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toxic-backlink-auditor

面向 SEO 的引荐域名毒性审计 Skill：通过 DataForSEO 或通用 Backlink API 拉取引荐域名及其属性，按低权重域名、可疑 TLD、全站链接、过度优化锚文本、异常增速等信号加权评分，划分低/中/高风险，生成按分数排序的 disavow 候选清单与组合统计；结果标注为概率性，需人工复核。

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最近更新 2026-10-18 21:44:29
分类：营销依赖：代码执行 / 读写文件 / 第三方 API应用场景：市场分析 / 报告撰写 / 数据处理 / 推荐

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add seoskills.sh/toxic-backlink-auditor
Metadata
name
toxic-backlink-auditor
description
Fetches the site's referring domains and scores each for toxicity from spam signals such as link farms, irrelevant TLDs, sitewide footer links, unnatural velocity, and over-optimized anchors, outputting a prioritized disavow-candidate list. Use when the user wants a backlink risk audit, disavow file preparation, or to assess penalty exposure.
metadata
{"title":"Toxic Backlink Auditor","category":"link-building"}

Toxic Backlink Auditor

AGENT ROLE: Autonomous backlink-risk agent. Score each referring domain against the toxicity signals in references/toxicity_signals.json and emit the JSON in references/output.schema.json, including a disavow candidate list. Recommend review; never auto-submit a disavow.

OBJECTIVE

Assign each referring domain a toxicity score from spam signals, classify low/medium/high risk, and produce a prioritized disavow-candidate list with the reason per domain — so a human can review before disavowing.

INPUTS

  • target (REQUIRED): the domain to audit.
  • disavow_threshold (OPTIONAL, default 70): toxicity score at/above which a domain is a disavow candidate.
  • max_domains (OPTIONAL, default 5000).

AUTHENTICATION (Backlink API)

  1. REQUIRE a provider: DATAFORSEO_LOGIN+DATAFORSEO_PASSWORD (Basic) or BACKLINK_API_KEY.
    • IF absent THEN STOP error.code="AUTH_MISSING_BACKLINK_PROVIDER".
  2. Endpoint (DataForSEO): POST https://api.dataforseo.com/v3/backlinks/referring_domains/live with backlink attributes (rank, backlinks, dofollow, first_seen, lost_date, anchor sample).

EXPECTED TOOL CALLS

  • Run scripts/toxic_backlinks.py --target example.com.
  • Fetch the referring-domain list with per-domain attributes needed by the signals (rank, backlink count, dofollow ratio, TLD, first-seen dates, anchors).

PROCEDURE (deterministic, per referring domain)

STEP 1 — FETCH referring domains + attributes. STEP 2 — SCORE toxicity by summing weighted signals from toxicity_signals.json:

  • very low domain rank / no organic presence.
  • spammy or irrelevant TLD (.xyz, .top, .loan, country mismatches at scale).
  • sitewide link (backlinks count ≈ pages, footer/blogroll pattern).
  • over-optimized commercial anchor from a low-quality domain.
  • unnatural velocity (many links first-seen in a tight burst).
  • nofollow-only from a link farm, or link networks (shared IP/registrant when available). STEP 3 — CLASSIFY low (<40) | medium (40–69) | high (>=70); cap at 100. STEP 4 — DISAVOW CANDIDATES = domains with score >= disavow_threshold; format as domain:example-spam.com lines. STEP 5 — EMIT the scored list (worst first), the disavow candidates, and profile-level stats (toxic %, dofollow ratio).

RATE LIMITS & ERROR HANDLING

  • Provider 429/402 → backoff 2^attempt (max 5) then STOP error.code="RATE_LIMITED"/PROVIDER_PAYMENT_REQUIRED with partial.
  • Page through large profiles; HARD cap at max_domains (hit_cap=true).

MISSING / INSUFFICIENT DATA

  • Toxicity is PROBABILISTIC — always mark output advisory=true and require human review before disavow. Never claim certainty that a link is harmful.
  • A missing attribute lowers that signal's contribution rather than assuming the worst.
  • Recently-lost links (lost_date set) are noted but excluded from disavow candidates (already gone).

OUTPUT

One JSON object per references/output.schema.json. No prose.

FILES

  • scripts/toxic_backlinks.py — provider client, weighted toxicity scoring, disavow formatting.
  • references/toxicity_signals.json — signals + weights + spam TLD list.
  • references/output.schema.json — output contract.