seo-backlinks

$27

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:

• 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)