seo-backlinks

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): "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run backlinks_auth.py --check moz --json
3. Bing Webmaster (free signup): "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run backlinks_auth.py --check bing --json
4. Keywords Everywhere (free signup, single-metric): "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run backlinks_auth.py --check keywordseverywhere --json
5. Common Crawl (always available): Domain-level graph with PageRank
6. Verification Crawler (always available): Checks if known backlinks still exist

Run "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run 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: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run moz_api.py metrics <url> --json → Domain Authority, Page Authority, Spam Score, linking root domains, external links.

Keywords Everywhere: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run keywordseverywhere_api.py rank <domain> --json → 0-10 domain rank only (no link counts). Use as a fallback when Moz isn't configured; do not use in place of Moz when both are available.

Common Crawl: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run commoncrawl_graph.py <domain> --json → PageRank, harmonic centrality, and low-confidence rank/presence data.

Scoring:

2. Anchor Text Distribution

DataForSEO: dataforseo_backlinks_anchors

Moz API: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run moz_api.py anchors <url> --json

Bing Webmaster: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run bing_webmaster.py links <url> --json (extract anchor text from link details)

Healthy distribution benchmarks:

Flag if exact-match anchors exceed 15% as a review heuristic; it may indicate unnatural or link-spam patterns.

3. Referring Domain Quality

DataForSEO: dataforseo_backlinks_referring_domains

Moz API: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run moz_api.py domains <url> --json → domains with DA scores

Common Crawl: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run commoncrawl_graph.py <domain> --json → domain-level rank/presence data, no verified referring-domain counts

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: Raw vendor spam_score from "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run moz_api.py metrics <url> --json (source-label the value; apply thresholds only if verified against current Moz docs)

Verification Crawler: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run 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 ../seo/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: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run 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: "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run bing_webmaster.py compare <url1> <url2> --json only when both properties are registered and accessible to the same Bing API account. For arbitrary competitors, use DataForSEO, Moz, or Common Crawl.

Moz API: Compare DA/PA between domains via "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run 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 "${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run verify_backlinks.py --target <url> --links <file> --json

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"

MUST NOT: score what you did not measure

When Common Crawl is the only available source, you MUST NOT produce a numeric score of any kind -- not a health score, not a per-factor score, not an "approximate" or "estimated" figure. Common Crawl supplies rank and presence signals only. Report low-confidence rank/presence data and the literal string Not Assessed in place of every number.

A finding written with source: not-assessed MUST NOT carry a numeric score.

A numeric score with fewer than 4 data sources is misleading: it implies poor health when the reality is that we simply lack data.

Validation gate (required, not optional)

This rule has been stated in this skill before and was violated anyway, so it is now checkable. Before writing any backlink output, run the validator:

"${CLAUDE_PLUGIN_ROOT}/scripts/claude-seo" run validate_backlink_report.py --report <report>.json --json

Pass the sources you actually collected (cc_data, moz_data, bing_data, dataforseo_data, scoring_factors, and your findings list). The source_score_consistency check fails the report with status: FAIL when:

• a numeric score is present but no scoreable source (Moz, Bing, or DataForSEO) supplied data -- i.e. the Common-Crawl-only case, and
• any finding marked source: not-assessed carries a numeric score, value, or health_score.

If the validator returns status: FAIL, do not present the report. Fix the findings -- replace the offending numbers with Not Assessed -- and re-run until it passes.

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 registered-property links and comparison only when both properties are accessible (confidence: 0.70)
4. Moz not configured but Keywords Everywhere is? → Use for a Profile Overview rank-only fallback (confidence: 0.60; single metric, no link counts/anchors)
5. Always: Common Crawl for domain-level metrics (confidence: 0.50)
6. Always: Verification crawler for known link checks (confidence: 0.95)
7. 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)