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Agent Skill · 研究检索

serp-analyzer(serp 分析器)

serp-analyzer 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。

serp-analyzer 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,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 31,159。

总安装 2,752周安装 117GitHub Stars 406下载 964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:serp-analyzer(serp 分析器)
来源仓库:https://github.com/openclaudia/openclaudia-skills
仓库路径:skills/serp-analyzer
安装命令:
npx skills add https://github.com/openclaudia/openclaudia-skills --skill serp-analyzer
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/openclaudia/openclaudia-skills --skill serp-analyzer

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。

用于查找、检索和筛选相关信息。适合在需要根据关键词、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。

注意该技能属于研究检索类别,适用于 Codex、Claude、Cursor、Gemini CLI。

来源仓库内路径:skills/serp-analyzer

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/openclaudia/openclaudia-skills --skill serp-an…;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

serp-analyzer 怎么安装?

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

serp-analyzer 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。

serp-analyzer 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。

serp-analyzer 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。

serp-analyzer 用的人多吗?

公开数据是累计安装 2,752 次,近一周安装 117 次,下载 964 次,GitHub 406 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。

serp-analyzer 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

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。

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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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
serp-analyzer

serp 分析器 · 本页

2,752406需要联网未明确未通过

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

SERP Analyzer Skill

You are an expert SERP analyst. Given a target keyword, analyze what currently ranks in Google, identify content patterns, and produce an actionable content brief for outranking the competition.

Prerequisites

Optional API keys for enriched data (the skill can work without any of them using web search):

  • SEMRUSH_API_KEY - for keyword and organic results data
  • SERPAPI_API_KEY - for real-time Google SERP data including SERP features
  • DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD - for advanced SERP data

Analysis Process

Step 1: Collect SERP Data

Use multiple data sources to build a complete SERP picture:

Method A: SemRush API (if available)

# Get organic results for keyword
https://api.semrush.com/?type=phrase_organic&key={KEY}&phrase={keyword}&database=us&export_columns=Dn,Ur,Fk,Fp&display_limit=20

Columns: Dn=Domain, Ur=URL, Fk=SERP Features, Fp=Position

Method B: Web Search (always do this) Use the WebSearch tool to search for the exact keyword. This gives you real-time SERP data.

Method C: Fetch top results Use WebFetch on the top 5-10 ranking URLs to analyze actual content.

Method D: SerpAPI (if SERPAPI_API_KEY available)

Real-time Google SERP data with structured SERP features:

# Real-time Google SERP data via SerpAPI
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en"

The JSON response includes:

  • organic_results - Array of organic listings with position, title, link, snippet, displayed_link
  • related_questions - People Also Ask questions with question, snippet, title, link
  • knowledge_graph - Knowledge panel data with title, description, entity_type, and attributes
  • shopping_results - Product listings (if present) with title, price, link, source
  • local_results - Local Pack listings (if present) with title, address, rating, reviews
  • inline_images - Image pack results
  • answer_box - Featured snippet content with type (paragraph, list, table), snippet, title
  • related_searches - Related search queries

Parse example:

# Extract organic results
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en" | \
  jq '.organic_results[] | {position, title, link, snippet}'

# Extract People Also Ask questions
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en" | \
  jq '.related_questions[] | {question, snippet}'

# Check for knowledge graph
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=20&gl=us&hl=en" | \
  jq '.knowledge_graph | {title, description, entity_type}'

SerpAPI is especially useful for mapping SERP features in Step 2, as it returns structured data for every feature type.

Method E: DataForSEO (if DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD available)

Advanced SERP data with detailed item types and ranking metrics:

# DataForSEO SERP API
curl -s -X POST "https://api.dataforseo.com/v3/serp/google/organic/live/advanced" \
  -H "Authorization: Basic $(echo -n '${DATAFORSEO_LOGIN}:${DATAFORSEO_PASSWORD}' | base64)" \
  -H "Content-Type: application/json" \
  -d '[{"keyword": "{keyword}", "location_code": 2840, "language_code": "en"}]'

The response provides:

  • result[0].items - Array of all SERP items, each with a type field:

- "organic" - Standard organic results with url, title, description, rank_group, rank_absolute - "featured_snippet" - Featured snippet with description, url, type (paragraph/list/table) - "people_also_ask" - PAA questions with items[].title (the questions) - "knowledge_graph" - Knowledge panel data - "local_pack" - Local results - "shopping" - Shopping results - "video" - Video carousel items - "images" - Image pack - "related_searches" - Related search suggestions

  • result[0].item_types - Array listing which SERP feature types are present (useful for Step 2 feature mapping)
  • result[0].se_results_count - Total search results count

Location codes: 2840 = US, 2826 = UK, 2124 = Canada, 2036 = Australia. Change location_code for geo-targeted analysis.

Step 2: Map SERP Features

Document every SERP feature present for this keyword:

FeaturePresent?Who owns it?Can you win it?
Featured SnippetYes/No{domain}{assessment}
People Also AskYes/No{list questions}-
Knowledge PanelYes/No{entity}-
Image PackYes/No{position in SERP}{assessment}
Video CarouselYes/No{platforms}{assessment}
Local PackYes/No-{assessment}
Shopping ResultsYes/No-{assessment}
News ResultsYes/No{sources}{assessment}
SitelinksYes/No{domain}-
Reviews/StarsYes/No{domains}{assessment}
FAQ Rich ResultsYes/No{domains}{assessment}
BreadcrumbsYes/No{domains}-

SERP Intent Signal Analysis:

  • Mostly blog posts/guides = Informational intent
  • Mostly product/service pages = Transactional intent
  • Mix of reviews + product pages = Commercial investigation
  • Brand homepage + login pages = Navigational intent
  • Featured snippet present = Strong informational component

Step 3: Analyze Top 10 Results

For each of the top 10 organic results, fetch and analyze:

FactorWhat to measure
URLFull URL
DomainDomain authority/reputation
Title tagExact title, length, keyword placement
Meta descriptionExact description, length, call-to-action
Content typeBlog post, landing page, tool, directory, video, etc.
Word countTotal content length
Heading structureH1, number of H2s/H3s, heading keywords
Content formatListicle, how-to, comparison, guide, definition, etc.
VisualsNumber of images, videos, infographics, tables
DatePublished date, last updated date
AuthorNamed author, credentials shown
Unique angleWhat differentiates this from others
Internal linksNumber of internal links
External linksNumber of outbound links, sources cited
Schema markupTypes of structured data used
Reading levelApproximate Flesch-Kincaid grade level

Step 4: Identify Patterns

After analyzing all top 10 results, find commonalities:

Content Pattern Analysis:

## Content Patterns for "{keyword}"

### Dominant Content Type: {type}
{X} of 10 results are {blog posts/landing pages/tools/etc.}

### Average Metrics:
- Word count: {average} (range: {min}-{max})
- Number of headings: {average}
- Number of images: {average}
- Number of links (internal): {average}
- Number of links (external): {average}

### Common Topics Covered:
1. {topic} - covered by {X}/10 results
2. {topic} - covered by {X}/10 results
3. {topic} - covered by {X}/10 results
...

### Common H2 Headings:
1. "{heading}" or similar - used by {X}/10
2. "{heading}" or similar - used by {X}/10
...

### Featured Snippet Format:
Type: {paragraph/list/table/video}
Content: {what the snippet shows}
How to win it: {specific advice}

Step 5: Find Content Gaps

Identify what the top results are MISSING:

  • Topics mentioned by only 1-2 results (opportunity to be comprehensive)
  • Outdated information (opportunity for freshness)
  • Missing media types (no videos, no infographics, no interactive tools)
  • Missing perspectives (no expert quotes, no data, no case studies)
  • Unanswered "People Also Ask" questions
  • Missing schema markup types
  • Poor user experience (slow, no mobile optimization, intrusive ads)

Step 6: Analyze Competitive Positioning

For each top 5 competitor, create a positioning map:

Competitor 1 ({domain}): {Positioning summary - e.g., "Beginner-friendly, surface-level guide"}
  Strengths: {what they do well}
  Weaknesses: {what they miss or do poorly}

Competitor 2 ({domain}): {Positioning summary}
  Strengths: ...
  Weaknesses: ...

Find your differentiation angle:

  • Can you be more comprehensive? (10x content)
  • Can you be more actionable? (templates, tools, checklists)
  • Can you be more current? (latest data, 2025 updates)
  • Can you be more authoritative? (expert interviews, original research)
  • Can you serve a different sub-audience? (beginners vs. advanced)
  • Can you provide a unique format? (interactive tool vs. blog post)

Step 7: Generate Content Brief

Produce a complete content brief based on the analysis:

# Content Brief: {Target Keyword}

## Target Keyword
- **Primary:** {keyword} (Volume: {vol}, KD: {kd})
- **Secondary:** {keyword2}, {keyword3}, {keyword4}
- **Long-tail:** {keyword5}, {keyword6}

## Search Intent
**Primary intent:** {Informational/Commercial/Transactional}
**User goal:** {What the searcher wants to accomplish}
**Stage in funnel:** {Awareness/Consideration/Decision}

## Content Specifications

| Spec | Recommendation | Reasoning |
|------|---------------|-----------|
| Content type | {blog/landing/tool} | {X}/10 results are this type |
| Word count | {target} words | Top 3 average {avg}, aim for {target} |
| Format | {listicle/how-to/guide} | Dominant format in SERP |
| Reading level | Grade {X} | Match audience expectation |
| Visuals | {X} images, {X} custom graphics | Top results average {Y} |
| Videos | {Yes/No - embed or create} | {Reasoning} |

## Title Tag Recommendations
Write 3 options following these patterns from top results:
1. "{Title option 1}" ({length} chars)
2. "{Title option 2}" ({length} chars)
3. "{Title option 3}" ({length} chars)

## Meta Description Recommendations
1. "{Meta option 1}" ({length} chars)
2. "{Meta option 2}" ({length} chars)

## Recommended Outline

### H1: {Heading}

### H2: {Section 1 - from pattern analysis}
- Key points to cover: {points}
- Data/examples needed: {specifics}

### H2: {Section 2}
- Key points: ...

### H2: {Section 3}
...

### H2: FAQ
- {Question from People Also Ask}
- {Question from People Also Ask}
- {Question from gap analysis}

## Content Gaps to Exploit
1. **{Gap}** - Only {X}/10 competitors cover this. Include {specific content}.
2. **{Gap}** - No competitors have {data/tool/visual}. Create {specific asset}.
3. **{Gap}** - Top results are outdated on {topic}. Include {current data}.

## Schema Markup to Include
- {Type}: {Brief description of properties}
- {Type}: {Brief description}

## Internal Linking Targets
- Link TO this page from: {related pages on your site}
- Link FROM this page to: {related pages on your site}

## Differentiation Strategy
{2-3 sentences on how this content will stand out from current SERP}

Output Format

Always present:

  1. SERP Overview - Feature map and intent analysis
  2. Top 10 Analysis Table - Key metrics for each result
  3. Pattern Summary - What the SERP rewards
  4. Content Gaps - Opportunities to differentiate
  5. Content Brief - Complete brief ready for a writer

Notes

  • If you cannot fetch a URL (paywall, auth, blocking), note it and work with available data.
  • Always note the date of analysis. SERPs change; this is a snapshot.
  • For local keywords, note if the Local Pack dominates (this changes the strategy significantly).
  • If the SERP shows extreme domain authority concentration (all DR 90+ sites), flag this as a difficulty indicator regardless of KD score.
  • For "Your Money or Your Life" (YMYL) topics (health, finance, legal), note the elevated E-E-A-T requirements.

平台分布

Codex

38.53%
按下载量换算371

Claude

29.44%
按下载量换算284

Cursor

18.8%
按下载量换算181

Gemini CLI

10.45%
按下载量换算101

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

未通过

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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