Backlink Profile Analysis

Source Detection

Before analysis, detect available data sources:

1. DataForSEO MCP (premium): Check if dataforseo_backlinks_summary tool is available
2. Moz API (free signup): python3 scripts/backlinks_auth.py --check moz --json
3. Bing Webmaster (free signup): python3 scripts/backlinks_auth.py --check bing --json
4. Common Crawl (always available): Domain-level graph with PageRank
5. Verification Crawler (always available): Checks if known backlinks still exist

Run python3 scripts/backlinks_auth.py --check --json to detect all sources at once.

If no sources are configured beyond the always-available tier:

• Still produce a report using Common Crawl domain metrics
• Suggest: "Run /seo backlinks setup to add free Moz and Bing API keys for richer data"

Quick Reference

Analysis Framework

Produce all 7 sections below. Each section lists data sources in preference order.

1. Profile Overview

DataForSEO: dataforseo_backlinks_summary → total backlinks, referring domains, domain rank, follow ratio, trend.

Moz API: python3 scripts/moz_api.py metrics <url> --json → Domain Authority, Page Authority, Spam Score, linking root domains, external links.

Common Crawl: python3 scripts/commoncrawl_graph.py <domain> --json → in-degree (referring domain count), PageRank, harmonic centrality.

Scoring:

2. Anchor Text Distribution

DataForSEO: dataforseo_backlinks_anchors

Moz API: python3 scripts/moz_api.py anchors <url> --json

Bing Webmaster: python3 scripts/bing_webmaster.py links <url> --json (extract anchor text from link details)

Healthy distribution benchmarks:

Flag if exact-match anchors exceed 15% -- this is a Google Penguin risk signal.

3. Referring Domain Quality

DataForSEO: dataforseo_backlinks_referring_domains

Moz API: python3 scripts/moz_api.py domains <url> --json → domains with DA scores

Common Crawl: python3 scripts/commoncrawl_graph.py <domain> --json → top referring domains (domain-level, no authority scores)

Analyze:

• TLD distribution: .edu, .gov, .org = high authority. Excessive .xyz, .info = low quality
• Country distribution: Match target market. 80%+ from irrelevant countries = PBN signal
• Domain rank distribution: Healthy profiles have links from all authority tiers
• Follow/nofollow per domain: Sites that only nofollow = limited SEO value

4. Toxic Link Detection

DataForSEO: dataforseo_backlinks_bulk_spam_score + toxic patterns from reference

Moz API: Spam Score from python3 scripts/moz_api.py metrics <url> --json (1-17% scale, >11% = high risk)

Verification Crawler: python3 scripts/verify_backlinks.py --target <url> --links <file> --json (verify suspicious links still exist)

High-risk indicators (flag immediately):

• Links from known PBN (Private Blog Network) domains
• Unnatural anchor text patterns (100% exact match from a domain)
• Links from penalized or deindexed domains
• Mass directory submissions (50+ directory links)
• Link farms (sites with 10K+ outbound links per page)
• Paid link patterns (footer/sidebar links across all pages of a domain)

Medium-risk indicators (review manually):

• Links from unrelated niches
• Reciprocal link patterns
• Links from thin content pages (<100 words)
• Excessive links from a single domain (>50 backlinks from 1 domain)

Load references/backlink-quality.md for the full 30 toxic patterns and disavow criteria.

5. Top Pages by Backlinks

DataForSEO: dataforseo_backlinks_backlinks with target type "page"

Moz API: python3 scripts/moz_api.py pages <domain> --json

Find:

• Which pages attract the most backlinks
• Pages with high-authority links (link magnets)
• Pages with zero backlinks (internal linking opportunities)
• 404 pages with backlinks (redirect opportunities to reclaim link equity)

6. Competitor Gap Analysis

DataForSEO: dataforseo_backlinks_referring_domains for both domains, then compare

Bing Webmaster (unique!): python3 scripts/bing_webmaster.py compare <url1> <url2> --json — the only free tool with built-in competitor comparison

Moz API: Compare DA/PA between domains via python3 scripts/moz_api.py metrics <url> --json for each

Output:

• Domains linking to competitor but NOT to target = link building opportunities
• Domains linking to both = validate existing relationships
• Domains linking only to target = competitive advantage
• Top 20 link building opportunities with domain authority

7. New and Lost Backlinks

DataForSEO only: dataforseo_backlinks_backlinks with date filters for 30/60/90 day changes

Verification Crawler: For known links, verify current status with python3 scripts/verify_backlinks.py

Note: Free sources cannot track new/lost links over time. If this section is requested without DataForSEO, inform the user: "Link velocity tracking requires the DataForSEO extension. Free sources provide point-in-time snapshots only."

Red flags:

• Sudden spike in new links (possible negative SEO attack)
• Sudden loss of many links (site penalty or content removal)
• Declining velocity over 3+ months (content not attracting links)

Backlink Health Score

Calculate a 0-100 score. When mixing sources, apply confidence weighting:

Data sufficiency gate: Count how many of the 7 factors have at least one data source available.

• 4+ factors with data: Produce a numeric 0-100 score (redistribute missing weights proportionally)
• Fewer than 4 factors: Do NOT produce a numeric score. Instead display:

  Backlink Health Score: INSUFFICIENT DATA (X/7 factors scored)
  

  Show individual factor scores that ARE available with their source and confidence. Recommend: "Configure Moz API (free) for a scoreable profile. Run /seo backlinks setup"

When only CC is available, cap maximum score at 70/100. A numeric score with fewer than 4 data sources is misleading — it implies poor health when the reality is we simply lack data.

Output Format

Backlink Health Score: XX/100 (or INSUFFICIENT DATA)

Critical Issues (fix immediately)

High Priority (fix within 1 month)

Medium Priority (ongoing improvement)

Link Building Opportunities (top 10)

Error Handling

Fallback cascade:

1. DataForSEO available? → Use as primary (confidence: 1.0)
2. Moz configured? → Use for DA/PA/spam/anchors (confidence: 0.85)
3. Bing configured? → Use for links/competitor comparison (confidence: 0.70)
4. Always: Common Crawl for domain-level metrics (confidence: 0.50)
5. Always: Verification crawler for known link checks (confidence: 0.95)
6. Nothing works? → "Run /seo backlinks setup to configure free APIs"

Pre-Delivery Review (MANDATORY)

Before presenting any backlink analysis to the user, run this checklist internally. Do NOT skip this step. Fix any issues found before showing the report.

Fact-Check Every Claim

• Schema claims: Did parse_html return @type for each block? If any @type is missing, re-check — it may use @graph wrapper (valid JSON-LD, not malformed).
• "link_removed" findings: Is the page JS-rendered? If unverifiable_js, say so — never report a JS-rendered page as "link removed" (that's a false negative).
• H1 findings: Are any H1s in the h1_suspicious list? If so, note they are likely counters/stats, not semantic headings.
• Reciprocal links: If site A links to site B AND B links back to A, flag it as a reciprocal link pattern. Check outbound links against verified inbound sources.
• Health score: Are 4+ of 7 factors scored? If not, report INSUFFICIENT DATA — never show a misleading numeric score.

Verify Data Source Labels

• Every metric in the report has a source label (e.g., "Parsed (0.95)", "CC (0.50)")
• Every "not found" result distinguishes between "not crawled" vs "below threshold" vs "error"
• Social media pages flagged as unverifiable_js (not link_removed)

Cross-Check Consistency

• Platform detection matches actual signals (check for wp-content, shopify CDN, etc.)
• Referring domain count in summary matches the actual verified links list
• No claim is presented without a data source backing it

If ANY check fails, fix the finding before presenting. Never present inferred data as fact.

Post-Analysis

After completing any backlink analysis command, always offer: "Generate a professional PDF report? Use /seo google report"

Reference Documentation

Load on demand (do NOT load at startup):

• skills/seo/references/backlink-quality.md -- Detailed toxic link patterns and scoring methodology (shared reference, load when analyzing toxic links or spam scores)
• skills/seo/references/free-backlink-sources.md -- Source comparison, confidence weighting, setup guides (shared reference, load when configuring free backlink APIs)

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Supporting file: LICENSE.txt

MIT License - see repository root LICENSE file for complete terms.

Copyright (c) 2026 AgriciDaniel
https://github.com/AgriciDaniel/claude-seo

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Supporting file: skills/seo/references/backlink-quality.md

Backlink Quality Scoring Methodology

Toxic Link Indicators (30 Patterns)

Definite Spam (auto-flag)

1. Link from domain with 10,000+ outbound links per page
2. Link from domain with no indexed pages in Google
3. Link from domain registered <30 days ago with 100+ outbound links
4. Exact-match anchor text from 5+ unrelated domains
5. Links from doorway pages (thin content, keyword-stuffed)
6. Links from hacked sites (pharma/casino injections)
7. Links from known link networks (check against known PBN lists)
8. Footer/sidebar site-wide links from unrelated domains
9. Links from auto-generated content (spun articles)
10. Links from domains with manual Google penalties

Likely Spam (manual review)

1. Links from domains with >90% outbound link ratio
2. Foreign-language domains linking to English content (and vice versa)
3. Links from expired/auctioned domains repurposed for link building
4. Links from pages with >50 outbound links
5. Links from sites with no real traffic (parked domains)
6. Reciprocal link patterns across 10+ domains
7. Links from Web 2.0 properties with thin content
8. Links from article directories (EzineArticles, ArticleBase)
9. Links from low-quality guest post networks
10. Links from unrelated niches (e.g., pet site linking to SaaS)

Potentially Problematic (monitor)

1. Links from social bookmarking sites at scale
2. Links from forum profiles (not discussions)
3. Links from press release syndication networks
4. Links from coupon/deal aggregators
5. Links from generic directories (not industry-specific)
6. Links with hidden/invisible anchor text
7. Links from pages with cloaked content
8. Links from sites with thin affiliate content
9. Links from comment sections without editorial context
10. Links from nofollow-only domains (limited SEO value)

Anchor Text Ratio Benchmarks by Industry

Link Velocity Red Flags

Disavow Recommendations

When to disavow:

• Domain has received a manual penalty from Google
• Clear evidence of negative SEO attack
• Toxic link ratio exceeds 10% of total profile
• Specific domains identified as PBN or link farms

When NOT to disavow:

• Low-quality links that Google likely ignores anyway
• Nofollow links (already devalued by Google)
• Links from legitimate but low-authority sites
• Small number of spam links (<2% of profile)

Disavow file format:

# Toxic domains identified by Claude SEO backlink analysis
# Date: YYYY-MM-DD
# Total domains disavowed: X
domain:spamsite1.com
domain:linkfarm2.net
domain:pbn-network3.xyz

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Supporting file: skills/seo/references/free-backlink-sources.md

Free Backlink Data Sources

Reference for the seo-backlinks skill. Loaded on demand when analyzing backlinks with free sources.

Source Comparison

Confidence Weighting

When merging data from multiple sources, apply confidence weights to each metric:

Composite formula:

weighted_score = Σ(source_score × confidence × factor_weight) / Σ(confidence × factor_weight)

When only Common Crawl is available, cap the maximum health score at 70/100 and note "limited to domain-level metrics" in the report.

Source Details

Moz API (Tier 1)

• Endpoint: https://api.moz.com/jsonrpc (JSON-RPC 2.0)
• Free tier: 2,500 rows/month, 1 request per 10 seconds (verify current limits at https://moz.com/products/api — free tier limits may change)
• Signup: https://moz.com/products/api (credit card required, not charged)
• Data: Domain Authority (0-100), Page Authority, Spam Score (1-17%), link counts, referring domains, anchor text distribution
• Script: scripts/moz_api.py
• Commands: metrics, domains, anchors, pages
• Blind spots: No link velocity, no toxic link patterns beyond Spam Score, 3-day update lag, smaller index than Ahrefs/Semrush

Bing Webmaster Tools (Tier 2)

• Endpoint: https://ssl.bing.com/webmaster/api.svc/json/
• Free tier: Unlimited for verified sites
• Signup: https://www.bing.com/webmasters (Microsoft account)
• Unique feature: Competitor backlink comparison (no other free tool offers this)
• Data: Inbound links with anchor text, source URL, discovery date
• Script: scripts/bing_webmaster.py
• Commands: links, counts, compare
• Blind spots: Only Bing-indexed pages (~15% of web), verified sites only, no authority metrics, no spam scoring

Common Crawl Web Graph (Always Available)

• Data source: s3://commoncrawl/projects/hyperlinkgraph/
• Releases: Quarterly (e.g., cc-main-2025-18)
• No auth needed: Public data, free to download
• Data: Domain-level in-degree, PageRank, harmonic centrality, referring domains
• Script: scripts/commoncrawl_graph.py
• Cache: ~/.cache/claude-seo/commoncrawl/ (90-day TTL)
• Blind spots: No anchor text, no page-level data, monthly/quarterly freshness, domain-level only (e.g., "nytimes.com links to example.com" but not which page)

Verification Crawler (Always Available)

• No auth needed: Uses existing fetch_page.py + parse_html.py
• Data: Binary verification (link exists/lost/moved), anchor text, rel attributes
• Script: scripts/verify_backlinks.py
• Input: JSON file with [{"source_url": "..."}] entries
• Polite crawling: 1-second delay between requests to same domain
• Best for: Checking if known backlinks still exist, monitoring link health

When to Recommend DataForSEO Upgrade

Suggest the paid DataForSEO extension when:

• User needs toxic link detection beyond Moz's basic Spam Score
• User needs competitor gap analysis at scale (Bing only compares verified sites)
• User needs link velocity trends (new/lost links over time)
• User needs real-time data (free sources update monthly at best)
• User manages multiple client sites (free tier limits are per-account)
• User needs disavow file generation with confidence scoring

Data Quality Reality Check

• Commercial tools index 35-45 trillion links across 500M+ referring domains
• Free sources combined capture 20-40% of raw backlink data
• But 60-70% of actionable intelligence since highest-authority links appear in free samples
• For sites with <500 backlinks, free sources can capture 50%+ of the meaningful profile
• Referring domain count matters more than raw backlink count for SEO
• Top 50-100 referring domains capture the majority of link authority

Five Systematic Biases in Free Data

1. Popularity bias: Free tools crawl popular sites more, underrepresenting niche sites
2. Truncation bias: All free tools cap at 100-1,000 links, hiding the long tail
3. Own-site restriction: GSC and Ahrefs Webmaster Tools only work for verified properties
4. Missing quality metrics: Raw CC data lacks authority/toxicity scores
5. Freshness lag: Free sources update monthly at best vs. minutes for commercial