Seo Keyword Research logo

Seo Keyword Research

Organization
Varnan-Tech
seo-keyword-research

SEO keyword research workflow for blog generation using Google Trends data. Use when writing blog posts, planning content calendars, or optimizing articles for search engines. Finds breakout keywords, builds content structure, and generates SEO-optimized blog outlines targeting tech and developer audiences.

Overview

PublisherVarnan-Tech
Repositoryopendirectory
Skill nameseo-keyword-research
Stars
674
Forks
74
Bundled files
3
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.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Varnan-Tech on GitHub. Read the source before you install it.

Installation

Install the Seo 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/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-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/Varnan-Tech/opendirectory.git /tmp/opendirectory
mkdir -p .claude/skills
cp -r /tmp/opendirectory/skills/google-trends-api-skills/seo-keyword-research .claude/skills/seo-keyword-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Seo 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 Seo 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 Seo 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.

SEO Keyword Research Skill

Find trending, high-opportunity keywords BEFORE writing blog content. This skill turns generic blog topics into SEO-optimized content that ranks.

When to Use

  • User asks to write a blog post or article
  • User wants keyword research for a topic
  • User needs a content calendar or content plan
  • User wants to optimize existing content for SEO
  • Any blog generation task for a tech/developer-focused audience

Core Principle

Always research keywords BEFORE generating blog content.

BAD:  Write blog -> Hope it ranks -> Usually doesn't
GOOD: Research keywords -> Find breakout opportunity -> Write optimized blog -> Ranks well

Keyword Priority System

When analyzing Google Trends RELATED_QUERIES results, prioritize keywords in this order:

1. Breakout Keywords — HIGHEST priority

  • formatted_value: "Breakout" = 5000%+ growth
  • Very low competition (trend is new)
  • Use as PRIMARY blog keyword
  • Create content IMMEDIATELY (first-mover advantage)

2. High-Growth Keywords — VERY HIGH priority

  • formatted_value: "+100%" or higher
  • Low to moderate competition
  • Use as primary or strong secondary keyword
  • Create content within 2-4 weeks

3. Moderate-Growth Keywords — HIGH priority

  • formatted_value: "+50%" to "+99%"
  • Moderate competition
  • Use as secondary keywords in body content

4. Long-Tail Keywords — STRATEGIC priority

  • Question-based queries (how, what, why, when, where)
  • Low competition, high conversion
  • Use as H3 headings — target featured snippets and voice search

5. Established Keywords — MODERATE priority

  • Top queries with stable interest, high competition
  • Use in body content, not as primary target

SEO Research Workflow

Step 1: Keyword Discovery (1 API call — REQUIRED)

Query RELATED_QUERIES with the user's blog topic:

bash
curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_QUERIES&date=today+3-m&api_key=$SERPAPI_KEY"

From the response, extract:

  • Breakout keywords → candidate primary keywords
  • High-growth keywords (+100%) → secondary candidates
  • Question-based queries → H3 headings and featured snippet targets

Select the primary keyword:

  1. First breakout keyword (if any)
  2. Else first high-growth keyword
  3. Else top query by score

Step 2: Content Structure (1 API call — REQUIRED)

Query RELATED_TOPICS with the same topic:

bash
curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_TOPICS&date=today+3-m&api_key=$SERPAPI_KEY"

Extract topic titles from rising + top results. These become your H2 section headings (pick 3-5).

Step 3: Trend Validation (1 API call — OPTIONAL)

Only if choosing between multiple candidate keywords or validating viability:

bash
curl -s "https://serpapi.com/search?engine=google_trends&q=KEYWORD&data_type=TIMESERIES&date=today+12-m&api_key=$SERPAPI_KEY"

Compare recent 2-month average vs. earlier 2-month average. If recent > earlier, trend is rising — proceed. If declining, consider a different keyword.

Step 4: Generate Blog Outline

Build the outline using this structure:

Title: [Primary Keyword] — [Benefit/Number] [Year]
  (max 60 characters, must include primary keyword)

Meta Description: (150-160 chars, primary + 1-2 secondary keywords)

# [H1 — same as or variation of title]

## Introduction (150 words)
  - Primary keyword in first 100 words
  - Hook with a problem or question
  - Preview what they'll learn

## [H2: Related Topic 1 from Step 2]
### [H3: Long-tail question from Step 1]
  Content answering the question (150-200 words)
### [H3: Another long-tail question]
  Content (150-200 words)

## [H2: Related Topic 2]
### [H3: Long-tail question]
### [H3: Long-tail question]

## [H2: Related Topic 3]
### [H3: Long-tail question]
### [H3: Long-tail question]

## Conclusion (100 words)
  - Summarize key points
  - Primary keyword mentioned once
  - Call-to-action

Target: 1500-2500 words total

Keyword Placement Rules

LocationRule
TitleInclude primary keyword, max 60 chars
H1Same as title or slight variation
H2 headings (3-5)Use related topics, natural language
H3 headings (8-12)Use long-tail keywords, question format
First paragraphPrimary keyword in first 100 words
Body contentPrimary keyword 1-2% density, secondary 0.5-1%
ConclusionPrimary keyword once
Meta descriptionPrimary + 1-2 secondary, 150-160 chars

Never keyword-stuff. Content must read naturally. Google penalizes unnatural repetition.

Quality Checklist

Before generating the blog, verify:

  • Found at least 1 breakout or +100% keyword (or justified using established keyword)
  • Have 3-5 H2 topics from RELATED_TOPICS
  • Have long-tail keywords for H3 headings
  • Primary keyword is specific enough to rank for
  • Blog structure follows the outline template above
  • Meta description is written (150-160 chars)
  • Target length is 1500-2500 words

If no breakout or high-growth keywords exist for the topic, inform the user that SEO opportunity is limited and suggest alternative angles or related topics that do have growth.

Budget Awareness

Free tier: 250 searches/month

StrategyCalls/BlogMonthly Capacity
Minimal (recommended)2125 blogs
Standard383 blogs
Complete462 blogs

Default to 2 calls (RELATED_QUERIES + RELATED_TOPICS). Only add TIMESERIES or GEO_MAP when specifically needed.

Common Mistakes to Avoid

  1. Skipping research — writing without checking trends misses breakout opportunities
  2. Ignoring breakout keywords — using a generic term when a breakout variant exists
  3. Keyword stuffing — repeating keywords unnaturally; keep density at 1-2%
  4. No long-tail keywords — missing featured snippet and voice search opportunities
  5. Generic H2 headings — always use RELATED_TOPICS data for section structure

Example Script

For a complete working example, run:

bash
python scripts/blog_seo_research.py "your blog topic"

See scripts/blog_seo_research.py for the implementation.

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Seo Keyword Research AI skill do?

SEO keyword research workflow for blog generation using Google Trends data. Use when writing blog posts, planning content calendars, or optimizing articles for search engines. Finds breakout keywords, builds content structure, and generates SEO-optimized blog outlines targeting tech and developer audiences.

Why use Seo Keyword Research on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Seo 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 Seo Keyword Research?

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

Is the Seo Keyword Research AI skill free?

Yes. It is published on GitHub by Varnan-Tech 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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