
Seo Backlinks
Audit referring domains, anchors, toxicity, and competitor link gaps using free APIs and optional DataForSEO.
Overview
seo-backlinks is an agent skill most often used in Launch (also Grow analytics) that analyzes referring domains, anchors, toxicity, and competitor link gaps from free and premium SEO data sources.
Install
npx skills add https://github.com/agricidaniel/claude-seo --skill seo-backlinksWhat is this skill?
- Detects data tier: DataForSEO MCP, Moz API, Bing Webmaster, Common Crawl, and live verification crawl
- Runs `backlinks_auth.py --check --json` to see which sources are configured
- Covers referring domains, anchor distribution, toxic links, competitor gaps, and disavow-oriented reporting
- Works from free tiers (Common Crawl + verify) when premium MCP is absent
- User-invokable with URL argument hint for site-level analysis
- Source detection script: backlinks_auth.py --check --json
- Skill metadata version 2.0.0
Adoption & trust: 1.6k installs on skills.sh; 8.5k GitHub stars; 2/3 security scanners passed (skills.sh audits).
What problem does it solve?
You do not know which domains link to you, whether anchors look natural, or which toxic links need disavow attention.
Who is it for?
Founders auditing a site URL with a mix of free APIs and optional DataForSEO MCP.
Skip if: Pure technical SEO crawls with no off-page focus, or teams that refuse any external API or script execution.
When should I use this skill?
User mentions backlinks, link profile, referring domains, anchor text, toxic links, link gap, link building, disavow, or backlink audit (optional URL argument).
What do I get? / Deliverables
You get a source-aware backlink report with gaps, risk flags, and next steps for link building or cleanup.
- Backlink profile report with source tier noted
- Toxic or suspicious link flags and competitor gap notes
- Disavow-oriented recommendations when applicable
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Launch / seo is the canonical shelf because backlink profile work supports getting found and trust signals for distribution. Seo subphase matches user triggers: backlinks, anchor text, disavow, and link-building audits.
Where it fits
Run a full referring-domain and anchor audit before a content or PR launch.
Re-check live backlinks monthly with the verification crawler after campaigns.
Compare competitor link graphs to judge how hard SEO-led validation will be.
Estimate authority gaps versus incumbent sites in your niche.
How it compares
Off-page checker skill with scripted auth detection—not a single-vendor backlink dashboard replacement.
Common Questions / FAQ
Who is seo-backlinks for?
Solo builders and small teams running Claude or similar agents who need backlink audits without always paying for enterprise SEO tools.
When should I use seo-backlinks?
In Launch for SEO and link-gap work before major distribution pushes; in Grow when monitoring authority and content-driven referrals; and in Validate when comparing competitor link profiles for positioning.
Is seo-backlinks safe to install?
It runs local Python scripts and may call third-party SEO APIs—review the Security Audits panel on this page and scope API keys minimally.
SKILL.md
READMESKILL.md - Seo Backlinks
MIT License - see repository root LICENSE file for complete terms. Copyright (c) 2026 AgriciDaniel https://github.com/AgriciDaniel/claude-seo --- 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: "2.0.0" category: seo --- # 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): `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:** `pytho