Token导航 LogoToken导航

Agent Skill · 研究检索

pseo-quality-guard(伪质量卫士)

pseo-quality-guard 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

pseo-quality-guard 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为权限未标注;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 64,959。

总安装 291周安装 12GitHub Stars 40下载 95核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:pseo-quality-guard(伪质量卫士)
来源仓库:https://github.com/lisbeth718/pseo-skills
仓库路径:skills/pseo-quality-guard
安装命令:
npx skills add https://github.com/lisbeth718/pseo-skills --skill pseo-quality-guard
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/lisbeth718/pseo-skills --skill pseo-quality-guard

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于检索质量保障相关规范、测试用例与缺陷模式库。适合在代码评审或自动化测试策略制定时提供支持。通过 GitHub 仓库安装,需确认其是否接入内部质量门禁系统。

安装命令:npx skills add https://github.com/lisbeth718/pseo-skills --skill pseo-quality-guard。建议结合组织内控要求定制检查项与告警阈值。

来源仓库内路径:skills/pseo-quality-guard

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/lisbeth718/pseo-skills --skill pseo-quality-gu…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    权限没有明确标注,第一次使用时只给一个无关紧要的任务,观察它到底读了哪些文件和网络。

常见问题

6 条

pseo-quality-guard 怎么安装?

在终端执行 npx skills add https://github.com/lisbeth718/pseo-skills --skill pseo-quality-guard。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

pseo-quality-guard 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

pseo-quality-guard 需要什么权限?

权限级别没有明确标注。第一次使用时建议只给无关紧要的任务,观察它实际读写了哪些内容。

pseo-quality-guard 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

pseo-quality-guard 用的人多吗?

公开数据是累计安装 291 次,近一周安装 12 次,下载 95 次,GitHub 40 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

pseo-quality-guard 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
pseo-quality-guard

伪质量卫士 · 本页

29140权限未标注未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

pSEO Quality Guard

Validate generated pages against SEO quality standards. Detect and flag issues that would cause Google to devalue, deindex, or penalize programmatic pages.

Core Principles

  1. No thin pages: Every page must provide substantial, unique value
  2. No duplicate content: No two pages should have the same or near-identical content
  3. No cannibalization: No two pages should target the same keyword or intent
  4. Metadata uniqueness: Every page has unique title and description
  5. Fail loudly: Quality issues should block deployment, not slip through silently

Quality Checks

1. Thin Content Detection

A page is thin if it:

  • Has fewer than 300 words of unique text content (excluding navigation, footer, boilerplate)
  • Is essentially a template with only 1-2 variable substitutions
  • Contains no meaningful content beyond its metadata
  • Has the same paragraph structure as other pages with only proper nouns changed

How to check:

  • Extract text content from each rendered page (strip HTML tags, nav, footer)
  • Count unique words per page
  • Compare content across pages — compute similarity ratios
  • Flag pages with < 300 unique words or > 80% similarity to another page

2. Duplicate Content Detection

Check for:

  • Exact duplicates: Two pages with identical body content
  • Near duplicates: Pages with > 80% text similarity (use Jaccard similarity on n-grams or cosine similarity)
  • Title duplicates: Two pages with the same <title> tag
  • Description duplicates: Two pages with the same meta description
  • URL-based duplicates: Different URLs serving the same content (www vs non-www, trailing slash variants)

How to detect:

At small scale (< 200 pages), pairwise comparison is feasible:

For each pair of pages:
  similarity = intersection(ngrams(page_a), ngrams(page_b)) / union(ngrams(page_a), ngrams(page_b))
  if similarity > 0.8: FLAG as near-duplicate

At scale (200+ pages), pairwise is O(n²) and impractical. Use one of:

  • MinHash / LSH: Hash n-gram sets into fixed-size signatures, use locality-sensitive hashing to find candidate pairs. Reduces comparisons from O(n²) to near-linear.
  • SimHash: Compute a fingerprint per page, compare fingerprints (Hamming distance). Pages with similar fingerprints are candidates.
  • Sampling: Compare each page against a random sample of 50 others + all pages in the same category. Not exhaustive but catches the common cases.
  • Template fingerprinting: Hash the non-variable parts of each page. If two pages share the same template fingerprint, flag them — they differ only in variable slots.

3. Keyword Cannibalization Detection

Cannibalization occurs when multiple pages target the same search query.

Detection method:

  • Extract the primary keyword/intent from each page (from title, H1, and first paragraph)
  • Group pages that share the same primary keyword
  • Flag groups with 2+ pages targeting the same keyword

Resolution strategies:

  • Merge thin pages targeting the same keyword into one comprehensive page
  • Differentiate intent (informational vs. transactional vs. navigational)
  • Use canonical tags to point duplicates to the primary page
  • Adjust titles and H1s to target distinct long-tail variations

4. Metadata Quality Validation

Check every page's metadata for:

FieldValidation Rule
TitlePresent, 30-70 chars, unique across all pages
DescriptionPresent, 100-170 chars, unique across all pages
H1Present, exactly one per page, unique across all pages
CanonicalPresent, absolute URL, self-referencing
OG:titlePresent
OG:descriptionPresent
OG:urlPresent, matches canonical

5. Schema Markup Validation

  • Every page has at least BreadcrumbList schema
  • Content pages have Article or appropriate type schema
  • FAQ pages have FAQPage schema with valid Q&A pairs
  • No schema has empty or placeholder values
  • All URLs in schema are absolute

6. Internal Link Health

  • No orphan pages (pages with zero inbound internal links)
  • No broken internal links (href targets that return 404)
  • Every page links back to its category hub
  • Breadcrumbs are present and correct

7. Scaled Content Abuse Detection (Google 2025)

Google's 2025 updates (March, June, August, December) increasingly target programmatic pages that exist primarily to manipulate rankings. The method (AI, templates, human) is irrelevant — only intent and value matter.

Check for these specific patterns that trigger Google's SpamBrain system:

  • Template repetitiveness ratio: Extract the boilerplate (shared HTML structure and text) from all pages of a type. If boilerplate is 60-80%, flag as warning; if > 80%, flag as critical risk for scaled content abuse.
  • Variable-swap-only differentiation: If the only differences between pages are proper nouns (city names, product names, keywords), flag as extremely high risk. Google specifically called out "location pages that use the same template in dozens of cities."
  • Filler content patterns: Detect generic introductory paragraphs ("In today's world...", "When it comes to...", "If you're looking for...") that add no information. These patterns are specifically targeted by the December 2025 "Needs Met" enforcement.
  • Value-first test: Check if the primary content/answer appears within the first 200 words. Pages that bury value below filler are devalued.
  • E-E-A-T signal presence: Check for author attribution, data sources, last-updated dates. Absence of all trust signals on pSEO pages is a risk factor.
  • Publication velocity: If 500+ pages were published within a single day or week, flag for review. Gradual rollout is safer.

Severity:

  • Template repetitiveness > 80%: Critical — will likely trigger scaled content abuse penalty
  • Template repetitiveness 60-80%: Warning — at risk, needs content enrichment
  • No E-E-A-T signals on any page: Warning
  • All pages published same day: Warning

8. Heading Hierarchy Validation

Check every page for correct heading structure:

  • Exactly one <h1> per page
  • No heading level skips (h1 → h3 without h2 is invalid)
  • Headings follow a logical document outline (h1 > h2 > h3)
  • No empty heading tags
  • Heading content is meaningful (not generic like "Section 1")

9. Robots and Indexation

  • Pages intended for indexing have index, follow (or no robots tag)
  • Thin or utility pages have noindex
  • No important pages accidentally blocked by robots.txt
  • Sitemap includes all indexable pages and excludes noindexed ones

Output Format

## pSEO Quality Report

### Summary
- Total pages checked: X
- Issues found: X (Y critical, Z warnings)

### Thin Content
- [list of pages with word counts below threshold]

### Duplicate Content
- [list of duplicate pairs with similarity scores]

### Keyword Cannibalization
- [list of keyword groups with competing pages]

### Metadata Issues
- [list of pages with metadata problems]

### Schema Issues
- [list of pages with schema problems]

### Linking Issues
- [list of orphan pages and broken links]

### Scaled Content Abuse Risk
- Template boilerplate ratio: X%
- Variable-swap-only pages: X
- Pages missing E-E-A-T signals: X
- Filler intro patterns detected: X pages
- Risk level: [Low | Medium | High | Critical]

### Pruning Recommendations
- Pages to remove: X
- Pages to merge: X
- Pages to noindex: X
- Pages to enrich: X
- [list of specific pages and recommended action]

### Action Required
1. [prioritized list of fixes]

Memory Considerations

Quality checks load and compare page content across the full site. At scale this is the most memory-intensive operation in the pSEO pipeline.

Do NOT load all full page content into memory at once. Instead:

  • Stream-compare: Load pages one at a time, compute a fingerprint (hash or MinHash signature), store only the fingerprint (~100 bytes per page). Compare fingerprints after all pages are processed.
  • Batch by category: Run similarity checks within each category first (most duplicates are same-category). Only run cross-category checks on a sample.
  • Write intermediate results to disk: For large sites, write per-page metrics (word count, fingerprint, title hash) to a JSON file, then process the file.

Memory budget estimate:

PagesFingerprints onlyFull content in memory
1,000~100KB~100-500MB
10,000~1MB~1-5GB (will OOM)
50,000~5MB~5-25GB (impossible)

Always use the fingerprint approach at 500+ pages.

Content Pruning Recommendations

When quality checks find issues, don't just flag — recommend action. Google's own guidance: "If you're considering deleting entire sections of your site, that's likely a sign those sections were created for search engines first, and not people."

See references/thresholds.md → "Content Pruning Decision Thresholds" for the exact decision table (conditions and actions).

200 genuinely valuable pages outperform 5,000 thin pages. Pruning is not failure — it's quality control.

After pruning:

  • Add 301 redirects from removed URLs to the best related page
  • Update the sitemap to exclude removed pages
  • Update internal links that pointed to removed pages
  • Re-run quality guard to verify improved metrics

Integration as Build Check

This skill can be turned into a build-time validation script:

// scripts/validate-pseo.ts
// Run: npx tsx scripts/validate-pseo.ts
// CI/CD: exits with code 1 if critical issues found

import { getAllSlugs, getPageData } from "../lib/data";

async function validate() {
  const slugs = await getAllSlugs();
  const issues: { slug: string; level: "critical" | "warning"; message: string }[] = [];

  for (const { slug } of slugs) {
    const page = await getPageData(slug);
    if (!page) {
      issues.push({ slug, level: "critical", message: "Page data missing" });
      continue;
    }
    if (!page.title || !page.metaDescription || !page.h1) {
      issues.push({ slug, level: "critical", message: "Missing required metadata" });
    }
    if (page.title.length > 70) {
      issues.push({ slug, level: "warning", message: `Title too long: ${page.title.length} chars` });
    }
    // Add more checks: word count, duplicate titles, schema presence, etc.
  }

  const critical = issues.filter((i) => i.level === "critical");
  console.log(`Checked ${slugs.length} pages. ${issues.length} issues (${critical.length} critical).`);
  issues.forEach((i) => console.log(`[${i.level}] ${i.slug}: ${i.message}`));
  process.exit(critical.length > 0 ? 1 : 0);
}

validate();

Scope Parameter

If $ARGUMENTS specifies a check:

  • all (default): Run all checks
  • thin: Thin content detection only
  • duplicates: Duplicate content detection only
  • cannibalization: Keyword cannibalization only
  • metadata: Metadata validation only
  • abuse: Scaled content abuse pattern detection only
  • prune: Run all checks and output pruning recommendations
  • delta: Run all checks on pages modified since last validation only (requires content hashing — see pseo-scale section 4)

Relationship to Other Skills

  • Validates output of: pseo-templates, pseo-metadata, pseo-schema, pseo-linking
  • Depends on: pseo-data (needs access to all page data for cross-page comparisons)
  • Run after: Any other pseo-* skill to verify quality
  • Extended by: pseo-scale (incremental/delta validation, parallel category-partitioned checks, content hash storage)

平台分布

Codex

35.68%
按下载量换算34

Claude

29.07%
按下载量换算28

Cursor

21.89%
按下载量换算21

Gemini CLI

9.88%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限未标注

当前来源未能明确判断权限范围,默认进入异常复核队列。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills