Serp Analyzer logo

Serp Analyzer

Organization
OpenClaudia
serp-analyzer

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.

Overview

PublisherOpenClaudia
Repositoryopenclaudia-skills
Skill nameserp-analyzer
Stars
714
Forks
60
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by OpenClaudia on GitHub. Read the source before you install it.

Installation

Install the Serp Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/serp-analyzer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OpenClaudia/openclaudia-skills.git /tmp/openclaudia-skills
mkdir -p .claude/skills
cp -r /tmp/openclaudia-skills/skills/serp-analyzer .claude/skills/serp-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Serp Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Serp Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Serp Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

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.

Frequently asked questions

What does the Serp Analyzer AI skill do?

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.

Why use Serp Analyzer on TypingMind?

Because you install it once and use it with any model. Serp Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Serp Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/serp-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Serp Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Serp Analyzer?

As many as you like. As long as a model supports skills, you can use Serp Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Serp Analyzer AI skill free?

Yes. It is published on GitHub by OpenClaudia under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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