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seo-eeat

@hainrixz · 收录于 1 周前

Audit and strengthen E-E-A-T and trust signals on a page — verify author identity/credentials (Person schema, byline, author page, sameAs), Organization about/contact/policies, visible experience/expertise markers, and transparency (sourcing, disclosures), and generate Person/Organization trust JSON-LD. Module M16. Feeds both the Search SEO and AI Visibility scores.

适合你,如果你需要提升网页在搜索引擎中的可信度和排名。

/ 下载安装
seo-eeat.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add hainrixz/claude-seo-ai/seo-eeat
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- hainrixz/claude-seo-ai/seo-eeat
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify hainrixz/claude-seo-ai/seo-eeat
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
27GitHub stars
~798上下文体积 · 单文件
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怎么用

技能原文 SKILL.md作者撰写 · MIT · 788e7b4

seo-eeat (M16)

Experience, Expertise, Authoritativeness, and Trust are how both Google's quality systems and AI answer engines decide whether to rely on a page. Trust is foundational and now applies beyond YMYL. Schema details: references/schema-tier1.md.

Audits

Working from the PageSnapshot (rendered_dom if present, else raw_html):

  1. Author identity & credentials: is there a visible byline? A Person schema with name, jobTitle, knowsAbout, and sameAs[] (LinkedIn/Wikidata)? Does the byline link to an author/bio page? Are credentials/experience stated, not just a name?
  2. Organization trust: discoverable About and Contact pages; an Organization block with name, url, logo, contactPoint, sameAs[]; visible editorial/privacy/returns policies appropriate to the vertical.
  3. Experience & expertise markers: first-hand signals (original photos, "we tested", dates, methodology) and topical depth — not just generic prose.
  4. Transparency: sourcing/citations for claims, author disclosures (affiliate, sponsored, AI-assisted), last-reviewed dates. Defer the sameAs identity-graph detail to M6/seo-entity-linking.
Fixes
  • AUTO (fixable: auto): inject Person (author) and Organization trust JSON-LD built only from confirmed inputs — name, jobTitle, contactPoint, policy URLs the user supplies. The block is a diff for fix.
  • PROPOSED (fixable: proposed): draft a byline link or a sameAs set for per-item accept.
  • ADVISORY (fixable: advisory): writing real author bios, About/contact pages, or editorial policies — the tool never authors these. Never fabricate names, credentials, dates, or identity links — ask the user or leave a clearly-marked TODO placeholder.
Verification
  • Offline: node ${CLAUDE_SKILL_DIR}/../../scripts/validate-jsonld.mjs --url <u> plus dom_assert for visible byline/links/policy pages.
  • When confirming an identity link or a live About/contact page requires a fetch that is unavailable, status is needs_api — never a false pass.
Findings

Emit findings per schema/finding.schema.json. Examples:

  • M16.author.missing_person_schema — editorial page with a byline but no Person schema (status fail, severity 4, fixable: auto, axis both, confidence established).
  • M16.author.no_bio_page — byline does not link to an author/bio page (status warn, severity 3, fixable: proposed, axis both, confidence directional).
  • M16.org.no_contact_page — no discoverable About/Contact or contactPoint (status warn, severity 3, fixable: advisory, axis both, confidence directional).

Each finding: evidence.observed quotes what is on the page; verification.reproduce is the runnable command above; expected_impact is banded + confidence-tagged (no naked %).

Honesty
  • E-E-A-T is not a single measurable score Google exposes — it is a quality framing assessed via many signals. Adding a Person block or an "author bio" is not a direct ranking lever; mark such impact directional, never as a guaranteed gain.
  • A fabricated author, invented credentials, or fake review markup is worse than none — it is a trust risk for Search and AI. The tool only emits trust signals the user can substantiate.
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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