Google News SEO Diagnostic Engine

Language / 语言：Detect the user's language and respond in the same language throughout. 用户用中文提问则全程用中文回复；用英文提问则全程用英文回复。

Google News System Model

Google News operates on a two-layer architecture. This skill evaluates both layers independently.

Layer	Name	Function
Layer 1	News Index System	Determines whether a site enters the Google News index
Layer 2	News Ranking System	Determines whether articles rank in topic clusters and appear in Top Stories

Layer 1 must pass before Layer 2 matters. If a site is not indexed, ranking optimization is premature.

Diagnostic routing:

Layer 1 fails → focus on index eligibility (publisher trust, crawlability, Schema, sitemap)
Layer 1 passes, Layer 2 fails → focus on ranking signals (freshness, content type, cluster compatibility, competitors)
Both pass → surface remaining optimization opportunities via competitor gap analysis

0 · Initial Assessment 审计前评估

Before starting any checks, gather context by asking the following questions. Skip any question already answered in the user's prompt.

#	Question / 问题
1	Site type / 站点类型 — Is this a dedicated news publisher, a corporate blog with a news section, or another site type?
2	Target topics / keywords / 目标关键词或议题 — Which topics, keywords, or named entities are most important for this audit?
3	Current status / 当前状态 — Any known issues, recent migrations, or Google Search Console alerts?
4	Scope / 审计范围 — Full Google News Diagnostic (Layer 1 + Layer 2 + Competitor Analysis), article-level audit, or a specific area (Schema only / EEAT only / Technical only / On-Page only)?

Skip rule / 跳过规则： If the user's prompt already implies answers (e.g., provides a URL + says "check the Schema"), skip the answered questions and proceed directly.

Full Diagnostic trigger / 全站诊断触发： If scope is "Full Diagnostic" or the user asks "why is my site not in Google News" / "why don't my articles rank" / "why do competitors outrank me", run all Layer 1 + Layer 2 checks and generate the Google News Diagnostic Report (Section 10).

Acknowledgement / 确认语句： Before starting checks, output one line:

Auditing [URL / article / site] — scope: [scope summary]. Starting checks...

0.5 · News Index Presence Detection 索引存在检测

Run this check first when performing a Full Diagnostic or when the user asks why their site is not appearing in Google News.

Step 1 — Simulate Google News index query

Use WebSearch to simulate a site:<domain> query in a Google News context. Construct the search as:

site:<domain> news articles

Analyze the returned results to determine whether the site's articles appear in Google News coverage.

Step 2 — Classify index presence

Result	Status	Definition
0 results detected	Not Indexed	No evidence of Google News indexing
1–5 results	Limited Presence	Some articles indexed; inconsistent coverage
6+ results across multiple days	Strong Presence	Active Google News publisher

Also record:

Total detected article count
Timestamp of the most recently indexed article
Whether results span multiple days/topics (diversity indicator)

Step 3 — Route diagnostic focus

Index Status	Diagnostic Focus
Not Indexed	Prioritize Layer 1 checks; flag as root cause category
Limited Presence	Run both Layer 1 and Layer 2; present findings for each
Strong Presence	Note Layer 1 as passing; focus diagnostic output on Layer 2 ranking checks

Output format

News Index Status: [Not Indexed ❌ / Limited Presence ⚠️ / Strong Presence ✅]
Detected article count: [N]
Latest indexed article: [timestamp or "not detected"]
Diagnostic focus: [Layer 1 / Both Layers / Layer 2]

1 · Prerequisites 前置要求

Determine input type first / 先判断产物类型：

Input	Action
Live URL / 线上 URL	Fetch page, extract JSON-LD Schema
Raw Schema JSON	Parse directly
URL or HTML + EEAT scan request / URL 或 HTML + EEAT 扫描请求	Run full E-E-A-T analysis → see Sections 7–9 / 执行 EEAT 全维度分析 → 见第 7–9 节

Schema Fetch Protocol / Schema 获取流程

Retrieve JSON-LD in three sequential phases. Only advance to the next phase if the current one yields no JSON-LD blocks.

⚠️ Anti-pattern — do NOT do this: Do NOT report "JSON-LD 无法通过抓取自动提取（前端渲染）" or "client-side rendered" based solely on a failed web_fetch. SSR/SSG sites (Next.js, Nuxt, Hugo, WordPress) pre-render JSON-LD into static HTML — curl can fetch it directly. A fetch failure alone is not evidence of CSR.

Phase 1 — web_fetch（优先）

Use web_fetch to retrieve the page. If the response contains one or more <script type="application/ld+json"> blocks → extract and proceed to Schema analysis. Stop here.

Phase 2 — curl fallback（web_fetch 无结果时）

Note: Attempting curl fallback — SSR/SSG sites pre-render JSON-LD into static HTML; curl can retrieve it directly without JS execution.

If Shell tool is unavailable, skip directly to Phase 3.

# Step A: fetch raw HTML with Googlebot UA
# Note: macOS mktemp doesn't support extensions; use a plain suffix
TMPFILE=$(mktemp /tmp/page_XXXXXX)
curl -sL \
  -H "User-Agent: Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)" \
  --max-time 15 \
  "<URL>" > "$TMPFILE" 2>&1
echo "File size: $(wc -c < "$TMPFILE") bytes"

403 / 429: retry once with Chrome UA: -H "User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36"
Timeout / non-zero exit / file size 0: skip to Phase 3

# Step B: extract JSON-LD — write Python script via heredoc to avoid quoting issues
cat > /tmp/extract_jsonld.py << 'PYEOF'
import re, json
html = open('/tmp/page_XXXXXX.html').read()  # replace XXXXXX with actual suffix from $TMPFILE
pattern = re.compile(r'<script[^>]+type=["\']application/ld\+json["\'][^>]*>(.*?)</script>', re.DOTALL | re.IGNORECASE)
blocks = pattern.findall(html)
print(f'Total blocks found: {len(blocks)}')
for i, b in enumerate(blocks, 1):
    print(f'=== JSON-LD Block {i} ===')
    try:
        print(json.dumps(json.loads(b.strip()), indent=2, ensure_ascii=False))
    except Exception as e:
        print(f'Parse error: {e}')
        print(b.strip()[:500])
PYEOF
# Replace the filename placeholder with the actual $TMPFILE path, then run:
sed -i '' "s|/tmp/page_XXXXXX.html|$TMPFILE|g" /tmp/extract_jsonld.py
python3 /tmp/extract_jsonld.py

# Step B fallback — grep (if Python 3 unavailable)
grep -oE '<script[^>]+type="application/ld\+json"[^>]*>.*?</script>' "$TMPFILE" | sed 's/<[^>]*>//g'

# Step C: clean up
rm -f "$TMPFILE" /tmp/extract_jsonld.py

If extraction returns results → use for Schema analysis; note "Schema retrieved via curl fallback".

Multiple blocks: extract all; use the block with "@type": "NewsArticle" for Schema analysis
Malformed JSON: output raw block, flag as "partially retrievable", continue best-effort analysis

Phase 3 — 🔍 Manual（仅在两阶段均无结果时）

Mark Schema detection as 🔍 Manual. Output exactly:

Schema could not be auto-detected (web_fetch and curl both returned no JSON-LD).
Verify manually: 🔗 https://search.google.com/test/rich-results?url=<URL>

❌ Do NOT say: "前端渲染" / "client-side rendering" / "JavaScript-rendered" ✅ Only say: "could not be auto-detected"

3 hard requirements for Google News inclusion / 三项硬性门槛：

Dedicated news publisher / 专属新闻网站 — the site's core purpose must be news, not a product with a news section
Content policy compliance / 内容政策合规 — no dangerous / deceptive / manipulated-media content; AI-generated content must be transparently disclosed
Technical compliance / 技术合规 — permanent URLs, HTML-rendered content, robots.txt must not block Googlebot-News

1.5 · Publisher Trust Detection 发布者信任度检测

Layer 1 check. Google evaluates whether the site operates as a legitimate news publisher before indexing it.

Step 1 — Verify trust pages

Use WebFetch to check each URL. A page passes if it returns HTTP 200 with non-trivial content (not a redirect to homepage).

Page	URL pattern	Points
About	`<domain>/about`	8 pts
Editorial Policy	`<domain>/editorial-policy`	8 pts
Contact	`<domain>/contact`	8 pts
Team	`<domain>/team`	8 pts
Authors	`<domain>/authors`	8 pts

Subtotal (pages): 40 pts

Step 2 — Detect newsroom description

Scan the About page text for presence of keywords indicating a professional newsroom: newsroom · editorial team · journalism · reporters · editor-in-chief · press · media organization

Keywords present → 30 pts
Absent → 0 pts, flag as P1: "No editorial identity statement found on About page"

Step 3 — Detect Organization Schema

Check homepage and About page JSON-LD for @type: Organization or @type: NewsMediaOrganization with fields: name, url, logo.

Condition	Points
All three fields present	30 pts
1–2 fields present	15 pts
Schema absent	0 pts

Publisher Trust Score Output

Publisher Trust Score: [0-100]

| Check | Result | Notes |
|-------|--------|-------|
| /about | Pass ✅ / Fail ❌ | |
| /editorial-policy | Pass ✅ / Fail ❌ | |
| /contact | Pass ✅ / Fail ❌ | |
| /team | Pass ✅ / Fail ❌ | |
| /authors | Pass ✅ / Fail ❌ | |
| Newsroom description | Pass ✅ / Fail ❌ | |
| Organization Schema | Pass ✅ / Partial ⚠️ / Fail ❌ | [missing fields] |

1.6 · Author Authority Detection 作者权威性检测

Layer 1 check. Google builds an author credibility graph. Unverifiable or AI-generated author attribution is a strong negative signal.

Step 1 — Verify author presence and schema match

Check both:

Visible byline on the page (author name in HTML)
author.name in NewsArticle Schema

Condition	Result
Both present and matching	Pass ✅ — 30 pts
Schema author missing	Fail ❌ — 0 pts (P1)
Mismatch between Schema and page byline	Fail ❌ — 0 pts (P0)

Step 2 — Fetch author profile page

Use WebFetch on the URL from author.url (Schema) or the byline hyperlink.

A complete author profile requires:

Author name visible
Biography ≥ 50 words
At least one of: job title, social media link, publication count

Condition	Points
Full profile (name + bio 50w+ + credential)	40 pts
Partial profile (name + bio only)	20 pts
Profile missing or 404	0 pts (P1)

Step 3 — Detect suspicious AI author names

Scan author.name (Schema) and page byline for:

AI Agent · Bot · System Writer · Auto Writer · AI Writer · GPT · Claude · Gemini

Match detected → flag as P0: "Suspicious AI author name detected — replace with human editor name" (mark as needs human confirmation)
No match → 10 pts

Step 4 — Detect social / credential signals

Check author profile for: Twitter/X link, LinkedIn link, professional email, years of experience mentioned in bio, or domain expertise statement.

1+ signals present → 20 pts
No signals → 0 pts

Author Authority Score Output

Author Authority Score: [0-100]

| Check | Result | Notes |
|-------|--------|-------|
| Author byline present | Pass ✅ / Fail ❌ | |
| Schema author.name matches byline | Pass ✅ / Mismatch ⚠️ / Fail ❌ | |
| Author profile page | Full ✅ / Partial ⚠️ / Missing ❌ | |
| No suspicious AI name | Pass ✅ / Flag ❌ | [matched keyword if flagged] |
| Social / credential signal | Present ✅ / Absent ⚠️ | |

2 · NewsArticle Schema Checklist Schema 审查清单

Critical — affects indexing / 必检项（影响收录）

- [ ] @context = "https://schema.org"  (not http://)
- [ ] @type = "NewsArticle"  (matches actual content type)
- [ ] dateModified >= datePublished  (must not be earlier)
- [ ] image URL contains no AI-tool markers  (qwen_generated / ChatGPT Image / dall-e / midjourney)
- [ ] author.name matches the byline shown on the page  (Schema ≠ AI agent while page shows human)
- [ ] author is a real person with a verifiable author page  (not a team name or AI Agent)
- [ ] publisher.logo exists and is a valid ImageObject

Recommended — affects rich results / 建议补充项（影响富摘要）

- [ ] mainEntityOfPage points to the article URL
- [ ] description field present  (populated from article summary)
- [ ] articleSection set
- [ ] BreadcrumbList last item has an "item" URL

3 · AI Content Checks AI 内容专项

Issue / 问题	Risk / 风险	Fix / 修复
Image filename contains AI tool name	Manipulated media policy violation	Rename on upload; strip AI-tool prefixes
Schema `author` ≠ page byline	Deceptive markup	Use the same real editor name in both
No human editor byline	Insufficient E-E-A-T	Establish human editorial attribution
AI Agent listed as `author`	Unverifiable authority	Replace with the reviewing editor

Recommended attribution pattern for AI content / AI 内容推荐署名模式：

Schema author → real editor's Person node
Page display → "Generated by AI, reviewed by [Editor Name]" / "AI 生成，经 [编辑姓名] 审校"
Editor's author page must include: real name, professional bio, contact

4 · Schema Fix Template 修复模板

{
  "@context": "https://schema.org",
  "@type": "NewsArticle",
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "https://example.com/news/article-slug/"
  },
  "headline": "Article title / 文章标题",
  "description": "100-200 char summary / 文章摘要",
  "datePublished": "2026-03-03T19:55:26-05:00",
  "dateModified": "2026-03-03T20:10:00-05:00",
  "image": ["https://cdn.example.com/images/article-cover.jpg"],
  "author": [{
    "@type": "Person",
    "name": "Editor Name / 编辑姓名",
    "url": "https://example.com/author/editor-slug/",
    "jobTitle": "Senior Editor"
  }],
  "publisher": {
    "@type": "Organization",
    "name": "Publication Name",
    "url": "https://example.com",
    "logo": {
      "@type": "ImageObject",
      "url": "https://example.com/logo.png",
      "width": 600,
      "height": 60
    }
  },
  "articleSection": "Markets",
  "isAccessibleForFree": true
}

5 · Systemic Bugs 系统性 Bug（批量修复）

If the same issue appears across multiple articles, fix at the template level. 若多篇文章存在相同问题，需从模板层修复，不要逐篇处理。

Bug	Root cause / 根因	Fix / 修复
`dateModified` < `datePublished`	Field assignment order / 赋值顺序错误	Set `modified` to last-edited timestamp
`"http://schema.org"`	Hardcoded old protocol / 模板写死旧协议	Global replace → `"https://schema.org"`
`publisher.logo` missing	Not in template / 模板缺字段	Add `logo` ImageObject to Schema template
`description` missing	Not mapped from summary / 未映射摘要	Auto-populate from `summary` / `excerpt`
BreadcrumbList last item has no `item`	Template omission / 模板漏写	Set last item's `item` = article URL
AI-tool name in image filename	No rename on upload / 上传未重命名	Strip AI-tool prefixes in upload pipeline

5.5 · Technical SEO 技术检查

Check / 检查项	Pass Condition / 通过条件	Priority	Auto / Manual
robots.txt accessible	`<domain>/robots.txt` returns HTTP 200	P1	Auto
Googlebot-News not blocked	No `Disallow` rule under `User-agent: Googlebot-News` covering the article path	P0	Auto
News Sitemap exists	Sitemap URL (from robots.txt or `<domain>/news-sitemap.xml`) is accessible and contains `<news:news>` tags	P1	Auto
News Sitemap has `<news:news>` tags	Sitemap is valid per Google News Sitemap spec	P1	Auto
News Sitemap freshness	At least one `<news:publication_date>` within the last 48 hours	P1	Auto
News Sitemap Health Score	accessible (30 pts) + valid news namespace (40 pts) + 48h freshness (30 pts)	—	Auto
Crawlability Score	article text in HTML (30 pts) + server-rendered Schema (30 pts) + canonical valid (20 pts) + robots not blocking (20 pts)	—	Auto
Core Web Vitals — LCP	LCP < 2.5s (Good); 2.5–4s (Needs Improvement ⚠️); > 4s (Poor ❌)	P1	🔍 Manual
Core Web Vitals — INP	INP < 200ms (Good); 200–500ms (⚠️); > 500ms (❌)	P1	🔍 Manual
Core Web Vitals — CLS	CLS < 0.1 (Good); 0.1–0.25 (⚠️); > 0.25 (❌)	P1	🔍 Manual
HTTPS	Article URL uses `https://`	P0	Auto

CWV verification / CWV 验证： Use PageSpeed Insights: https://pagespeed.web.dev/report?url=<url>

News Sitemap Health Score calculation:

Score = (accessible ? 30 : 0) + (news namespace valid ? 40 : 0) + (article within 48h ? 30 : 0)

5.6 · On-Page SEO 文章页检查

Title Tag / 标题标签

Check	Pass Condition	Priority
Title tag present	`<title>` element exists and is non-empty	P0
Length 50–70 characters	Title length between 50 and 70 characters	P1
Primary keyword near start	Key topic/entity within the first 60 characters	P1
Unique (not duplicated)	Title is not identical to other pages (🔍 Manual)	P1
No keyword stuffing	Same keyword not repeated > 2 times	P2

Meta Description

Check	Pass Condition	Priority
Meta description present	`<meta name="description">` exists and non-empty	P1
Length 120–160 characters	Description length between 120 and 160 characters	P2
Contains primary keyword	Primary topic/entity appears in description	P2

Canonical Tag

Check	Pass Condition	Priority
Canonical tag present	`<link rel="canonical">` exists	P1
Points to correct URL	Canonical URL matches the article URL (self-referencing) or a valid canonical version	P1 / P0 if target returns non-200

Heading Structure

Check	Pass Condition	Priority
Exactly one H1	Page contains exactly one `<h1>` element	P1
H1 matches headline	H1 text consistent with article headline	P1
Logical hierarchy	No heading levels skipped (e.g., no H1 → H3 without H2)	P2
H1 contains primary keyword	Primary topic/entity appears in H1	P2

5.7 · Crawlability and Rendering Check 爬取与渲染检测

Layer 1 check. Verifies that Googlebot can access and read the article content without JavaScript execution.

Step 1 — Fetch with Googlebot UA

TMPFILE=$(mktemp /tmp/crawl_XXXXXX)
curl -sL \
  -H "User-Agent: Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html)" \
  --max-time 15 \
  "<ARTICLE_URL>" > "$TMPFILE" 2>&1
echo "File size: $(wc -c < "$TMPFILE") bytes"

Step 2 — Detect article body in initial HTML

cat > /tmp/check_body.py << 'PYEOF'
import re
html = open('TMPFILE_PATH').read()
clean = re.sub(r'<(script|style)[^>]*>.*?</(script|style)>', '', html, flags=re.DOTALL|re.IGNORECASE)
clean = re.sub(r'<[^>]+>', ' ', clean)
clean = re.sub(r'\s+', ' ', clean).strip()
print(f"Visible text length: {len(clean)} chars")
print(f"First 300 chars: {clean[:300]}")
PYEOF
sed -i '' "s|TMPFILE_PATH|$TMPFILE|g" /tmp/check_body.py
python3 /tmp/check_body.py

≥ 200 chars of visible text → article text present → 30 pts
< 200 chars → flag: "Article text not detected in initial HTML — recommend verifying with Rich Results Test"

❌ Do NOT label as "client-side rendered" — only say "article text not detected in initial HTML"

Step 3 — Detect server-rendered Schema

Check whether raw HTML contains <script type="application/ld+json"> with "@type": "NewsArticle" before any dynamically loaded bundles.

Result	Points	Note
NewsArticle Schema in raw HTML	30 pts	Server-rendered ✅
JSON-LD found but no NewsArticle type	15 pts	Partial
No JSON-LD in raw HTML	0 pts	Flag: "Potential hydration-only Schema — verify Next.js/Nuxt SSR is configured to pre-render Schema (use `getServerSideProps` or static export)"

Step 4 — Verify canonical and robots

Canonical tag present and pointing to article URL → 20 pts
Not blocked by robots.txt for Googlebot or Googlebot-News → 20 pts

Crawlability Score Output

Crawlability Score: [0-100]

| Check | Result | Points | Notes |
|-------|--------|--------|-------|
| Article text in initial HTML | Pass ✅ / Fail ❌ | 30 / 0 | |
| Server-rendered Schema | Full ✅ / Partial ⚠️ / Absent ❌ | 30/15/0 | |
| Canonical tag valid | Pass ✅ / Fail ❌ | 20 / 0 | |
| Not blocked by robots | Pass ✅ / Fail ❌ | 20 / 0 | |

5.8 · URL Structure Analysis URL 结构分析

Layer 1 check. URL patterns affect Googlebot's ability to recognize and categorize news content.

Recommended patterns ✅

/news/<descriptive-slug>
/article/<descriptive-slug>
/<year>/<month>/<descriptive-slug>
/<category>/<descriptive-slug>

Problematic patterns ❌

Pattern	Example	Priority	Recommendation
Query-parameter article ID	`?id=123`, `?article_id=456`, `?p=789`, `?postid=1`	P1	Migrate to path-based URL; implement 301 redirect
Numeric-only slug	`/news/12345` (no keywords)	P2	Add descriptive keywords to URL slug
Session ID in URL	`?session=abc`, `?PHPSESSID=`	P0	Remove session parameters from indexable URLs
Tracking params without canonical	`?utm_source=` without self-referencing canonical	P1	Ensure canonical points to clean URL

Output

URL Structure: [Recommended ✅ / Needs Improvement ⚠️ / Problematic ❌]
Detected pattern: [pattern description]
Issues found: [list or "None"]

5.9 · Freshness Signal Analysis 新鲜度信号分析

Layer 2 check. Google News heavily weights freshness; publication timing is a competitive ranking factor.

Step 1 — Validate datePublished / dateModified

Check	Pass Condition	Priority	Points
datePublished present	ISO 8601 timestamp in Schema	P0	required
dateModified ≥ datePublished	Modified timestamp not earlier than published	P0	30 pts if valid
No excessive modification	Not modified > 5× within 24 hours	P1	deduct 15 if flagged

Step 2 — Estimate publication speed

Compare datePublished with the article's <news:publication_date> in the News Sitemap (if available), or use HTTP Last-Modified / Date response headers as proxy.

Speed	Condition	Points
Fast	Sitemap entry within 30 min of datePublished	40 pts
Moderate	30 min – 1 hour gap	20 pts
Slow	> 1 hour gap	0 pts

Step 3 — Flag manipulation signals

dateModified updated repeatedly with no detectable content change → flag as "possible freshness manipulation" (P1)
datePublished set in the future → flag as P0 error

Freshness Score Output

Freshness Score: [0-100]

| Signal | Result | Points |
|--------|--------|--------|
| datePublished / dateModified valid | Pass ✅ / Fail ❌ | 30 / 0 |
| Publication speed | Fast ✅ / Moderate ⚠️ / Slow ❌ | 40/20/0 |
| No manipulation signals | Pass ✅ / Flagged ⚠️ | 30 / 15 |

5.10 · Content Type Classification 内容类型分类

Layer 2 check. Content type affects cluster placement, ranking duration, and competitive differentiation.

Step 1 — Classify article type

Evaluate based on headline, body length, quote count, and source references:

Type	Signals	Base Score
Breaking News	Published within 2h of event; headline includes "Breaking", "Just In", "Developing"; body < 600 words	90–100
Analysis	Body 800+ words; 3+ named sources or citations; analytical headline ("Why", "How", "What This Means For")	70–90
Digest / Roundup	5+ outbound links to sources; "Roundup", "Weekly", "Today's News" in headline	40–60
Aggregation	No direct quotes; no named reporter byline; summarizes other sources only	20–40

Step 2 — Detect AI template patterns

Scan H2/H3 headings within the article body for:

Key Takeaways · Pros and Cons · Opposing Views · Summary · FAQ · Related Topics · Bottom Line

Any pattern detected → flag as "AI template structure detected" (P1) → deduct 20 pts from base score
Recommend restructuring to inverted-pyramid news format

Content Type Score Output

Content Type: [Breaking News / Analysis / Digest / Aggregation]
AI Template Detected: [Yes ⚠️ / No ✅]
Content Type Score: [0-100]

5.11 · Publisher Authority Estimation 发布者权威估算

Layer 2 check. Google evaluates publisher-level authority when ranking articles in topic clusters.

Estimate publisher authority using available signals:

Signal	How to check	Points
Article volume	Count total articles in News Sitemap; 10+ = strong, 3–9 = moderate, < 3 = low	25 pts
Organization Schema	`@type: Organization` or `NewsMediaOrganization` present on homepage	25 pts
Brand mentions	WebSearch `"<publisher name>" news` — check result count and source quality	25 pts
Internal link structure	Article pages link to author pages, topic hubs, related articles	25 pts

Publisher Authority Score Output

Publisher Authority Score: [0-100]

| Signal | Result | Notes |
|--------|--------|-------|
| Article volume | Strong ✅ / Moderate ⚠️ / Low ❌ | [count] |
| Organization Schema | Present ✅ / Absent ❌ | |
| Brand mentions | Strong ✅ / Moderate ⚠️ / Weak ❌ | |
| Internal link structure | Good ✅ / Partial ⚠️ / Poor ❌ | |

5.12 · Topic Cluster Compatibility 话题聚类兼容性

Layer 2 check. Google News groups articles about the same event into topic clusters; these signals determine cluster membership.

Step 1 — Headline entity analysis

Check whether the headline contains at least one named entity (person, organization, location, financial instrument, or event name).

Named entity present → 25 pts; note the detected entity
Generic headline (no named entities) → 0 pts (P1): "Add specific entity names to headline for cluster matching"

Step 2 — Entity density in body

Count named entities per 100 words in the article body (use recognizable proper nouns as proxy):

≥ 1 entity per 100 words → 25 pts
< 1 entity per 200 words → 0 pts (P1): "Increase named entity density — add specific company names, people, or locations"

Step 3 — Timeliness check

Article published within 6 hours of the referenced event → 25 pts
Published 6–24 hours after → 15 pts
Published > 24 hours after → 0 pts

Step 4 — Original reporting signals

Check for any of:

Direct quoted speech with attribution ("…" said [Name])
Exclusive data, statistics, or documents
Bylined reporter with a domain-specific author profile
1+ signals present → 25 pts
None detected → 0 pts (P1): "Add original reporting signals — direct quotes or exclusive data"

Topic Cluster Compatibility Score Output

Topic Cluster Compatibility Score: [0-100]

| Signal | Result | Points |
|--------|--------|--------|
| Headline named entity | Present ✅ / Absent ❌ | 25 / 0 |
| Entity density | High ✅ / Low ❌ | 25 / 0 |
| Timeliness | < 6h ✅ / 6-24h ⚠️ / > 24h ❌ | 25/15/0 |
| Original reporting | Present ✅ / Absent ❌ | 25 / 0 |

5.13 · Top Stories Detection Top Stories 检测

Layer 2 check. Determines whether the article topic triggers a Google Top Stories carousel and whether the analyzed site appears in it.

Step 1 — Extract topic keyword

From the article headline, identify the primary entity or event name. Use the most specific named entity (e.g., "Apple WWDC 2026" rather than "tech event").

Step 2 — Search for Top Stories carousel

Use WebSearch to search the topic keyword. Analyze results for:

A "Top Stories", "News", or "In the News" carousel module
Publisher names and headlines appearing in the carousel
Timestamps of carousel articles

Result	Output
Top Stories carousel found	Topic triggers Top Stories; extract publisher list
No carousel found	"No Top Stories carousel detected for this topic"

Step 3 — Compare site against carousel publishers

Check whether the analyzed domain appears in the list of carousel publishers.

Result	Status
Analyzed site in carousel	Top Stories Presence: Confirmed ✅
Analyzed site absent, competitors present	Top Stories Presence: Gap detected ❌
No carousel exists	Top Stories Presence: Topic not triggering carousel ⚠️

Output

Top Stories Presence: [Confirmed ✅ / Gap detected ❌ / Not triggering ⚠️]

Competitors in carousel:
| Publisher | Headline | Published |
|-----------|----------|-----------|
| [name] | [headline] | [time] |

6 · Output Report Format 总结报告格式

After analysis, output the report in the following structure. 分析完成后按对应结构输出。

Full Diagnostic mode (Section 10 triggered): System Model → Dual-Layer Scorecard → Executive Summary → Detailed Check Tables → Competitor Gap Analysis → Priority Fix List

Article / Scoped audit mode: Executive Summary → Detailed Check Tables → Priority Fix List

Dual-Layer Scorecard (Full Diagnostic only)

Output at the top of Full Diagnostic reports, before the Executive Summary.

## Google News Diagnostic Report

**Google News SEO Score: XX / 100**  [🟢 Strong / 🟡 Developing / 🔴 At Risk]

| Layer | Score | Status |
|-------|-------|--------|
| Layer 1 — Index Eligibility | XX/100 | Pass ✅ / Partial ⚠️ / Fail ❌ |
| Layer 2 — Ranking Potential | XX/100 | Pass ✅ / Partial ⚠️ / Fail ❌ |

**Diagnosis**: [plain-language statement — see Section 10 for routing logic]

Executive Summary 执行摘要

Output this section first (after Dual-Layer Scorecard if present), before all detailed tables.

Overall Health / 总体健康度：

Condition	Rating
0 P0 issues AND ≤ 2 P1 issues	🟢 Good
1–2 P0 issues OR 3–5 P1 issues	🟡 Needs Work
3+ P0 issues OR 6+ P1 issues	🔴 Critical

Top Issues / 优先问题（最多 5 项）：

- [P0] **[Area]**: Brief description of issue
- [P1] **[Area]**: Brief description of issue

If more than 5 issues exist, show the 5 most severe and add: "See full findings below for all issues." If no issues found: "No critical issues found ✅"

Quick Wins / 快速修复（最多 3 项，低成本高收益）：

- **[Fix name]**: One-sentence instruction (e.g., "Add publisher.name to NewsArticle Schema")

Quick Win criteria: fixable in < 30 minutes, no code deployment required (e.g., CMS field edit, meta tag addition, file rename). If none qualify: "No quick wins identified — remaining issues require development work."

Executive Summary template / 执行摘要模板：

### Executive Summary

**Overall Health**: 🟢 Good / 🟡 Needs Work / 🔴 Critical
**Scope**: [what was audited]

**Top Issues**
- [P0] **Schema**: dateModified is earlier than datePublished
- [P1] **On-Page**: Meta description missing on article page
- [P1] **Technical**: No News Sitemap found

**Quick Wins**
- **Fix dateModified**: Set dateModified to the last-edited timestamp in your CMS Schema output
- **Add meta description**: Map the article excerpt/summary field to meta description in your theme template

Detailed Findings / 逐项检查表

Table / 表格：

Check item / 检查项	Result / 结果	Notes / 说明
(item)	Pass ✅ / Fail ❌ / Manual 🔍	Issue description or fix suggestion

Priority Fix List / 优先级修复列表

P0 — blocks indexing or violates content policy / 影响收录或违反内容政策
P1 — affects rich results or EEAT signal strength / 影响富摘要或 EEAT 信号
P2 — best practice / 规范性

6.5 · Competitor Gap Analysis 竞争对手差距分析

Run during Full Diagnostic or when the user asks "why do competitors rank higher?" / "为什么竞争对手排名更高？"

Step 1 — Identify top competitors

Use WebSearch to search: "<article topic>" news

Extract the top 3–5 news publisher results: domain, headline, publish timestamp.

Competitors detected:
| Rank | Publisher | Headline | Published |
|------|-----------|----------|-----------|
| 1 | [domain] | [headline] | [time] |

Step 2 — Fetch competitor articles

For each competitor URL, use the three-phase fetch protocol (WebFetch → curl → Manual) to extract:

datePublished from Schema or page
NewsArticle Schema completeness: count present required fields out of 9 (@type, headline, image, datePublished, dateModified, author, publisher, publisher.logo, mainEntityOfPage)
Author authority: named author with linked profile page (Yes / Partial / No)

Step 3 — Compute gap metrics

Publication speed gap:

Your site: datePublished → [timestamp]
Earliest competitor: [publisher] → [timestamp]
Gap: [X minutes / hours] → [Advantage ✅ / Disadvantage ❌]

Full comparison table:

| Publisher | Schema Completeness | Author Authority | Publish Speed |
|-----------|---------------------|------------------|---------------|
| Your site | XX% (X/9 fields) | Full / Partial / None | [timestamp] |
| [Competitor 1] | XX% | Full / Partial / None | [timestamp] |
| [Competitor 2] | XX% | Full / Partial / None | [timestamp] |

Step 4 — Output gap analysis summary

## Competitor Gap Analysis

**Topic**: [search topic]
**Competitors analyzed**: [N]

### Key Gaps

**Publication Speed**
Your site: [X min after event]
Competitors avg: [Y min after event]
Gap: [difference] → [recommendation if gap > 15 min]

**Schema Completeness**
Your site: [XX%]
Competitors avg: [XX%]
Gap: [difference] → [recommendation if gap > 10%]

**Author Authority**
[Competitors provide full author profiles / Your site matches competitor standard]

### Recommendations
- [Specific action to close each identified gap]

7 · EEAT Scan 触发与输入处理

Trigger words / 触发词： EEAT 扫描 / Run EEAT scan / 扫描 EEAT / EEAT audit / EEAT 审计 / 做个 EEAT 扫描

Step 1 — Read the signal checklist / 第一步：读取检查项清单

Before scanning, read eeat-reference.md (same directory as this file) to load all 24 signal definitions.

Read: eeat-reference.md

Step 2 — Determine input type / 第二步：判断输入类型

Input	Action
Live URL / 线上 URL	Use WebFetch to fetch the page; extract full page HTML, JSON-LD, and visible text
Raw HTML / 原始 HTML	Parse provided HTML directly; no fetch needed
URL unreachable / 无法抓取	Mark all signals that require live page inspection as 🔍 Manual; proceed with available information

Step 3 — Execute dimensional scans / 第三步：按维度执行扫描

Run the four dimensions in order: Experience → Expertise → Authoritativeness → Trustworthiness

For each signal in eeat-reference.md:

Check the pass condition against the fetched page content
Record result: Pass ✅ / Fail ❌ / 🔍 Manual
For Fail results, note what was found and what is expected
For Manual signals, note what requires manual verification

8 · EEAT 评分算法

Per-dimension score / 维度评分：

维度分 = floor(该维度通过项数 / 该维度有效项数 × 100)

有效项数 = 总项数 − 🔍 Manual 项数（Manual 项从分母中排除）
四个维度各自独立计算

Total score / 总分：

总分 = floor((经验分 + 专业度分 + 权威性分 + 可信度分) / 4)

Rating labels / 评级标签：

Score range	Label
80–100	良好 ✅
50–79	需改进 ⚠️
0–49	差 ❌

9 · EEAT 报告格式

After completing all scans, output the report in the following structure. Use the same language as the user's prompt throughout (Chinese prompt → Chinese report; English prompt → English report).

Report template / 报告模板：

## EEAT 扫描报告

**扫描对象**：https://example.com/article/ （或 "Raw HTML input"）
**扫描日期**：YYYY-MM-DD
**总分：XX / 100**

---

### 维度总览

| 维度 | 得分 | 评级 |
|------|------|------|
| 经验 (Experience) | XX | 良好 ✅ / 需改进 ⚠️ / 差 ❌ |
| 专业度 (Expertise) | XX | ... |
| 权威性 (Authoritativeness) | XX | ... |
| 可信度 (Trustworthiness) | XX | ... |

---

### 经验 (Experience) — XX/100

| 检查项 | 结果 | 说明 |
|--------|------|------|
| 第一手内容标识 | Pass ✅ | 符合要求 |
| 经历日期明确 | Fail ❌ | 未注明具体经历时间 |
| 作者署名可见 | Pass ✅ | 符合要求 |
| 作者简介链接存在 | 🔍 Manual | 需手动核查作者页是否可访问 |
| 原创媒体 | Fail ❌ | 图片文件名含 "dall-e" |

### 专业度 (Expertise) — XX/100

| 检查项 | 结果 | 说明 |
|--------|------|------|
| ... | ... | ... |

### 权威性 (Authoritativeness) — XX/100

| 检查项 | 结果 | 说明 |
|--------|------|------|
| ... | ... | ... |

### 可信度 (Trustworthiness) — XX/100

| 检查项 | 结果 | 说明 |
|--------|------|------|
| ... | ... | ... |

---

### 行动建议

**P0 — 立即修复（影响收录或违反内容政策）**
- **[经验]** 原创媒体 — 将图片文件名中的 "dall-e" 前缀去除，上传流程中禁止保留 AI 工具名称
- **[可信度]** HTTPS — 将站点迁移至 HTTPS 并配置 301 重定向

**P1 — 应尽快修复**
- **[专业度]** 作者资质说明 — 在作者简介中补充职业背景或领域经验描述
- **[权威性]** 发布方名称缺失 — 在 NewsArticle Schema 的 publisher.name 字段填写机构名称

**P2 — 建议跟进**
- **[专业度]** 内容深度 — 文章不足 500 字，建议扩充至覆盖 3 个以上子议题

10 · Google News Diagnostic Report 完整诊断报告

Trigger: Run this section when scope is "Full Diagnostic", or when the user asks why their site is not in Google News / why articles don't rank / why competitors outrank them.

Score Aggregation / 评分聚合

Google News SEO Score = Layer 1 Score × 60% + Layer 2 Score × 40%

Layer 1 — Index Eligibility (60% weight)

Sub-check	Weight
Publisher Trust Score	15%
Author Authority Score	15%
Schema Health (completeness %)	15%
News Sitemap Health Score	10%
Crawlability Score	5%

Layer 2 — Ranking Potential (40% weight)

Sub-check	Weight
Freshness Score	15%
Content Type Score	10%
Topic Cluster Compatibility Score	10%
Top Stories Presence (binary: 5 or 0 pts)	5%

Rating labels:

Score	Label
80–100	🟢 Strong
50–79	🟡 Developing
0–49	🔴 At Risk

Diagnosis Routing / 诊断结论路由

Condition	Diagnosis Statement
Layer 1 score < 50	"Primary issue: This site is likely not eligible for Google News indexing. Fix Layer 1 issues before optimizing for ranking."
Layer 1 50–69 (Partial)	"Site has partial Google News index presence. Resolve remaining Layer 1 gaps to achieve consistent indexing, then address Layer 2 ranking."
Layer 1 ≥ 70, Layer 2 < 50	"Site is indexed but articles are not competitive in Google News clusters. Focus on Layer 2 ranking improvements."
Layer 1 ≥ 70, Layer 2 ≥ 70	"Site and articles meet Google News baseline requirements. Competitor gap analysis shows remaining optimization opportunities."

Full Diagnostic Report Template / 完整诊断报告模板

## Google News Diagnostic Report

**Analyzed**: [URL or domain]
**Date**: [YYYY-MM-DD]
**Google News SEO Score**: XX / 100  🟢 Strong / 🟡 Developing / 🔴 At Risk

---

### Dual-Layer Scorecard

| Layer | Score | Status |
|-------|-------|--------|
| Layer 1 — Index Eligibility | XX/100 | Pass ✅ / Partial ⚠️ / Fail ❌ |
| Layer 2 — Ranking Potential | XX/100 | Pass ✅ / Partial ⚠️ / Fail ❌ |

**Diagnosis**: [statement from routing table above]

---

### Layer 1 — Index Eligibility

| Check | Score | Status |
|-------|-------|--------|
| News Index Status | — | Not Indexed ❌ / Limited ⚠️ / Strong ✅ |
| Publisher Trust | XX/100 | Pass / Partial / Fail |
| Author Authority | XX/100 | Pass / Partial / Fail |
| Schema Health | XX% complete | Pass / Partial / Fail |
| News Sitemap Health | XX/100 | Pass / Partial / Fail |
| Crawlability | XX/100 | Pass / Partial / Fail |
| URL Structure | — | Recommended ✅ / Issues ⚠️ |

---

### Layer 2 — Ranking Potential

| Check | Score | Status |
|-------|-------|--------|
| Freshness | XX/100 | Fast ✅ / Moderate ⚠️ / Slow ❌ |
| Content Type | [type] / XX pts | Breaking / Analysis / Digest / Aggregation |
| Publisher Authority | XX/100 | Strong / Moderate / Weak |
| Topic Cluster Compatibility | XX/100 | High / Medium / Low |
| Top Stories Presence | — | Confirmed ✅ / Gap ❌ / Not triggering ⚠️ |

---

### Competitor Gap Analysis Summary

[See Section 6.5 output]

---

### Priority Fix List

**P0 — Fix immediately (blocks indexing)**
- [item]

**P1 — Fix soon (affects ranking)**
- [item]

**P2 — Best practice**
- [item]

References 参考资源

Google News ranking factors, optimization strategies, AI content policy, News Sitemap examples, two-layer architecture model, Topic Cluster signals: 见 reference.md
EEAT signal definitions and priority table: 见 eeat-reference.md

google-news-seo

Google News SEO Diagnostic Engine

Google News System Model

0 · Initial Assessment 审计前评估

0.5 · News Index Presence Detection 索引存在检测

Step 1 — Simulate Google News index query

Step 2 — Classify index presence

Step 3 — Route diagnostic focus

Output format

1 · Prerequisites 前置要求

Schema Fetch Protocol / Schema 获取流程

1.5 · Publisher Trust Detection 发布者信任度检测

Step 1 — Verify trust pages

Step 2 — Detect newsroom description

Step 3 — Detect Organization Schema

Publisher Trust Score Output

1.6 · Author Authority Detection 作者权威性检测

Step 1 — Verify author presence and schema match

Step 2 — Fetch author profile page

Step 3 — Detect suspicious AI author names

Step 4 — Detect social / credential signals

Author Authority Score Output

2 · NewsArticle Schema Checklist Schema 审查清单

Critical — affects indexing / 必检项（影响收录）

Recommended — affects rich results / 建议补充项（影响富摘要）

3 · AI Content Checks AI 内容专项

4 · Schema Fix Template 修复模板

5 · Systemic Bugs 系统性 Bug（批量修复）

5.5 · Technical SEO 技术检查

5.6 · On-Page SEO 文章页检查

Title Tag / 标题标签

Meta Description

Canonical Tag

Heading Structure

5.7 · Crawlability and Rendering Check 爬取与渲染检测

Step 1 — Fetch with Googlebot UA

Step 2 — Detect article body in initial HTML

Step 3 — Detect server-rendered Schema

Step 4 — Verify canonical and robots

Crawlability Score Output

5.8 · URL Structure Analysis URL 结构分析

Recommended patterns ✅

Problematic patterns ❌

Output

5.9 · Freshness Signal Analysis 新鲜度信号分析

Step 1 — Validate datePublished / dateModified

Step 2 — Estimate publication speed

Step 3 — Flag manipulation signals

Freshness Score Output

5.10 · Content Type Classification 内容类型分类

Step 1 — Classify article type

Step 2 — Detect AI template patterns

Content Type Score Output

5.11 · Publisher Authority Estimation 发布者权威估算

Publisher Authority Score Output

5.12 · Topic Cluster Compatibility 话题聚类兼容性

Step 1 — Headline entity analysis

Step 2 — Entity density in body

Step 3 — Timeliness check

Step 4 — Original reporting signals

Topic Cluster Compatibility Score Output

5.13 · Top Stories Detection Top Stories 检测

Step 1 — Extract topic keyword

Step 2 — Search for Top Stories carousel

Step 3 — Compare site against carousel publishers

Output

6 · Output Report Format 总结报告格式

Dual-Layer Scorecard (Full Diagnostic only)

Executive Summary 执行摘要

Detailed Findings / 逐项检查表

Priority Fix List / 优先级修复列表

6.5 · Competitor Gap Analysis 竞争对手差距分析

Step 1 — Identify top competitors

Step 2 — Fetch competitor articles

Step 3 — Compute gap metrics

Step 4 — Output gap analysis summary

7 · EEAT Scan 触发与输入处理

8 · EEAT 评分算法

9 · EEAT 报告格式

10 · Google News Diagnostic Report 完整诊断报告