
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## Quick Start
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@@ -42,7 +42,7 @@ python scripts/content_auditor.py --url https://example.com --type blog --json
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```
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**Capabilities**:
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- Content inventory via sitemap crawl + Ahrefs top-pages
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- Content inventory via sitemap crawl + our-seo-agent CLI or pre-fetched data
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- Performance scoring (traffic, rankings, backlinks)
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- Content decay detection (pages losing traffic over time)
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- Content type classification (blog, product, service, landing, resource)
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@@ -85,15 +85,13 @@ python scripts/content_brief_generator.py --keyword "dental implant cost" --url
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- Internal linking suggestions
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- Korean content format recommendations
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## Ahrefs MCP Tools Used
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## Data Sources
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| Tool | Purpose |
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|------|---------|
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| `site-explorer-top-pages` | Get top performing pages |
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| `site-explorer-pages-by-traffic` | Pages ranked by organic traffic |
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| `site-explorer-organic-keywords` | Keywords per page |
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| `site-explorer-organic-competitors` | Find content competitors |
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| `site-explorer-best-by-external-links` | Best content by links |
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| Source | Purpose |
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|--------|---------|
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| `our-seo-agent` CLI | Primary data source (future); use `--input` for pre-fetched JSON |
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| WebSearch / WebFetch | Supplementary live data |
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| Notion MCP | Save audit report to database |
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## Output Format
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@@ -20,16 +20,10 @@ Audit existing content performance, identify topic gaps vs competitors, map topi
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## MCP Tool Usage
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### Ahrefs for Content Data
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### SEO Data
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```
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site-explorer-top-pages: Get top performing pages
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site-explorer-pages-by-traffic: Pages ranked by organic traffic
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site-explorer-organic-keywords: Keywords per page
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site-explorer-organic-competitors: Find content competitors
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site-explorer-best-by-external-links: Best content by backlinks
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keywords-explorer-matching-terms: Secondary keyword suggestions
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keywords-explorer-related-terms: LSI keyword suggestions
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serp-overview: Analyze top ranking results for a keyword
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our-seo-agent CLI: Primary content/traffic data source (future); use --input for pre-fetched JSON
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WebSearch / WebFetch: Supplementary content data
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```
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### WebSearch for Content Research
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@@ -47,7 +41,7 @@ notion-create-pages: Save audit reports to SEO Audit Log
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### 1. Content Audit
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1. Crawl sitemap to discover all content URLs
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2. Fetch top pages data from Ahrefs (traffic, keywords, backlinks)
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2. Fetch top pages data via our-seo-agent CLI, pre-fetched JSON, or WebSearch
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3. Classify content types (blog, product, service, landing, resource)
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4. Score each page performance (0-100 composite)
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5. Detect decaying content (traffic decline patterns)
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@@ -56,7 +50,7 @@ notion-create-pages: Save audit reports to SEO Audit Log
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8. Generate recommendations
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### 2. Content Gap Analysis
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1. Gather target site keywords from Ahrefs
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1. Gather target site keywords via our-seo-agent CLI or pre-fetched data
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2. Gather competitor top pages and keywords
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3. Identify topics present in competitors but missing from target
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4. Score gaps by priority (traffic potential + competition coverage)
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@@ -123,7 +117,7 @@ notion-create-pages: Save audit reports to SEO Audit Log
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## Limitations
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- Ahrefs API required for traffic and keyword data
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- our-seo-agent CLI or pre-fetched JSON required for traffic and keyword data
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- Competitor analysis limited to publicly available content
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- Content decay detection uses heuristic without historical data in standalone mode
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- Topic clustering requires minimum 3 topics per cluster
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