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Agent Skill · 研究检索

content-optimizer(内容优化器)

用于辅助文档、README、Markdown、说明文和内容稿件的整理与改写。它适合让 Agent 提炼结构、补齐章节、统一术语、检查链接或把零散材料整理成可读文档。使用时应保留项目已有事实、命令和路径,不要把未确认的信息写成确定结论;涉及对外文案时,还需要控制语气,避免过度营销或夸大能力。

content-optimizer 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,权限为需要联网;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 73,674 个 Skill 里排第 44,563。

总安装 906周安装 37GitHub Stars 37下载 290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:content-optimizer(内容优化器)
来源仓库:https://github.com/majesticlabs-dev/majestic-marketplace
仓库路径:skills/content-optimizer
安装命令:
npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill content-optimizer
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/majesticlabs-dev/majestic-marketplace --skill content-optimizer

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。

用于辅助文档、README、Markdown 和内容稿件的整理与改写。适合提炼结构、补齐章节、统一术语或检查链接。使用时应保留项目已有事实与路径,避免写成确定结论。涉及对外文案时需控制语气,防止过度营销或夸大能力。content-optimizer 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/content-optimizer

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/majesticlabs-dev/majestic-marketplace --skill…;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

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

content-optimizer 怎么安装?

在终端执行 npx skills add https://github.com/majesticlabs-dev/majestic-marketplace --skill content-op…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

content-optimizer 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。

content-optimizer 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

content-optimizer 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。

content-optimizer 用的人多吗?

公开数据是累计安装 906 次,近一周安装 37 次,下载 290 次,GitHub 37 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。

content-optimizer 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
content-optimizer

内容优化器 · 本页

90637需要联网未明确通过

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

Content Optimizer Skill

A systematic workflow for optimizing content to perform well in both traditional search engines and AI-powered systems (GEO).

When to Use

  • Improving existing content performance
  • Preparing content for publication
  • Refreshing underperforming pages
  • Enhancing AI citation potential
  • Optimizing for featured snippets
  • Building topical authority

Optimization Workflow

Phase 1: Content Analysis

Before optimizing, understand the current state:

Content Inventory:

Title: [Current title]
Word Count: [X words]
Last Updated: [Date]
Target Keyword: [Primary keyword]
Current Structure: [H1 → H2 count → H3 count]

Performance Assessment:

  • Search intent alignment
  • Topic completeness
  • Competitive positioning
  • Freshness status

Phase 2: Keyword Optimization

Optimize keyword presence without over-optimization:

Primary Keyword Placement:

LocationStatusRecommendation
Title tagFirst 30 chars
H1Natural inclusion
First 100 wordsOpening paragraph
H2 headers2-3 variations
Meta descriptionWith CTA
URLIf practical

Keyword Density Check:

  • Target: 0.5-1.5%
  • Calculate: (keyword occurrences / total words) × 100
  • Flag if >2% (over-optimization risk)

Semantic Enhancement: Add related terms and concepts:

  • LSI keywords (5-10 variations)
  • Related entities
  • Question variations
  • Synonym usage

Phase 3: Structure Optimization

Improve content organization:

Header Hierarchy:

H1: Primary Topic (1 only)
├── H2: Main Section 1
│   ├── H3: Subsection
│   └── H3: Subsection
├── H2: Main Section 2
│   └── H3: Subsection
└── H2: Conclusion/Summary

Scannable Formatting:

  • Short paragraphs (2-3 sentences)
  • Bullet lists for features/benefits
  • Numbered lists for steps/processes
  • Tables for comparisons
  • Bold for key terms
  • Block quotes for callouts

Internal Linking:

  • Link to related content (3-5 contextual links)
  • Use descriptive anchor text
  • Link from high-authority pages
  • Create hub/spoke patterns

Phase 4: AI Extractability

Optimize for LLM citation:

Add Extractable Elements:

TL;DR Block:

## TL;DR

[2-3 sentence summary with key facts. Include primary keyword and main value proposition. Perfect for AI extraction.]

FAQ Section:

## Frequently Asked Questions

### [Exact question users ask]?

[40-60 word direct answer. Lead with the key fact. Include relevant data or specifics.]

### [Another common question]?

[Direct, comprehensive answer.]

Comparison Table:

| Feature | Option A | Option B | Option C |
|---------|----------|----------|----------|
| Price | $X | $Y | $Z |
| Feature 1 | Yes | No | Yes |
| Best For | [Use case] | [Use case] | [Use case] |

Paragraph Optimization:

  • Break paragraphs >120 words
  • Lead with key fact
  • One idea per paragraph
  • Clear topic sentences

Phase 5: E-E-A-T Enhancement

Add trust and expertise signals:

Author Enhancement:

*Written by [Name], [Title/Credentials]. [1-2 sentence bio with expertise indicators.]*

Source Citations:

  • Add statistics with sources
  • Link to authoritative references
  • Include expert quotes
  • Reference original research

Trust Signals:

  • Last updated date
  • Editorial review note
  • Fact-checking statement
  • Contact information

Phase 6: Meta Optimization

Finalize metadata:

Title Tag Formula:

[Primary Keyword] - [Benefit/Hook] | [Brand] (55-60 chars)

Meta Description Formula:

[Action verb] + [benefit]. [Key feature]. [CTA] (150-160 chars)

URL Structure:

/primary-keyword-descriptive-slug (under 60 chars)

Output Format

Content Optimization Report

## Content Optimization Complete

**Page:** [Title/URL]
**Optimizations Applied:** X changes

### Summary of Changes

**Keyword Optimization:**
- Added primary keyword to [locations]
- Integrated X LSI keywords
- Current density: X%

**Structure Improvements:**
- Reorganized headers for logical flow
- Added X bullet lists
- Created X tables
- Broke X long paragraphs

**AI Extractability:**
- Added TL;DR summary
- Created FAQ section (X questions)
- Added comparison table
- Optimized paragraph lengths

**E-E-A-T Additions:**
- Added author bio
- Included X source citations
- Added last updated date

**Meta Updates:**
- New title: [title] (X chars)
- New description: [desc] (X chars)

### Before/After Comparison

| Metric | Before | After |
|--------|--------|-------|
| Word count | X | Y |
| Keyword density | X% | Y% |
| H2 count | X | Y |
| Internal links | X | Y |
| Extractable elements | X | Y |

### Next Steps
1. [Recommended follow-up action]
2. [Additional improvement]

Quick Optimization Checklist

For rapid optimization:

  • Title tag optimized (keyword + hook)
  • H1 matches target keyword
  • First 100 words include keyword
  • At least 3 internal links
  • TL;DR or summary present
  • FAQ section (3+ questions)
  • Paragraphs under 120 words
  • At least 1 table or comparison
  • Author/expertise signals
  • Updated date visible

Cannibalization Detection

When optimizing multiple pages, check for keyword cannibalization — pages competing against each other for the same queries.

Cannibalization Types

Title/Meta Overlap:

  • Similar page titles targeting same keyword
  • Duplicate meta descriptions
  • Same target keywords across pages

Content Overlap:

  • Similar topic coverage
  • Duplicate sections
  • Same search intent served by multiple pages

Structural Issues:

  • Identical header patterns
  • Similar content depth on same topic
  • Overlapping keyword focus

Detection Process

  1. Analyze keywords across provided pages
  2. Identify topic and keyword overlap
  3. Compare search intent targets
  4. Assess content similarity
  5. Find differentiation opportunities
  6. Recommend consolidation if needed

Prevention Strategy

  1. Clear keyword mapping - One primary keyword per page
  2. Distinct search intent - Different user needs per page
  3. Unique angles - Different perspectives on related topics
  4. Differentiated metadata - Unique titles/descriptions
  5. Strategic consolidation - Merge when appropriate

Resolution Tactics

  • Merge similar content into single authoritative page
  • 301 redirect weak pages to stronger ones
  • Rewrite competing pages for different intent
  • Update internal anchor text for clarity
  • Implement canonical tags to primary
  • Create hub/spoke structure with topic clusters

Cannibalization Report Format

Conflict: [Keyword]
Competing Pages:
- Page A: [URL] | Focus: [angle]
- Page B: [URL] | Focus: [angle]

Resolution:
- Consolidate / Differentiate / Canonical
- Specific actions for each page

Guiding Principles

  1. User-first - Optimize for humans, not just algorithms
  2. Natural integration - Keywords should flow naturally
  3. Value addition - Every change should add value
  4. Extractability - Make key info easy to find/cite
  5. Authenticity - Maintain author voice and expertise

平台分布

Codex

35.08%
按下载量换算102

Claude

28.79%
按下载量换算83

Cursor

18.08%
按下载量换算52

Gemini CLI

9.16%
按下载量换算27

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。

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