serp-analyzer — Claude Code skill: Google SERP analysis
A skill that pulls Google results for a keyword, spots content patterns among the top pages, and writes an actionable content brief.
npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a claude-codeWhat serp-analyzer does
Give the skill a keyword and it looks at what currently ranks in Google. It gathers organic results, People Also Ask questions, featured snippets, knowledge graph data, shopping and local pack listings, then hunts for shared patterns across the top pages. The output is a content brief you can hand to a writer.
It runs without any API keys using web search, but enriched data comes from SEMRUSH_API_KEY, SERPAPI_API_KEY, DATAFORSEO_LOGIN plus DATAFORSEO_PASSWORD, and SERPINGAPI_API_KEY. SerpAPI and Serping calls go through curl piped into jq; DataForSEO uses a POST with Basic auth. Geo targeting is set by location codes such as 2840 for the US.
Without keys the skill still runs, just with a thinner picture — no structured SERP feature data. To study actual page content it fetches the top 5–10 ranking URLs with WebFetch.
What it helps with
- pull Google results for a target keyword
- find content patterns shared by top-ranking pages
- build a content brief to outrank competitors
- extract People Also Ask questions for a topic
- check which SERP features a competitor holds
Example prompts for the agent
- analyze the SERP for "best crm for small business"
- what ranks for "seo audit checklist"
- build a content brief for the keyword "email marketing tools"
How to install serp-analyzer
1Skills CLI (Vercel)
npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a claude-code2Manually: put the files in place
~/.claude/skills/serp-analyzer/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/serp-analyzer/
1Skills CLI (Vercel)
npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a codex2Manually: put the files in place
.agents/skills/serp-analyzer/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a cursor2Manually: put the files in place
.cursor/skills/serp-analyzer/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install OpenClaudia/openclaudia-skills serp-analyzer2Manually: put the files in place
.github/skills/serp-analyzer/SKILL.md1Skills CLI (Vercel)
npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a gemini-cli2Platform CLI
gemini skills install https://github.com/OpenClaudia/openclaudia-skills --path skills/serp-analyzerManually: put the files in place
.devin/skills/serp-analyzer/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about serp-analyzer
How do I install serp-analyzer in Claude?
Run npx skills add OpenClaudia/openclaudia-skills --skill serp-analyzer -a claude-code. The skill lands at ~/.claude/skills/serp-analyzer/SKILL.md.
What does serp-analyzer do?
It analyzes Google results for a keyword — organic listings, People Also Ask, featured snippets, knowledge graph — and turns the patterns it finds into a content brief.
Do I need API keys for it to work?
No, it works through web search without keys. Adding SEMRUSH_API_KEY, SERPAPI_API_KEY, DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD, or SERPINGAPI_API_KEY gives richer structured data.
Source text
SKILL.md · MIT
---
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:
| Feature | Present? | Who owns it? | Can you win it? |
|---------|----------|-------------|-----------------|
| Featured Snippet | Yes/No | {domain} | {assessment} |
| People Also Ask | Yes/No | {list questions} | - |
| Knowledge Panel | Yes/No | {entity} | - |
| Image Pack | Yes/No | {position in SERP} | {assessment} |
| Video Carousel | Yes/No | {platforms} | {assessment} |
| Local Pack | Yes/No | - | {assessment} |
| Shopping Results | Yes/No | - | {assessment} |
| News Results | Yes/No | {sources} | {assessment} |
| Sitelinks | Yes/No | {domain} | - |
| Reviews/Stars | Yes/No | {domains} | {assessment} |
| FAQ Rich Results | Yes/No | {domains} | {assessment} |
| Breadcrumbs | Yes/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:
| Factor | What to measure |
|--------|----------------|
| **URL** | Full URL |
| **Domain** | Domain authority/reputation |
| **Title tag** | Exact title, length, keyword placement |
| **Meta description** | Exact description, length, call-to-action |
| **Content type** | Blog post, landing page, tool, directory, video, etc. |
| **Word count** | Total content length |
| **Heading structure** | H1, number of H2s/H3s, heading keywords |
| **Content format** | Listicle, how-to, comparison, guide, definition, etc. |
| **Visuals** | Number of images, videos, infographics, tables |
| **Date** | Published date, last updated date |
| **Author** | Named author, credentials shown |
| **Unique angle** | What differentiates this from others |
| **Internal links** | Number of internal links |
| **External links** | Number of outbound links, sources cited |
| **Schema markup** | Types of structured data used |
| **Reading level** | Approximate 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 · OpenClaudia/openclaudia-skills