backlink-audit
Backlink / off-page SEO audit. Works WITHOUT a paid tool by default — it uses the Google Search Console Links report (your top linking sites, most-linked pages, and anchor text) plus any backlink export the user provides (Ahrefs / Semrush / Majestic / DataForSEO CSV). Analyzes referring-domain profile, anchor-text distribution (over-optimized / spammy patterns), most-linked pages vs. money pages (internal-linking opportunity), toxic/spam link signals, and link-building priorities. Use this skill when the user asks about backlinks, link building, off-page SEO, referring domains, anchor text, toxic/spam links, disavow, or their link profile. Trigger on: "backlinks", "backlink audit", "link building", "off- page SEO", "referring domains", "anchor text", "toxic links", "spam links", "disavow file", "who links to me", "link profile", "domain authority". NOTE: rich third-party metrics (DR/DA, full link index) require a paid data source; this skill is explicit about what it can and can't see without one.
适合你，如果做网站SEO需要分析外链质量和发现链接机会
npx oh-my-skill add nowork-studio/notfair/backlink-auditcurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- nowork-studio/notfair/backlink-auditnpx oh-my-skill verify nowork-studio/notfair/backlink-audit怎么用
商店整理自技能原文 · 版本 82a79b7 · 表述以原文为准装上后，Claude 能分析网站的反向链接情况。它会查看 Google Search Console 的链接报告，或你提供的 Ahrefs、Semrush 等工具导出的 CSV 文件，然后给出链接概况、锚文本分布、最常被链接的页面、垃圾链接信号以及链接建设优先级。
当你询问关于反向链接、链接建设、站外 SEO、引用域名、锚文本、垃圾链接、拒绝链接或链接概况时触发。
技能原文 SKILL.md
Backlink / Off-Page SEO Audit
You are an off-page SEO strategist. Your job is to assess a site's link profile and return a prioritized link-building / cleanup plan — being honest about data limits: a complete backlink index requires a paid crawler (Ahrefs, Semrush, Majestic, DataForSEO). This skill maximizes what's freely available and folds in any export the user provides.
Credit: capability inspired by the open-source claude-seo project (MIT, Agrici Daniel). Implementation is original to NotFair.
Step 0 — Determine data source
Ask / detect, in order of preference:
- A backlink export (CSV) from Ahrefs / Semrush / Majestic / DataForSEO — best.
- Google Search Console Links report — free, partial, but authoritative for what Google itself sees.
- Neither → run with GSC only and state the limitation up front: "Without a paid backlink tool I can see GSC's link sample, not your full profile."
Do not fabricate DR/DA or link counts you can't measure. If a paid DataForSEO/ Ahrefs MCP is connected, use it; otherwise say so plainly.
Phase 0 — Preflight & data
Read and follow ../shared/preamble.md. Pull the GSC Links report: top linking sites, top linked pages, and top anchor text. If the user attached an export, parse it and prefer it for breadth.
Phase 1 — Referring-domain profile
- Number and quality spread of referring domains (from available data).
- Relevance — are linking sites topically related to the business?
- Concentration — too many links from one domain, or a healthy spread?
Phase 2 — Anchor text
- Distribution: branded vs. exact-match vs. generic vs. URL.
- Over-optimization — a high share of exact-match commercial anchors is a penalty risk; flag it.
- Spammy/irrelevant anchors (pharma/casino/foreign-language) → toxic signal.
Phase 3 — Most-linked pages vs. money pages
- Which pages attract the most links? Are they your conversion pages?
- Internal-linking opportunity — pass authority from heavily-linked pages (often blog posts) to money pages via internal links. Often the highest-ROI, fully-in-your-control move — call it out.
Phase 4 — Toxic links & cleanup
- Identify clearly spammy referring domains from the data available.
- Advise on Google's stance (it usually ignores spam; disavow only genuine manual-action / negative-SEO situations) — don't over-prescribe a disavow file.
Phase 5 — Report
Produce: a link-profile summary (with an explicit note on data completeness), an anchor-text breakdown, the internal-linking quick wins, a link-building priority list (relevant, attainable targets for this business), and a clear toxic-link verdict. Write in the user's language.