Agent skill

Serp Analyzer

by OpenClaudia in OpenClaudia/openclaudia-skills

Analyze Google search results (SERP) for any keyword. An agent skill from OpenClaudia/openclaudia-skills.

MITAuto-check passedProductivity & Automation

Install Serp Analyzer

skills CLI
$ npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install OpenClaudia/openclaudia-skills serp-analyzer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/serp-analyzer .claude/skills/serp-analyzer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
serp-analyzer
GitHub stars
713
Token cost
~3.6k tokens
SKILL.md length
1,074 words
Files
1
Skills in repo
74
Repo updated
First seen
Licence
MIT

At a glance

Analyze Google search results (SERP) for any keyword. An agent skill from OpenClaudia/openclaudia-skills.

  • Works in 7 steps: Collect SERP Data → Map SERP Features → Analyze Top 10 Results → …
  • The user says analyze the SERP
  • SKILL.md covers Prerequisites, Analysis Process, Output Format and Notes
  • Calls curl and jq; reaches serpapi.com and api.serpingapi.com; needs SERPINGAPI_API_KEY and SERPAPI_API_KEY

What it does

Serp Analyzer is an agent skill from OpenClaudia/openclaudia-skills. Analyze Google search results (SERP) for any keyword. Use when the user says "analyze the SERP", "what ranks for", "SERP analysis", "competitive analysis for keyword", "content brief", "what's ranking", "search results for", "who ranks for", or asks about ranking content patterns for a keyword.

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Productivity & Automation, covering Web search, Content strategy and Keyword research. It works with SerpApi. The repository describes itself as: 77 open-source marketing skills for Claude Code, Codex, and other AI coding agents. SEO, content, email, ads, analytics, and growth. The licence is MIT.

When your agent uses it

  • The user says analyze the SERP
  • Competitive analysis for keyword
  • Search results for
  • Asks about ranking content patterns for a keyword

Example prompts

  • “analyze the SERP”
  • “what ranks for”
  • “SERP analysis”
  • “/serp-analyzer”

Requirements

  • A credential in SEMRUSH_API_KEY
  • A credential in SERPAPI_API_KEY

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. Collect SERP Data
  2. Map SERP Features
  3. Analyze Top 10 Results
  4. Identify Patterns
  5. Find Content Gaps
  6. Analyze Competitive Positioning
  7. Generate Content Brief

What it can do on your machine

Read from SKILL.md and the folder at commit 28bf209. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • jq

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • serpapi.com
    • api.serpingapi.com
    • api.semrush.com
    • api.dataforseo.com

    Also links to:

    • serpingapi.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • SERPINGAPI_API_KEY
    • SERPAPI_API_KEY
    • DATAFORSEO_PASSWORD
    • SEMRUSH_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Serp Analyzer loads about 3.6k tokens when it runs. Until then it costs about 77 tokens; SKILL.md has 1,074 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~77
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from OpenClaudia/openclaudia-skills at commit 28bf209, republished under its MIT licence (© OpenClaudia). 1,074 words, ~3,643 tokens.

Download SKILL.mdSave it as .claude/skills/serp-analyzer/SKILL.md (or your agent's skills folder).
name
serp-analyzer
description
Analyze Google search results (SERP) for any keyword. Use when the user says "analyze the SERP", "what ranks for", "SERP analysis", "competitive analysis for keyword", "content brief", "what's ranking", "search results for", "who ranks for", or asks about ranking content patterns for a keyword.

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
  • SERPINGAPI_API_KEY - for real-time Google SERP data (free tier available)

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:

bash
# 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:

bash
# 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:

bash
# 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.

Method F: Serping API (if SERPINGAPI_API_KEY available)

Real-time Google SERP data as Serper-style JSON from a single endpoint. Get a key at https://serpingapi.com (free tier available) and set SERPINGAPI_API_KEY.

bash
# Real-time Google SERP data via Serping API (POST, JSON body)
curl -s -X POST "https://api.serpingapi.com/v1/search" \
  -H "X-API-Key: ${SERPINGAPI_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{"q": "{keyword}", "gl": "us", "hl": "en", "num": 20}'

The JSON response includes (sections appear only when Google returns them):

  • organic - Array of organic listings with position, title, link, snippet (sometimes sitelinks, date, rating)
  • peopleAlsoAsk - People Also Ask questions with question, snippet, title, link
  • answerBox - Featured snippet / direct answer
  • knowledgeGraph - Knowledge panel data with title, type, description, attributes
  • relatedSearches - Related search queries as { "query": ... }
  • searchParameters - Echo of the parameters the search ran with

Parse example:

bash
# Extract organic results
curl -s -X POST "https://api.serpingapi.com/v1/search" \
  -H "X-API-Key: ${SERPINGAPI_API_KEY}" -H "Content-Type: application/json" \
  -d '{"q": "{keyword}", "gl": "us", "hl": "en", "num": 20}' | \
  jq '.organic[] | {position, title, link, snippet}'

# Extract People Also Ask questions
curl -s -X POST "https://api.serpingapi.com/v1/search" \
  -H "X-API-Key: ${SERPINGAPI_API_KEY}" -H "Content-Type: application/json" \
  -d '{"q": "{keyword}", "gl": "us", "hl": "en", "num": 20}' | \
  jq '.peopleAlsoAsk[] | {question, snippet}'

# Check for featured snippet / knowledge graph
curl -s -X POST "https://api.serpingapi.com/v1/search" \
  -H "X-API-Key: ${SERPINGAPI_API_KEY}" -H "Content-Type: application/json" \
  -d '{"q": "{keyword}", "gl": "us", "hl": "en", "num": 20}' | \
  jq '{answerBox, knowledgeGraph: (.knowledgeGraph | {title, type, description})}'

Optional parameters: location (e.g. "Seattle, Washington, United States"), page (starting at 1), tbs for a time filter (qdr:d day, qdr:w week, qdr:m month, qdr:y year). Web search only — no ads, shopping, or local pack sections.

Errors come back as {"error": {"code": "...", "message": "..."}}: 401 invalid_api_key means the key is wrong or revoked; 429 quota_exceeded means the monthly quota is used up (resets on the 1st, UTC). In either case tell the user the specific error and fall back to Method B.

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
Show full SKILL.md (413 more words)Show less
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:

markdown
## 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:

markdown
# 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.

© OpenClaudia, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/serp-analyzer of OpenClaudia/openclaudia-skills.

Open the folder on GitHubat commit 28bf209

Compare with similar skills

Serp Analyzer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Serp Analyzer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Serp Analyzer this skillOpenClaudia/openclaudia-skills713—~3.6kAutomated safety check: PassMIT
SEO Content Brief GeneratorAgriciDaniel/claude-seo19k2 repos~2.6kAutomated safety check: PassMIT
Strategic SEO PlanningAgriciDaniel/claude-seo19k5 repos~1.1kAutomated safety check: PassMIT
Chatgpt Web Researchbear2u/my-skills932—~3.3kAutomated safety check: PassNone
Serp AnalysisMadAppGang/claude-code2851 repos~1kAutomated safety check: PassMIT
Blog OutlineAgriciDaniel/claude-blog2.3k1 repos~1.5kAutomated safety check: PassMIT

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Works with

Questions about Serp Analyzer

What does Serp Analyzer do?

Analyze Google search results (SERP) for any keyword. An agent skill from OpenClaudia/openclaudia-skills. Serp Analyzer is an agent skill from OpenClaudia/openclaudia-skills. Analyze Google search results (SERP) for any keyword.

When should I use Serp Analyzer?

Serp Analyzer fits situations like: the user says analyze the SERP; competitive analysis for keyword; search results for; asks about ranking content patterns for a keyword.

How do I install Serp Analyzer in Claude Code?

Run `npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a claude-code`. Or copy the skill folder (skills/serp-analyzer in OpenClaudia/openclaudia-skills) into .claude/skills/serp-analyzer in your project. Claude Code loads it when a task matches its description.

How do I install Serp Analyzer in Codex?

Run `npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a codex`. Or copy the skill folder (skills/serp-analyzer in OpenClaudia/openclaudia-skills) into .agents/skills/serp-analyzer in your project. Codex loads it when a task matches its description.

Can I use Serp Analyzer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/serp-analyzer, .gemini/skills/serp-analyzer, .github/skills/serp-analyzer and .opencode/skills/serp-analyzer in your project.

What does Serp Analyzer need to run?

Going by SKILL.md and its folder, Serp Analyzer needs the command-line tools its instructions call (curl and jq) and credentials named SERPINGAPI_API_KEY, SERPAPI_API_KEY, DATAFORSEO_PASSWORD and SEMRUSH_API_KEY. Our summary lists: A credential in SEMRUSH_API_KEY; A credential in SERPAPI_API_KEY.

Does Serp Analyzer access the network?

SKILL.md names 5 domains. In commands or code: serpapi.com, api.serpingapi.com, api.semrush.com and api.dataforseo.com; the agent is likely to contact these when it follows the instructions. As links in the text: serpingapi.com. This is read from the text; nothing was executed.

Is Serp Analyzer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Serp Analyzer use?

Serp Analyzer is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Serp Analyzer use?

About 3.6k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Serp Analyzer?

Skills that share tags, products or a category with Serp Analyzer: SEO Content Brief Generator (AgriciDaniel/claude-seo, 19k stars), Strategic SEO Planning (AgriciDaniel/claude-seo, 19k stars), Chatgpt Web Research (bear2u/my-skills, 932 stars) and Serp Analysis (MadAppGang/claude-code, 285 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Serp Analyzer?

OpenClaudia (a GitHub organization) maintains it in OpenClaudia/openclaudia-skills, which has 713 GitHub stars. The repository holds 74 skills in this directory. The repository was last updated on September 18, 2026.

Source: OpenClaudia/openclaudia-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.