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schema-coverage-auditor

按 URL 或站点抓取页面并解析 JSON-LD 与微数据，判定页面内容类型，比对推荐 schema.org 类型及必需/建议属性，标记 SCHEMA_MISSING、REQUIRED_PROPERTY_MISSING、INVALID_VALUE 等问题，评估富媒体结果资格并给出覆盖率评分，结果按输出契约以 JSON 返回。

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最近更新 2026-10-18 21:43:21

安装

发给 Agent
帮我安装这个技能：npx secure-skills-cli add seoskills.sh/schema-coverage-auditor
兼容性
通用兼容
联网要求
需要联网
数据出网
数据不出本机
Metadata
name
schema-coverage-auditor
description
Classifies each page's content type and compares the structured data it should carry against what is present and valid, flagging missing, incomplete, or invalid schema and the exact properties to add. Use when the user wants a structured-data coverage audit, rich-result eligibility check, or to find schema gaps across a site.
metadata
{"title":"On-Page Schema Coverage Auditor","category":"on-page-seo"}

On-Page Schema Coverage Auditor

AGENT ROLE: Autonomous structured-data agent. Determine the schema each page SHOULD have, compare to what it HAS, validate required properties, and emit the JSON in references/output.schema.json. Audit coverage; do not generate the markup.

OBJECTIVE

For each page, detect its content type, look up the recommended schema.org types and required properties in references/content_type_schema.json, and report exactly what structured data is missing, incomplete, or invalid — with rich-result eligibility impact.

INPUTS

  • urls (REQUIRED string[]) OR sitemap_url.
  • content_type_hints (OPTIONAL): map url → type to override auto-classification.
  • max_pages (OPTIONAL, default 2000).

AUTHENTICATION / RUNTIME

  • No API key. Keyless HTTPS GET, UA seoskills-schema-audit/1.0, honor robots.

EXPECTED TOOL CALLS

  • Run scripts/schema_coverage.py --urls a,b,c.
  • Per URL: GET; parse all JSON-LD blocks and microdata; classify content type from on-page signals.

PROCEDURE (deterministic, per page)

STEP 1 — CLASSIFY content type (article, product, faq, howto, localbusiness, recipe, event, breadcrumb-eligible, organization) from signals: URL pattern, og:type, presence of price/rating (product), Q&A structure (faq), step lists (howto), address/geo (local). Use content_type_hints when provided. STEP 2 — RECOMMENDED: from content_type_schema.json, get the recommended @type(s) and their required + recommended properties for the classified type. STEP 3 — PRESENT: parse existing JSON-LD/microdata; map to schema types found. STEP 4 — DIFF + VALIDATE:

  • SCHEMA_MISSING: a recommended type is entirely absent.
  • REQUIRED_PROPERTY_MISSING: present type lacks a required property (e.g., Product without offers/name).
  • INVALID_VALUE: property present but malformed (e.g., priceCurrency not ISO-4217, aggregateRating.ratingValue out of range).
  • RECOMMENDED_PROPERTY_MISSING: info-level. STEP 5 — ELIGIBILITY: map missing/invalid to lost rich-result types (review star, FAQ accordion, breadcrumb, sitelinks). STEP 6 — SCORE coverage_score 0–100; EMIT per page with the exact properties to add.

RATE LIMITS & ERROR HANDLING

  • Crawl: ≤ 5 concurrent, ≥ 150ms per host, timeout 12s. Per-URL failure → {url, status:"unreachable"}.
  • Malformed JSON-LD → capture as INVALID_JSONLD (a finding), do not crash the page.

MISSING / INSUFFICIENT DATA

  • Auto-classification can be wrong for ambiguous pages — always return detected_type + classification_confidence; low confidence → recommend Organization/WebPage only and note it.
  • Never invent property values; report what is missing, not what to set them to (that is content, not audit).
  • A page legitimately needing no special schema (e.g., a contact page) is pass with recommended_types: [].

OUTPUT

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

FILES

  • scripts/schema_coverage.py — content-type classification, JSON-LD/microdata parse, diff + validation.
  • references/content_type_schema.json — recommended types + required/recommended properties + validators.
  • references/output.schema.json — output contract.