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seo-backlinks

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UpdatedJuly 10, 2026 at 22:22

Backlink profile analysis with free data sources: referring domains, anchor-text distribution, toxic-link detection, competitor link-gap analysis, disavow recommendations — works without paid APIs via Common Crawl web graphs and a verification crawler (Moz/Bing APIs used automatically when credentials exist). Use when the user says 'backlinks', 'link profile', 'referring domains', 'anchor text', 'toxic links', 'link gap', 'link building', 'disavow', or 'backlink audit'.

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name	seo-backlinks
description	Backlink profile analysis with free data sources: referring domains, anchor-text distribution, toxic-link detection, competitor link-gap analysis, disavow recommendations — works without paid APIs via Common Crawl web graphs and a verification crawler (Moz/Bing APIs used automatically when credentials exist). Use when the user says 'backlinks', 'link profile', 'referring domains', 'anchor text', 'toxic links', 'link gap', 'link building', 'disavow', or 'backlink audit'.
user-invocable	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":"2.2.0","category":"seo"}

Backlink Profile Analysis

Source Detection

Before analysis, detect available data sources:

DataForSEO MCP (premium): Check if dataforseo_backlinks_summary tool is available
Moz API (free signup): python3 scripts/backlinks_auth.py --check moz --json
Bing Webmaster (free signup): python3 scripts/backlinks_auth.py --check bing --json
Common Crawl (always available): Domain-level graph with PageRank
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

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: 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:

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: 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:

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: 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:

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:

DataForSEO available? → Use as primary (confidence: 1.0)
Moz configured? → Use for DA/PA/spam/anchors (confidence: 0.85)
Bing configured? → Use for links/competitor comparison (confidence: 0.70)
Always: Common Crawl for domain-level metrics (confidence: 0.50)
Always: Verification crawler for known link checks (confidence: 0.95)
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)