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Yao geo page audit

Skill ShenyuanNext/wepr-growth-skills/skills/yao-geo-page-audit

Diagnose a website page or small page set for GEO readiness with authoritative public evidence, systematic page analysis, code/content/schema fixes, and four-format Chinese report delivery.From its SKILL.md

Install
npx -y skills add ShenyuanNext/wepr-growth-skills --skill yao-geo-page-audit

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 28 days oldThe repository was created 28 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

SKILL.md

4.8 KB, 921 tokens by cl100k_base, as published. Nobody here has run it

<!-- Copyright © 2026 姚金刚. All rights reserved. Project: yao-geo-page-audit Created by: 姚金刚 Date: 2026-05-16 X: https://x.com/yaojingang -->

yao-geo-page-audit

Use this skill when the user wants a GEO Page Audit, website/page GEO diagnosis, page technical audit, AI extractability audit, schema/HTML module advice, or code/content repair list for a URL.

Job

Given a target URL or website, diagnose the homepage, a representative first-level page, and a representative second-level page when possible. Output development-ready and content-ready recommendations that improve how public pages can be discovered, parsed, cited, and summarized by search-driven AI systems. By default, analyze public page readiness and public evidence coverage only; do not estimate AI-platform recall, rankings, citation share, or internal platform behavior unless the user provides platform sampling data.

Workflow

  1. Read references/research-foundation.md, references/authority-reference-model.md, and references/report-module-taxonomy.md. Frame the audit as a five-stage chain: discovery, retrieval candidate, main-content extraction, evidence quality, generated citation.
  2. Identify page type and sample scope. If the input is a homepage, select homepage, one representative first-level page, and one representative second-level page. State the selection basis and unresolved input gaps.
  3. Build an evidence ledger. Prefer official pages, official docs, schema/source code, standards, and peer-reviewed or arXiv research before third-party commentary. Mark each finding as observed, official, standard, research, inferred, or input gap.
  4. Check crawlability and renderability: status code, robots, sitemap, canonical, meta robots, mobile-first parity, JavaScript dependency, and whether primary content appears in initial HTML.
  5. Check structural quality: H1-H3, main/article, summary, table of contents, FAQ, tables, lists, breadcrumbs, internal links, anchor text, accessibility headings, and schema.
  6. Check content evidence: conclusions first, full entity names, data, citations, cases, dates, author/source, freshness, objectivity, price, service boundaries, regional constraints, and source accountability.
  7. Check AI extractability and public-answer material coverage: key-value facts, atomic facts, comparison tables, steps, Q&A, context-independent summary, paragraph independence, entity graph, sameAs links, and chunk-level citation readiness. Convert domestic platform concerns into high-intent question material gaps, not platform recall claims.
  8. Produce code-layer fixes, content-layer fixes, page-module suggestions, schema/HTML snippets, priority, owner, acceptance test, risk, and estimated cost.
  9. Deliver Word, PDF, sticky-menu HTML, and Markdown from one Markdown content source. Use the kami editorial report style in references/report-formatting-spec.md and references/output-layout-policy.md.
  10. After DOCX generation, run scripts/polish_docx.py to apply Kami-style Word typography, margins, and table formatting.
  11. Run scripts/review_report_layout.py and references/quality-gates.md before claiming completion.

Boundaries

  • Without crawl/log access, only report front-end observable evidence. Do not infer log-level crawl frequency.
  • Without AI-platform sampling data, do not analyze platform recall, ranking, answer frequency, citation share, or platform-internal weighting. Replace that section with public material coverage and high-intent question readiness.
  • Distinguish user-visible content, crawler-readable content, and AI-extractable content.
  • Schema must match page body facts. Do not use schema to invent facts absent from the page.
  • Cite or name the evidence source for important claims. If the source is not available, label the recommendation as a hypothesis or input gap.
  • Domestic answer-material adaptation should consider search results, public webpages, news pages, encyclopedia pages, WeChat public articles, documentation, and help-center pages as possible public material. Treat actual platform answer performance as optional user-supplied evidence.

Outputs

  • Page GEO diagnosis report.
  • Code-layer repair checklist.
  • Content-structure remodeling advice.
  • Schema and HTML module suggestions.
  • Evidence ledger and report completeness self-check.
  • Default four-piece deliverable: Word, PDF, HTML package, Markdown.
  • quality-report.json with artifact existence, byte size, and layout checks.

Keep looking

Skills are one crate of 326,851. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.