Content Engine
ericrisco/rsc-harness
A skill your agent uses when a content operation needs a SYSTEM: a dated editorial calendar built top-down from pillars, plus the stage gates, briefs, WIP limits and 1:10 atomization plan that move…
SEO keyword research workflow for blog generation using Google Trends data.
Install SEO Keyword Research
$ npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install Varnan-Tech/opendirectory seo-keyword-research --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/Varnan-Tech/opendirectory.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/google-trends-api-skills/seo-keyword-research .claude/skills/seo-keyword-research && rm -rf skills-srcUse ~/.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/
Install the "seo-keyword-research" agent skill from https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-research into .claude/skills/seo-keyword-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "seo-keyword-research", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-researchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install Varnan-Tech/opendirectory seo-keyword-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Varnan-Tech/opendirectory.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/google-trends-api-skills/seo-keyword-research .agents/skills/seo-keyword-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "seo-keyword-research" agent skill from https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-research into .agents/skills/seo-keyword-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "seo-keyword-research", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install Varnan-Tech/opendirectory seo-keyword-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Varnan-Tech/opendirectory.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/google-trends-api-skills/seo-keyword-research .cursor/skills/seo-keyword-research && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "seo-keyword-research" agent skill from https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-research into .cursor/skills/seo-keyword-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "seo-keyword-research", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/Varnan-Tech/opendirectory.git --path skills/google-trends-api-skills/seo-keyword-research--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install Varnan-Tech/opendirectory seo-keyword-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Varnan-Tech/opendirectory.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/google-trends-api-skills/seo-keyword-research .gemini/skills/seo-keyword-research && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "seo-keyword-research" agent skill from https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-research into .gemini/skills/seo-keyword-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "seo-keyword-research", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install Varnan-Tech/opendirectory seo-keyword-researchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/Varnan-Tech/opendirectory.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/google-trends-api-skills/seo-keyword-research .github/skills/seo-keyword-research && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "seo-keyword-research" agent skill from https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-research into .github/skills/seo-keyword-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "seo-keyword-research", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install Varnan-Tech/opendirectory seo-keyword-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/Varnan-Tech/opendirectory.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/google-trends-api-skills/seo-keyword-research .opencode/skills/seo-keyword-research && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "seo-keyword-research" agent skill from https://github.com/Varnan-Tech/opendirectory/tree/main/skills/google-trends-api-skills/seo-keyword-research into .opencode/skills/seo-keyword-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "seo-keyword-research", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
seo-keyword-research- GitHub stars
- 674
- Token cost
- ~1.8k tokens
- SKILL.md length
- 625 words
- Files
- 4 (incl. scripts, references)
- Skills in repo
- 61
- Repo updated
- First seen
- Licence
- MIT
At a glance
SEO keyword research workflow for blog generation using Google Trends data.
- Works in 9 steps: Breakout Keywords — HIGHEST priority → High-Growth Keywords — VERY HIGH priority → Moderate-Growth Keywords — HIGH priority → …
- Writing blog posts
- SKILL.md covers When to Use, Core Principle, Keyword Priority System and SEO Research Workflow, plus 6 more sections
- Runs Python scripts from its folder; calls curl and python; reaches serpapi.com; needs SERPAPI_KEY
What it does
SEO Keyword Research is an agent skill from Varnan-Tech/opendirectory. 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.
Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/keyword-placement-guide.md`, `references/tech-blog-examples.md` and `scripts/blog_seo_research.py`).
It sits in Marketing & SEO, covering Keyword research, Content strategy and Blog and article writing. The repository describes itself as: AI Agent Skills built for Founders who hate Marketing. The licence is MIT.
When your agent uses it
- Writing blog posts
- Planning content calendars
- Optimizing articles for search engines
Example prompts
- “/seo-keyword-research”
Requirements
- Python 3
- A credential in SERPAPI_KEY
- Pre-approved tools (allowed-tools): Bash(python:*), Bash(curl:*), Read
Workflow steps
9 steps, taken from the step headings in SKILL.md.
- Breakout Keywords — HIGHEST priority
- High-Growth Keywords — VERY HIGH priority
- Moderate-Growth Keywords — HIGH priority
- Long-Tail Keywords — STRATEGIC priority
- Established Keywords — MODERATE priority
- Keyword Discovery (1 API call — REQUIRED)
- Content Structure (1 API call — REQUIRED)
- Trend Validation (1 API call — OPTIONAL)
- Generate Blog Outline
What it can do on your machine
Read from SKILL.md and the folder at commit 9bf5144. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
Bash(python:*)Bash(curl:*)Read
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
curlpython
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
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
SERPAPI_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
SEO Keyword Research loads about 1.8k tokens when it runs, and up to ~3.8k if it reads all its reference files. Until then it costs about 82 tokens; SKILL.md has 625 words of instructions outside code blocks.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from Varnan-Tech/opendirectory at commit 9bf5144, republished under its MIT licence (© Varnan-Tech). 625 words, ~1,757 tokens.
Download SKILL.mdSave it as
.claude/skills/seo-keyword-research/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- seo-keyword-research
- description
- 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.
- allowed-tools
- Bash(python:*), Bash(curl:*), Read
- license
- MIT
- metadata.author
- farizanjum
- metadata.version
- 2.0
- metadata.domain
- tech-developer-blogs
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 wellKeyword 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:
curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_QUERIES&date=today+3-m&api_key=$SERPAPI_KEY"From the response, extract:
- Breakout keywords → candidate primary keywords
- High-growth keywords (+100%) → secondary candidates
- Question-based queries → H3 headings and featured snippet targets
Select the primary keyword:
- First breakout keyword (if any)
- Else first high-growth keyword
- Else top query by score
Step 2: Content Structure (1 API call — REQUIRED)
Query RELATED_TOPICS with the same topic:
curl -s "https://serpapi.com/search?engine=google_trends&q=USER_TOPIC&data_type=RELATED_TOPICS&date=today+3-m&api_key=$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:
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 totalShow full SKILL.md (281 more words)Show less
Keyword Placement Rules
| Location | Rule |
|---|---|
| Title | Include primary keyword, max 60 chars |
| H1 | Same 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 paragraph | Primary keyword in first 100 words |
| Body content | Primary keyword 1-2% density, secondary 0.5-1% |
| Conclusion | Primary keyword once |
| Meta description | Primary + 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
| Strategy | Calls/Blog | Monthly Capacity |
|---|---|---|
| Minimal (recommended) | 2 | 125 blogs |
| Standard | 3 | 83 blogs |
| Complete | 4 | 62 blogs |
Default to 2 calls (RELATED_QUERIES + RELATED_TOPICS). Only add TIMESERIES or GEO_MAP when specifically needed.
Common Mistakes to Avoid
- Skipping research — writing without checking trends misses breakout opportunities
- Ignoring breakout keywords — using a generic term when a breakout variant exists
- Keyword stuffing — repeating keywords unnaturally; keep density at 1-2%
- No long-tail keywords — missing featured snippet and voice search opportunities
- Generic H2 headings — always use RELATED_TOPICS data for section structure
Example Script
For a complete working example, run:
python scripts/blog_seo_research.py "your blog topic"See scripts/blog_seo_research.py for the implementation.
References
- references/keyword-placement-guide.md — Detailed placement rules and examples
- references/tech-blog-examples.md — Real-world examples for tech/developer blogs
© Varnan-Tech, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (scripts, references) in skills/google-trends-api-skills/seo-keyword-research of Varnan-Tech/opendirectory.
- SKILL.md
- references/keyword-placement-guide.md
- references/tech-blog-examples.md
- scripts/blog_seo_research.py
Open the folder on GitHubat commit 9bf5144
Compare with similar skills
SEO Keyword Research 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| SEO Keyword Research this skillVarnan-Tech/opendirectory | 674 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Content Engineericrisco/rsc-harness | 180 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Article Writingericrisco/rsc-harness | 180 | — | ~2.9k | Automated safety check: Pass | MIT | |
| Ink Briefjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.2k | Automated safety check: Notes | MIT | |
| SEO Keyword ClusteringAgriciDaniel/claude-seo | 19k | 2 repos | ~3.3k | Automated safety check: Pass | MIT | |
| SEO Content Brief GeneratorAgriciDaniel/claude-seo | 19k | 2 repos | ~2.6k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about SEO Keyword Research
What does SEO Keyword Research do?
SEO keyword research workflow for blog generation using Google Trends data. SEO Keyword Research is an agent skill from Varnan-Tech/opendirectory. SEO keyword research workflow for blog generation using Google Trends data.
When should I use SEO Keyword Research?
SEO Keyword Research fits situations like: writing blog posts; planning content calendars; optimizing articles for search engines.
How do I install SEO Keyword Research in Claude Code?
Run `npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a claude-code`. Or copy the skill folder (skills/google-trends-api-skills/seo-keyword-research in Varnan-Tech/opendirectory) into .claude/skills/seo-keyword-research in your project. Claude Code loads it when a task matches its description.
How do I install SEO Keyword Research in Codex?
Run `npx skills add Varnan-Tech/opendirectory --skill seo-keyword-research -a codex`. Or copy the skill folder (skills/google-trends-api-skills/seo-keyword-research in Varnan-Tech/opendirectory) into .agents/skills/seo-keyword-research in your project. Codex loads it when a task matches its description.
Can I use SEO Keyword Research 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 Varnan-Tech/opendirectory --skill seo-keyword-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/seo-keyword-research, .gemini/skills/seo-keyword-research, .github/skills/seo-keyword-research and .opencode/skills/seo-keyword-research in your project.
What does SEO Keyword Research need to run?
Going by SKILL.md and its folder, SEO Keyword Research needs Python for the scripts in its folder, the command-line tools its instructions call (curl and python) and credentials named SERPAPI_KEY. Our summary lists: Python 3; A credential in SERPAPI_KEY. Its frontmatter pre-approves these tools: Bash(python:*), Bash(curl:*), Read.
Does SEO Keyword Research access the network?
SKILL.md names 1 domain. In commands or code: serpapi.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.
Is SEO Keyword Research 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does SEO Keyword Research use?
SEO Keyword Research is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does SEO Keyword Research use?
About 1.8k tokens (SKILL.md is roughly 7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.
What are the alternatives to SEO Keyword Research?
Skills that share tags, products or a category with SEO Keyword Research: Content Engine (ericrisco/rsc-harness, 180 stars), Article Writing (ericrisco/rsc-harness, 180 stars), Ink Brief (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and SEO Keyword Clustering (AgriciDaniel/claude-seo, 19k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains SEO Keyword Research?
Varnan-Tech (a GitHub organization) maintains it in Varnan-Tech/opendirectory, which has 674 GitHub stars. The repository holds 61 skills in this directory. The repository was last updated on October 10, 2026.
Source: Varnan-Tech/opendirectory on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.