seo-backlinks

name	seo-backlinks
description	Backlink profile analysis: referring domains, anchor text distribution, toxic link detection, competitor gap analysis. Works with free APIs (Moz, Bing Webmaster, Common Crawl) and DataForSEO extension. Use when user says backlinks, link profile, referring domains, anchor text, toxic links, link gap, link building, disavow, or backlink audit.
user-invokable	true
argument-hint	<url>
license	MIT
compatibility	Free: Common Crawl + verify always available. Optional: Moz API, Bing Webmaster (free signup). Premium: DataForSEO extension.
metadata	{"author":"AgriciDaniel","version":"1.9.6","category":"seo"}

Backlink Profile Analysis

Shared Data Cache

Step 0 -- Check shared data cache:

Before gathering, check .seo-cache/ for reusable context from related SEO skills. Reference: ../seo/references/shared-data-cache.md for schemas and dependency map.

Check these cache files when present:

• .seo-cache/site-meta.json for domain, business type, industry, and crawl context

• .seo-cache/audit-scores.json for prior full-audit priorities

• .seo-cache/pages/{url-slug}/page-analysis.json for page-level context when a URL is provided

• If found: parse and use clearly valid fields (note "Using cached [X] from [date]")

• If missing, corrupt, or irrelevant: continue with fresh evidence

• If the user says "refresh" or "re-run": ignore cache reads and overwrite on write

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): python scripts/backlinks_auth.py --check moz --json
3. Bing Webmaster (free signup): python 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 python 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

Command	Purpose
/seo backlinks <url>	Full backlink profile analysis (uses all available sources)
/seo backlinks gap <url1> <url2>	Competitor backlink gap analysis
/seo backlinks toxic <url>	Toxic link detection and disavow recommendations
/seo backlinks new <url>	New and lost backlinks (DataForSEO only)
/seo backlinks verify <url> --links <file>	Verify known backlinks still exist
/seo backlinks setup	Show setup instructions for free backlink APIs

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

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

Scoring:

Metric	Good	Warning	Critical
Referring domains	>100	20-100	<20
Follow ratio	>60%	40-60%	<40%
Domain diversity	No single domain >5%	1 domain >10%	1 domain >25%
Trend	Growing or stable	Slow decline	Rapid decline (>20%/quarter)

2. Anchor Text Distribution

DataForSEO: dataforseo_backlinks_anchors

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

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

Healthy distribution benchmarks:

Anchor Type	Target Range	Over-Optimization Signal
Branded (company/domain name)	30-50%	<15%
URL/naked link	15-25%	N/A
Generic ("click here", "learn more")	10-20%	N/A
Exact match keyword	3-10%	>15%
Partial match keyword	5-15%	>25%
Long-tail / natural	5-15%	N/A

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

Common Crawl: python 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 python scripts/moz_api.py metrics <url> --json (1-17% scale, >11% = high risk)

Verification Crawler: python 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: python 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!): python 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 python 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 python 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:

Factor	Weight	Sources (preference order)	Confidence
Referring domain count	20%	DataForSEO > Moz > CC in-degree	1.0 / 0.85 / 0.50
Domain quality distribution	20%	DataForSEO > Moz DA distribution	1.0 / 0.85
Anchor text naturalness	15%	DataForSEO > Moz > Bing anchors	1.0 / 0.85 / 0.70
Toxic link ratio	20%	DataForSEO > Moz spam score	1.0 / 0.85
Link velocity trend	10%	DataForSEO only	1.0
Follow/nofollow ratio	5%	DataForSEO > Bing details	1.0 / 0.70
Geographic relevance	10%	DataForSEO > Bing country	1.0 / 0.70

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)

Section	Status	Score	Data Source
Profile Overview	pass/warn/fail	XX/100	Moz (0.85)
Anchor Distribution	pass/warn/fail	XX/100	Moz (0.85)
Referring Domain Quality	pass/warn/fail	XX/100	CC (0.50)
Toxic Links	pass/warn/fail	XX/100	Moz Spam (0.85)
Top Pages	info	N/A	Moz (0.85)
Link Velocity	pass/warn/fail	XX/100	DataForSEO only

Critical Issues (fix immediately)

High Priority (fix within 1 month)

Medium Priority (ongoing improvement)

Link Building Opportunities (top 10)

Error Handling

Error	Cause	Resolution
No sources configured	No API keys, no DataForSEO	Run /seo backlinks setup
Moz rate limit	Free tier: 1 req/10s	Wait 10 seconds, retry. Built into script.
Bing site not verified	Site not verified in Bing	Verify at https://www.bing.com/webmasters
CC download timeout	Large graph file, slow connection	Use --timeout 180 flag
DataForSEO unavailable	Extension not installed	Run ./extensions/dataforseo/install.sh
No backlink data returned	Domain too new or very small	Note: small sites may have <10 backlinks

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)

Write to shared data cache

After completing all work, write a concise JSON summary to .seo-cache/ when the workflow produced durable findings. Use the schemas and naming rules in ../seo/references/shared-data-cache.md; include at least cache_type, analyzed_at, source URL/domain, key findings, issues, recommendations, and tool limitations. Add .seo-cache/ to .gitignore if it is missing.