seo-backlinks

安装量: 1.1K
排名: #3934

安装

npx skills add https://github.com/agricidaniel/claude-seo --skill seo-backlinks
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):
python scripts/backlinks_auth.py --check moz --json
Bing Webmaster
(free signup):
python 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
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
Full backlink profile analysis (uses all available sources)
/seo backlinks gap
Competitor backlink gap analysis
/seo backlinks toxic
Toxic link detection and disavow recommendations
/seo backlinks new
New and lost backlinks (DataForSEO only)
/seo backlinks verify --links
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 --json
→ Domain Authority, Page Authority, Spam Score, linking root domains, external links.
Common Crawl:
python scripts/commoncrawl_graph.py --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 --json
Bing Webmaster:
python scripts/bing_webmaster.py links --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 --json
→ domains with DA scores
Common Crawl:
python scripts/commoncrawl_graph.py --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 --json
(1-17% scale, >11% = high risk)
Verification Crawler:
python scripts/verify_backlinks.py --target --links --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 --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 --json
— the only free tool with built-in competitor comparison
Moz API:
Compare DA/PA between domains via
python scripts/moz_api.py metrics --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)
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