据 AlphaMoat 统计,截至 2026-09,PLS SEO Audit 累计下载 3,492 次、获 2 个星标,在全部 5,078 个 AI 技能中排名第 3,164 位,在「内容创作」分类(362 个)中排名第 214 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 116 | 3,451 | 1 | - |
| SkillHub ↗ | 114 | 3,492 | 2 | — |
各平台的计数口径不同,统计时间不一致。
该技能涵盖页面元素、技术 SEO、内容质量、结构化数据和竞品比较,也提供生成审计报告的模板。部分检查会调用外部服务或抓取网站;结果应作为检查线索,不代表排名保证。 仅提交获准的公开 URL,保护 API token,使用经审查且固定版本的 Lighthouse。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 3,492 | — | 2 |
| 2026-08 | 3,350 | +3.4% | 2 |
| 2026-07 | 3,240 | +9.6% | 2 |
| 2026-06 | 2,957 | +5.5% | 1 |
| 2026-05 | 2,802 | — | 2 |
| 2026-03 | 1,908 | — | 1 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。
--- name: seo-audit description: Scan content and websites for SEO gaps, identify opportunities to outrank competitors. Use when: (1) Analyzing page SEO, (2) Checking meta tags and structured data, (3) Reviewing content for keyword optimization, (4) Auditing technical SEO factors. --- # SEO Audit Comprehensive SEO analysis for content and websites. ## Quick Audit Checklist
### On-Page SEO
```markdown
- [ ] Title tag (50-60 chars, keyword near front)
- [ ] Meta description (150-160 chars, compelling)
- [ ] H1 tag (one per page, includes target keyword)
- [ ] H2-H6 hierarchy (logical structure)
- [ ] Image alt text (descriptive, keyword-relevant)
- [ ] Internal links (3-5 per page minimum)
- [ ] URL structure (short, descriptive, hyphens)
- [ ] Canonical tags (prevent duplicate content)
```
### Technical SEO
```markdown
- [ ] Page speed (<3s load time)
- [ ] Mobile-friendly (responsive design)
- [ ] HTTPS (SSL certificate valid)
- [ ] XML sitemap (submitted to Search Console)
- [ ] Robots.txt (properly configured)
- [ ] Structured data (Schema.org markup)
- [ ] Core Web Vitals (LCP, FID, CLS)
```
### Content Quality
```markdown
- [ ] Keyword in first 100 words
- [ ] Content length matches intent
- [ ] No keyword stuffing (<2% density)
- [ ] Readable (Flesch-Kincaid score)
- [ ] Unique value (not duplicate content)
- [ ] Fresh content (updated regularly)
```
---
## Technical SEO Commands
### Page Speed Analysis
```bash
# Using PageSpeed Insights API
curl "https://www.googleapis.com/pagespeedonline/v5/runPagespeed?url=https://example.com&strategy=mobile"
# Using lighthouse locally
npx lighthouse https://example.com --view
```
### Check Robots.txt
```bash
curl https://example.com/robots.txt
```
### Check Sitemap
```bash
curl https://example.com/sitemap.xml | xmllint --format -
```
### SSL Certificate Check
```bash
openssl s_client -connect example.com:443 -servername example.com 2>/dev/null | openssl x509 -noout -dates
```
### Mobile-Friendly Test
```bash
# Google's Mobile-Friendly Test API
curl "https://searchconsole.googleapis.com/v1/urlTestingTools/mobileFriendlyTest:run?key=YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"url":"https://example.com"}'
```
---
## Content Analysis
### Keyword Density
```python
import re
from collections import Counter
def keyword_density(text, keyword):
words = re.findall(r'\b\w+\b', text.lower())
keyword_count = text.lower().count(keyword.lower())
density = (keyword_count / len(words)) * 100
return {
"keyword": keyword,
"count": keyword_count,
"total_words": len(words),
"density": f"{density:.2f}%"
}
# Target: 1-2% density
```
### Readability Score
```python
import textstat
text = "Your content here..."
flesch = textstat.flesch_reading_ease(text)
# 90-100: Very Easy
# 60-70: Standard
# 0-30: Very Difficult
grade = textstat.flesch_kincaid_grade(text)
# Target: 8-9 for general audience
```
### Content Structure Analysis
```python
from bs4 import BeautifulSoup
def analyze_headings(html):
soup = BeautifulSoup(html, 'html.parser')
headings = {
'h1': soup.find_all('h1'),
'h2': soup.find_all('h2'),
'h3': soup.find_all('h3'),
}
issues = []
if len(headings['h1']) == 0:
issues.append("Missing H1 tag")
elif len(headings['h1']) > 1:
issues.append("Multiple H1 tags (should be one)")
return {
"counts": {k: len(v) for k, v in headings.items()},
"issues": issues
}
```
---
## Meta Tag Analysis
### Extract Meta Tags
```python
from bs4 import BeautifulSoup
import requests
def audit_meta_tags(url):
response = requests.get(url)
soup = BeautifulSoup(response.text, 'html.parser')
title = soup.find('title')
description = soup.find('meta', attrs={'name': 'description'})
keywords = soup.find('meta', attrs={'name': 'keywords'})
issues = []
if not title or len(title.text) < 30:
issues.append("Title too short or missing")
elif len(title.text) > 60:
issues.append("Title too long (>60 chars)")
if not description:
issues.append("Meta description missing")
elif len(description.get('content', '')) < 120:
issues.append("Meta description too short")
elif len(description.get('content', '')) > 160:
issues.append("Meta description too long")
return {
"title": title.text if title else None,
"description": description.get('content') if description else None,
"issues": issues
}
```
---
## Structured Data Check
### Validate Schema Markup
```bash
# Using Google's Rich Results Test
curl "https://searchconsole.googleapis.com/v1/urlTestingTools/richResultsTest:run" \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{"url":"https://example.com"}'
```
### Common Schema Types
```json
// Article
{
"@context": "https://schema.org",
"@type": "Article",
"headline": "Title",
"author": {"@type": "Person", "name": "Author"},
"datePublished": "2026-01-01"
}
// Local Business
{
"@context": "https://schema.org",
"@type": "LocalBusiness",
"name": "Business Name",
"address": {"@type": "PostalAddress", "streetAddress": "123 Main"},
"telephone": "+1-555-555-5555"
}
// FAQ
{
"@context": "https://schema.org",
"@type": "FAQPage",
"mainEntity": [{
"@type": "Question",
"name": "Question?",
"acceptedAnswer": {"@type": "Answer", "text": "Answer"}
}]
}
```
---
## Competitor Analysis
### Compare Page Metrics
```python
import requests
from bs4 import BeautifulSoup
def compare_seo(target_url, competitor_url):
def get_metrics(url):
r = requests.get(url)
soup = BeautifulSoup(r.text, 'html.parser')
return {
"title_len": len(soup.find('title').text) if soup.find('title') else 0,
"h1_count": len(soup.find_all('h1')),
"h2_count": len(soup.find_all('h2')),
"word_count": len(soup.get_text().split()),
"images": len(soup.find_all('img')),
"images_no_alt": len([i for i in soup.find_all('img') if not i.get('alt')])
}
return {
"target": get_metrics(target_url),
"competitor": get_metrics(competitor_url)
}
```
---
## SEO Audit Report Template
```markdown
# SEO Audit Report
## Summary
- **Score:** X/100
- **Critical Issues:** X
- **Warnings:** X
- **Passed:** X
## Critical Issues
1. [Issue description]
- Impact: [High/Medium/Low]
- Fix: [Recommended action]
## Technical SEO
| Factor | Status | Notes |
|--------|--------|-------|
| Page Speed | ⚠️ | 4.2s load time |
| Mobile | ✅ | Responsive |
| HTTPS | ✅ | Valid SSL |
| Sitemap | ✅ | Submitted |
## On-Page SEO
| Factor | Status | Notes |
|--------|--------|-------|
| Title | ✅ | 55 chars |
| Meta Desc | ⚠️ | Too short |
| H1 | ✅ | Present |
| Images | ⚠️ | 3 missing alt |
## Recommendations
1. [Priority 1]
2. [Priority 2]
3. [Priority 3]
```
识别并去掉文本里的 AI 写作痕迹,让文字读起来更像人写。
把文本里 AI 写作的典型痕迹挑出来并改写得更自然,同时保留原意的写作编辑技能。
识别并去除 AI 生成文本痕迹的中文文字编辑技能。
wechat-search 检索公众号热门文章,并按相关性、热度和时效整理结果。
从公开短视频分享链接提取标题、简介和口播文案,并给出改写版本。
humanizer 为中英文文本提供更自然的表达和风格编辑指导。
写网文章节并按文件落盘,聊天里只回摘要与指针。
把一组营销方法模块收进同一个技能,按请求路由到对应模块取清单与可交付稿。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。