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blog-analyze博客分析

Agent Skill

blog-analyze 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,258

周安装

97

GitHub Stars

619

下载量

792
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:blog-analyze(博客分析)
来源仓库:https://github.com/agricidaniel/claude-blog
仓库路径:skills/blog-analyze
安装命令:
npx skills add https://github.com/agricidaniel/claude-blog --skill blog-analyze
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/agricidaniel/claude-blog --skill blog-analyze

简介

用于对博客文章进行质量评分与 AI 内容检测,输出 0–100 分多维评估报告。

  • 适合内容运营者定期审计站点质量或投稿前自我检查原创程度。
  • 支持本地文件或 URL 输入,涵盖 E-E-A-T 信号与改进优先级排序。
  • 安装方式:GitHub 仓库,使用 npx skills add 命令添加;无外部依赖纯 Claude 处理。
  • 注意:分数仅供参考,高得分不代表内容正确,低分需人工介入复核。

SKILL.md

Blog Analyzer -- Quality Audit & Scoring

Scores blog posts on a 0-100 scale across 5 categories and provides prioritized improvement recommendations. Includes AI content detection analysis. Works with local files or published URLs.

Reference documents:

  • references/quality-scoring.md -- full scoring checklist
  • references/eeat-signals.md -- E-E-A-T evaluation criteria

Input Handling

  • Local file: Read the file directly
  • URL: Fetch with WebFetch, extract content
  • Directory: Scan for blog files, audit all (batch mode)
  • Flags: --format json|table, --batch, --sort score

Scoring Process

Step 1: Content Extraction

Read the blog post and extract:

  • Frontmatter (title, description, date, lastUpdated, author, tags)
  • Heading structure (H1, H2, H3 with hierarchy)
  • Paragraph count and word counts per paragraph
  • Statistics (any number claims with or without sources)
  • Images (count, alt text presence, format)
  • Charts/SVGs (count, type diversity)
  • Links (internal, external, broken)
  • FAQ section presence
  • Schema markup (types present)
  • Meta tags (title, description, OG tags, twitter cards)
  • Sentence lengths for burstiness analysis
  • Vocabulary tokens for diversity scoring

Step 2: Score Each Category

Load references/quality-scoring.md for the full checklist. Score each:

Content Quality (30 points)

CheckPointsPass Criteria
Depth/comprehensiveness7Covers topic thoroughly, no major gaps
Readability (Flesch 60-70)7Flesch 60-70 ideal, 55-75 acceptable; Grade 7-8; Gunning Fog 7-8
Originality/unique value markers5Original data, case studies, first-hand experience
Sentence & paragraph structure4Avg sentence 15-20 words, ≤25% over 20; paragraphs 40-80 words; H2 every 200-300 words
Engagement elements4Summary box, callouts, varied content blocks. Accepts: "TL;DR", "Key Takeaways", "The Bottom Line", "What You'll Learn", "At a Glance", "In Brief"
Grammar/anti-pattern3Passive voice ≤10%, AI trigger words ≤5/1K, transition words 20-30%, clean prose

Readability Bands (apply per persona, or use default):

AudienceFlesch GradeFlesch EaseScoring Impact
Consumer6-860-80Full points if in range
Professional8-1050-60Full points if in range
Technical10-1230-50Full points if in range
Default (no persona)7-860-70Current scoring unchanged

Content clarity is the #2 factor for AI citation probability (+32.83% score differential). Average US adult reads at 7th-8th grade level.

SEO Optimization (25 points)

CheckPointsPass Criteria
Heading hierarchy with keywords5H1 -> H2 -> H3, no skips, keyword in 2-3 headings
Title tag (40-60 chars, keyword, power word)4Front-loaded keyword, positive sentiment
Keyword placement/density4Natural integration, no stuffing, in first 100 words
Internal linking (3-10 contextual)4Descriptive anchor text, bidirectional
URL structure3Short, keyword-rich, no stop words, lowercase
Meta description (150-160 chars, stat)3Fact-dense, includes one statistic
External linking (tier 1-3)23-8 outbound links to authoritative sources

E-E-A-T Signals (15 points)

CheckPointsPass Criteria
Author attribution (named, with bio)4Real name, credentials, not sales pitch
Source citations (tier 1-3, inline)48+ unique stats, zero fabricated
Trust indicators4Contact page, about page, editorial policy
Experience signals3"When we tested...", original photos/data

Technical Elements (15 points)

CheckPointsPass Criteria
Schema markup (3+ types = bonus)4BlogPosting + FAQ + Person minimum
Image optimization3AVIF/WebP, descriptive alt text, lazy except LCP
Structured data elements2Tables, lists, comparison blocks
Page speed signals2LCP < 2.5s, no render-blocking JS
Mobile-friendliness2Responsive, tap targets 48px+
OG/social meta tags2og:title, og:description, og:image, twitter:card

AI Citation Readiness (15 points)

CheckPointsPass Criteria
Passage-level citability (120-180 words)4Self-contained sections with stat + source
Q&A formatted sections360-70% of H2s as questions, FAQ present
Entity clarity3Unambiguous topic entity, consistent terminology
Content structure for extraction3Answer-first, tables with thead, comparison formats
AI crawler accessibility2SSR/SSG, no JS-gated content

Step 3: AI Content Detection

Analyze the post for AI-generated content risk:

Burstiness Score (sentence length variance):

  • Calculate standard deviation of sentence lengths across the post
  • Human writing: high variance (short punchy + long complex sentences)
  • AI writing: low variance (consistently medium-length sentences)
  • Score: 0-10 scale (10 = very human-like burstiness)

Known AI Phrase Detection -- flag occurrences of these 17 phrases:

  1. "It's important to note"
  2. "In today's digital landscape"
  3. "Delve into"
  4. "Navigating the complexities"
  5. "Let's explore"
  6. "Furthermore"
  7. "In conclusion"
  8. "It is worth mentioning"
  9. "Embark on"
  10. "Cutting-edge"
  11. "Leverage" (as a verb, non-financial context)
  12. "Game-changer"
  13. "Revolutionize"
  14. "Streamline"
  15. "Harness the power"
  16. "Dive deep"
  17. "Unlock the potential"
  18. Em dashes (-) - count all instances, flag as AI writing pattern

Vocabulary Diversity (Type-Token Ratio):

  • Calculate unique words / total words
  • Human writing: TTR typically 0.4-0.6 for long-form
  • AI writing: TTR often below 0.35 (repetitive vocabulary)

AI Content Risk Assessment:

  • Flag if AI probability > 50% based on combined signals
  • Provide specific passages that triggered the flag
  • Recommend humanization: personal anecdotes, varied sentence rhythm, domain jargon

Step 4: Determine Rating

ScoreRatingAction
90-100ExceptionalPublish as-is, flagship content
80-89StrongMinor polish, ready for publication
70-79AcceptableTargeted improvements needed
60-69Below StandardSignificant rework required
< 60RewriteFundamental issues, start from outline

Step 5: Generate Report

Default output format (Markdown):

## Blog Quality Report: [Title]

**Score: [X]/100** -- [Rating]

### Score Breakdown
| Category | Score | Max | Notes |
|----------|-------|-----|-------|
| Content Quality | X | 30 | [1-line summary] |
| SEO Optimization | X | 25 | [1-line summary] |
| E-E-A-T Signals | X | 15 | [1-line summary] |
| Technical Elements | X | 15 | [1-line summary] |
| AI Citation Readiness | X | 15 | [1-line summary] |
| **Total** | **X** | **100** | |

### AI Content Risk
- **Burstiness score**: [X]/10 ([human-like / moderate / flat])
- **AI phrases detected**: [N] ([list phrases found])
- **Vocabulary diversity (TTR)**: [X] ([high / acceptable / low])
- **AI probability**: [X]% -- [No concern / Review recommended / High risk]
- **Flagged passages**: [quote specific flat or formulaic sections, if any]

### Issues Found

#### Critical (Must Fix)
- [ ] [Issue with specific location and fix]

#### High Priority
- [ ] [Issue with specific location and fix]

#### Medium Priority
- [ ] [Issue with specific location and fix]

#### Low Priority
- [ ] [Issue with specific location and fix]

### Quick Stats
- Word count: [N]
- Paragraphs: [N] (X over 150 words)
- H2 sections: [N] (X as questions, X with answer-first formatting)
- Statistics: [N] sourced / [N] unsourced
- Images: [N] (X with alt text, formats: ...)
- Charts: [N] (types: ...)
- Internal links: [N]
- External links: [N] (tier breakdown: ...)
- Schema types: [list]
- OG/social tags: [present/missing]

### Recommended Actions
1. [Most impactful fix -- Critical items first]
2. [Second most impactful]
3. [Third]

Run `/blog rewrite <file>` to apply these optimizations automatically.

Export Formats

Default: Markdown Report

Standard detailed report as shown above.

JSON Export (--format json)

Machine-readable output for integration with CI/CD or dashboards:

{
  "file": "post.md",
  "title": "...",
  "score": 78,
  "rating": "Acceptable",
  "categories": {
    "content_quality": { "score": 22, "max": 30 },
    "seo_optimization": { "score": 18, "max": 25 },
    "eeat_signals": { "score": 12, "max": 15 },
    "technical_elements": { "score": 13, "max": 15 },
    "ai_citation_readiness": { "score": 13, "max": 15 }
  },
  "ai_detection": {
    "burstiness": 6.2,
    "ai_phrases_found": ["Furthermore", "Let's explore"],
    "ttr": 0.44,
    "ai_probability": 32
  },
  "issues": {
    "critical": [],
    "high": [],
    "medium": [],
    "low": []
  }
}

Table Export (--format table)

Compact summary for quick review:

File            | Score | Rating     | Content | SEO | EEAT | Tech | AI-Ready | AI Risk
post.md         |    78 | Acceptable |   22/30 | 18/25 | 12/15 | 13/15 |    13/15 |    32%

Batch Mode

When given a directory or --batch flag, scan for blog files and produce a summary table. Use --sort score to order by score (ascending by default).

## Blog Audit Summary: [N] Posts Analyzed

| File | Score | Rating | Content | SEO | EEAT | Tech | AI-Ready | AI Risk | Top Issue |
|------|-------|--------|---------|-----|------|------|----------|---------|-----------|
| post-1.md | 85 | Strong | 26/30 | 20/25 | 13/15 | 14/15 | 12/15 | 18% | Missing OG tags |
| post-2.md | 42 | Rewrite | 10/30 | 8/25 | 5/15 | 9/15 | 10/15 | 71% | 12 fabricated stats |
| post-3.md | 71 | Acceptable | 20/30 | 16/25 | 10/15 | 12/15 | 13/15 | 25% | No answer-first |

### Priority Queue (Lowest Scoring First)
1. post-2.md (42) -- Full rewrite needed, high AI content risk
2. post-3.md (71) -- Answer-first formatting + stats needed
3. post-1.md (85) -- Add OG tags, minor polish

Run `/blog rewrite <file>` on each, starting from lowest score.

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平台分布

Codex

36.57%
按下载量换算290

Claude

30%
按下载量换算238

Cursor

17.45%
按下载量换算138

Gemini CLI

9.3%
按下载量换算74

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