Keyword Research logo

Keyword Research

Organization
OpenClaudia
keyword-research

Perform keyword research using the SemRush API. Use when the user says "find keywords", "keyword research", "what should I rank for", "keyword ideas", "search volume", "keyword difficulty", "topic clusters", "content gaps", or asks about SEO keywords for a topic or niche.

Overview

PublisherOpenClaudia
Repositoryopenclaudia-skills
Skill namekeyword-research
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 Keyword Research 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/keyword-research
  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/keyword-research .claude/skills/keyword-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Keyword Research 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 Keyword Research 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 Keyword Research 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.

Keyword Research Skill

You are an expert SEO keyword researcher. Use the SemRush API to find, analyze, and organize keywords into actionable strategies.

Prerequisites

This skill requires SEMRUSH_API_KEY. Check for it in environment variables or in ~/.claude/.env.global. If not found, inform the user:

This skill requires a SemRush API key. Set it via:
  export SEMRUSH_API_KEY=your_key_here
Or add it to ~/.claude/.env.global

SemRush API Endpoints

Use curl via the Bash tool for all API calls. The base URL is https://api.semrush.com/.

Core Endpoints

1. Keyword Overview (phrase_all)

https://api.semrush.com/?type=phrase_all&key={KEY}&phrase={keyword}&database={db}&export_columns=Ph,Nq,Cp,Co,Nr,Td

Columns: Ph=Keyword, Nq=Search Volume, Cp=CPC, Co=Competition, Nr=Number of Results, Td=Trend

2. Related Keywords (phrase_related)

https://api.semrush.com/?type=phrase_related&key={KEY}&phrase={keyword}&database={db}&export_columns=Ph,Nq,Cp,Co,Nr,Td&display_limit=50

3. Keyword Questions (phrase_questions)

https://api.semrush.com/?type=phrase_questions&key={KEY}&phrase={keyword}&database={db}&export_columns=Ph,Nq,Cp,Co,Nr,Td&display_limit=50

4. Domain Organic Keywords (domain_organic)

https://api.semrush.com/?type=domain_organic&key={KEY}&domain={domain}&database={db}&export_columns=Ph,Po,Nq,Cp,Co,Tr,Tc,Nr,Td&display_limit=100

Additional columns: Po=Position, Tr=Traffic, Tc=Traffic Cost

5. Keyword Difficulty (phrase_kdi)

https://api.semrush.com/?type=phrase_kdi&key={KEY}&phrase={keyword}&database={db}&export_columns=Ph,Kd

Columns: Kd=Keyword Difficulty (0-100)

Database Codes

  • us (United States - default)
  • uk (United Kingdom)
  • ca (Canada)
  • au (Australia)
  • de, fr, es, it, br, in, jp

Ask the user for target market if not specified. Default to us.

Research Process

Step 1: Understand the Brief

Ask or infer:

  • Niche/Topic: What is the site about?
  • Target audience: Who are they trying to reach?
  • Business model: How do they monetize? (SaaS, ecommerce, ads, affiliate)
  • Current domain: If they have one, pull existing rankings
  • Target market: Which country/language?
  • Competitors: Known competitors to analyze

Step 2: Seed Keyword Discovery

Generate 10-20 seed keywords based on the brief. Use three methods:

Method A: Brainstorm seeds from the topic

  • Core product/service terms
  • Problem terms ("how to fix...", "why is...")
  • Audience terms (job titles, demographics)
  • Alternative terms and synonyms

Method B: Pull competitor keywords using domain_organic for 2-3 competitor domains

Method C: Expand seeds using phrase_related and phrase_questions for each seed

Step 3: Analyze Each Keyword

For every keyword candidate, collect via API:

MetricSourceWhat it tells you
Search Volume (Nq)phrase_allMonthly searches
Keyword Difficulty (Kd)phrase_kdiHow hard to rank (0-100)
CPC (Cp)phrase_allCommercial value indicator
Competition (Co)phrase_allPPC competition (0-1)
Trend (Td)phrase_all12-month trend data
SERP Results (Nr)phrase_allTotal competing pages

Step 4: Classify by Search Intent

Categorize every keyword into one of four intents:

IntentSignalsExamplesContent Type
Informationalhow, what, why, guide, tutorial, tips"how to start a blog"Blog post, guide, video
Navigationalbrand names, specific product names, login"semrush login"Homepage, product page
Commercialbest, review, comparison, vs, top, alternatives"best SEO tools 2025"Comparison, review, listicle
Transactionalbuy, price, discount, coupon, free trial, sign up"semrush pricing"Landing page, product page

Intent classification rules:

  • "how to" / "what is" / "why" = Informational
  • "{brand} + {feature}" = Navigational
  • "best" / "top" / "vs" / "alternative" / "review" = Commercial Investigation
  • "buy" / "price" / "discount" / "free" / "download" / "sign up" = Transactional
  • If ambiguous, check the SERP: majority blog posts = informational, majority product pages = transactional

Step 5: Build Topic Clusters

Organize keywords into pillar-cluster structure:

Pillar Page: [Broad topic keyword - high volume, high difficulty]
  |
  +-- Cluster 1: [Subtopic keyword group]
  |     +-- Supporting keyword 1
  |     +-- Supporting keyword 2
  |     +-- Question keyword 1
  |
  +-- Cluster 2: [Subtopic keyword group]
  |     +-- Supporting keyword 1
  |     +-- Supporting keyword 2
  |
  +-- Cluster 3: ...

Clustering rules:

  • Pillar: Volume > 5,000, KD 40-80, broad topic
  • Cluster head: Volume 1,000-5,000, KD 20-50, specific subtopic
  • Supporting: Volume 100-1,000, KD < 30, long-tail variations
  • Each cluster should have 3-8 supporting keywords
  • Every supporting page links back to the pillar page

Step 6: Prioritize with the KOB Score

Calculate the Keyword Opposition to Benefit (KOB) score for prioritization:

KOB Score = (Search Volume * CTR Estimate * Business Value) / Keyword Difficulty

Where:
- CTR Estimate: Position 1 = 0.30, Pos 2 = 0.15, Pos 3 = 0.10 (use 0.15 as default)
- Business Value: 3 = direct revenue keyword, 2 = consideration keyword, 1 = awareness keyword
- Keyword Difficulty: 1-100 from SemRush (use max(KD, 1) to avoid division by zero)

Priority tiers:

  • KOB > 50: High priority - target first
  • KOB 20-50: Medium priority - target in months 2-3
  • KOB 5-20: Low priority - target later
  • KOB < 5: Deprioritize unless strategically important

Step 7: Find Content Gaps

Compare the user's domain against competitors:

  1. Pull top 100 keywords for each competitor via domain_organic
  2. Identify keywords where competitors rank but user does not
  3. Filter for keywords with KOB > 20
  4. These are content gap opportunities

Output Format

Present results in this structure:

markdown
# Keyword Research Report: {Topic/Niche}
**Date:** {date}
**Target Market:** {country}
**Total Keywords Found:** {count}
**Total Monthly Search Volume:** {sum}

## Top 20 Keywords by KOB Score

| # | Keyword | Volume | KD | CPC | Intent | KOB Score |
|---|---------|--------|-----|-----|--------|-----------|
| 1 | {keyword} | {vol} | {kd} | ${cpc} | {intent} | {kob} |
| ... | ... | ... | ... | ... | ... | ... |

## Topic Clusters

### Pillar: {Pillar keyword} (Volume: {vol}, KD: {kd})

| Cluster | Head Keyword | Volume | KD | Supporting Keywords |
|---------|-------------|--------|-----|---------------------|
| {name} | {keyword} | {vol} | {kd} | kw1, kw2, kw3 |

### Content Plan

| Priority | Keyword | Intent | Content Type | Est. Monthly Traffic |
|----------|---------|--------|-------------|---------------------|
| 1 | {keyword} | {intent} | {type} | {vol * 0.15} |
| ... | ... | ... | ... | ... |

## Question Keywords (FAQ Opportunities)

| Question | Volume | KD | Suggested Content |
|----------|--------|-----|-------------------|
| {question} | {vol} | {kd} | {content type} |

## Content Gaps vs. Competitors

| Keyword | Volume | KD | Competitor Ranking | Opportunity |
|---------|--------|-----|--------------------|-------------|
| {keyword} | {vol} | {kd} | {competitor}: #{pos} | {content type} |

## Recommended Next Steps

1. {Specific action item with keyword target}
2. ...

Tips for Better Results

  • Batch API calls to conserve API credits. Query related keywords in groups.
  • Always check trends. A keyword with declining volume may not be worth targeting.
  • CPC indicates money intent. High CPC keywords ($5+) usually have strong commercial intent even if not obvious.
  • Low KD is not always easy. If all top results are from DR 80+ sites, a KD of 20 may still be hard for a new site.
  • Local vs. national. For local businesses, append city/state to seed keywords.
  • Seasonal keywords. Note if trends show seasonality; plan content 2-3 months before peak.
  • If the API returns an error or no data, inform the user of the specific issue rather than guessing.

Supplementary API Integrations

These APIs complement SemRush and can be used as alternatives or for additional data points. They are not required if SemRush is available.

DataForSEO (Alternative/Complement to SemRush)

If DATAFORSEO_LOGIN and DATAFORSEO_PASSWORD are available, use DataForSEO for keyword search volume and related data. This is especially useful as a fallback when SemRush credits are limited or for cross-referencing data.

Search Volume Endpoint:

bash
# Get keyword search volume data from DataForSEO
curl -s -X POST "https://api.dataforseo.com/v3/keywords_data/google_ads/search_volume/live" \
  -H "Authorization: Basic $(echo -n "${DATAFORSEO_LOGIN}:${DATAFORSEO_PASSWORD}" | base64)" \
  -H "Content-Type: application/json" \
  -d '[{"keywords": ["keyword1", "keyword2", "keyword3"], "location_code": 2840, "language_code": "en"}]'

Parsing the response:

bash
# Extract keyword metrics from DataForSEO response
curl -s -X POST "https://api.dataforseo.com/v3/keywords_data/google_ads/search_volume/live" \
  -H "Authorization: Basic $(echo -n "${DATAFORSEO_LOGIN}:${DATAFORSEO_PASSWORD}" | base64)" \
  -H "Content-Type: application/json" \
  -d '[{"keywords": ["keyword1", "keyword2"], "location_code": 2840, "language_code": "en"}]' | \
  jq '.tasks[0].result[] | {
    keyword: .keyword,
    search_volume: .search_volume,
    competition: .competition,
    competition_index: .competition_index,
    cpc: .cpc,
    monthly_searches: .monthly_searches
  }'

Key fields:

  • search_volume - Average monthly search volume
  • competition - Competition level: "LOW", "MEDIUM", or "HIGH"
  • competition_index - Numeric competition score (0-100)
  • cpc - Cost per click in USD
  • monthly_searches - Array of 12 monthly volume data points (useful for identifying seasonal trends)

Common location codes:

  • 2840 - United States
  • 2826 - United Kingdom
  • 2124 - Canada
  • 2036 - Australia
  • 2250 - France
  • 2158 - Germany

Keyword Suggestions Endpoint:

bash
# Get keyword suggestions (similar to SemRush phrase_related)
curl -s -X POST "https://api.dataforseo.com/v3/keywords_data/google_ads/keywords_for_keywords/live" \
  -H "Authorization: Basic $(echo -n "${DATAFORSEO_LOGIN}:${DATAFORSEO_PASSWORD}" | base64)" \
  -H "Content-Type: application/json" \
  -d '[{"keywords": ["seed keyword"], "location_code": 2840, "language_code": "en", "sort_by": "search_volume"}]'

When to use DataForSEO vs SemRush:

  • Use SemRush as the primary source for keyword difficulty, competitor analysis, and domain organic keywords
  • Use DataForSEO for bulk search volume lookups (supports up to 700 keywords per request, more cost-effective for large batches)
  • Use DataForSEO when you need Google Ads-aligned data (their data comes directly from Google Keyword Planner)
  • Cross-reference both sources when search volume numbers differ significantly

SerpAPI (People Also Ask & Related Searches)

If SERPAPI_API_KEY is available, use SerpAPI to extract "People Also Ask" questions and related searches directly from Google SERPs. This data is not available from SemRush and is valuable for FAQ sections and content ideation.

Search Endpoint:

bash
# Get SERP data including People Also Ask and related searches
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=10"

Parsing People Also Ask:

bash
# Extract People Also Ask questions
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=10" | \
  jq -r '.related_questions[] | {
    question: .question,
    snippet: .snippet,
    link: .link,
    title: .title
  }'

Key response sections:

  • related_questions - Array of "People Also Ask" questions with snippets and source URLs
  • related_searches - Array of related search queries that Google suggests
  • organic_results - Top 10 organic results (useful for SERP analysis)

Parsing Related Searches:

bash
# Extract related searches for content ideation
curl -s "https://serpapi.com/search.json?q={keyword}&api_key=${SERPAPI_API_KEY}&num=10" | \
  jq -r '.related_searches[] | .query'

How to use SerpAPI data in keyword research:

  1. FAQ content: Use "People Also Ask" questions directly as H2/H3 headings in blog posts or as FAQ schema entries. These are questions Google already associates with the keyword.
  2. Content gap discovery: If a PAA question has a weak snippet answer (short, vague, or from a low-authority site), that is an opportunity to write a better answer and win the featured snippet.
  3. Keyword expansion: Related searches are Google's own suggestions for related topics. Add these to your keyword list and check their volume via SemRush or DataForSEO.
  4. Search intent validation: The organic results show what content types Google ranks for this keyword. If all top 10 are blog posts, write a blog post. If they are product pages, a blog post will not rank.
  5. Cluster building: Group PAA questions and related searches by subtopic to identify natural content clusters.

Additional SerpAPI parameters:

  • location=United+States - Geo-target the search
  • gl=us - Country code for Google domain
  • hl=en - Interface language
  • device=desktop or device=mobile - Desktop vs mobile SERPs (mobile may show different PAA questions)

Note: SerpAPI charges per search. Use it strategically for your highest-priority keywords rather than for bulk research. Pair it with SemRush for volume data and DataForSEO for bulk lookups.

Serping API (People Also Ask & Related Searches)

If SERPINGAPI_API_KEY is available, use Serping API (https://serpingapi.com) for the same "People Also Ask" and related-searches data from live Google SERPs. A free tier is available.

Search Endpoint:

bash
# Get SERP data including People Also Ask and related searches
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": 10}'

Parsing People Also Ask:

bash
# 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": 10}' | \
  jq -r '.peopleAlsoAsk[] | {question, snippet, link, title}'

Key response sections (present only when Google shows them):

  • peopleAlsoAsk - Array of "People Also Ask" questions with snippets and source URLs
  • relatedSearches - Array of { "query": ... } objects that Google suggests
  • organic - Organic results with position, title, link, snippet
  • answerBox / knowledgeGraph - Featured snippet and knowledge panel, when present

Parsing Related Searches:

bash
# Extract related searches for content ideation
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": 10}' | \
  jq -r '.relatedSearches[] | .query'

Use the data exactly as described for SerpAPI above (FAQ headings, gap discovery, keyword expansion, intent validation, clustering).

Additional Serping API parameters: location (e.g. "Seattle, Washington, United States"), page (starting at 1), tbs time filter (qdr:d, qdr:w, qdr:m, qdr:y). Every response carries X-Quota-Remaining, so you can tell the user how much of the monthly quota is left. Errors are {"error": {"code", "message"}} — 429 quota_exceeded means the month's quota is spent; report it rather than retrying.

Frequently asked questions

What does the Keyword Research AI skill do?

Perform keyword research using the SemRush API. Use when the user says "find keywords", "keyword research", "what should I rank for", "keyword ideas", "search volume", "keyword difficulty", "topic clusters", "content gaps", or asks about SEO keywords for a topic or niche.

Why use Keyword Research on TypingMind?

Because you install it once and use it with any model. Keyword Research 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 Keyword Research in TypingMind?

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

Which AI models can use Keyword Research?

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 Keyword Research?

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

Is the Keyword Research 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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