Quickstart · ZensInk

Prerequisites — Python 3.10+ and a terminal. No pip install, no virtual environment, no API keys.

The 60-second tour

keyword_research → keyword_volume → search_performance → competitor_gap

Each tool feeds the next. Start with seeds, end with a content plan. Let’s walk through the first step.

Clone the repo

git clone https://github.com/respectevery01/zens-ink-seo-package.git
cd zens-ink-seo-package

The toolkit lives in zens_ink/. Each script is standalone — they share zero external dependencies beyond the Python standard library.

Run your first keyword search

python3 -m zens_ink keyword_research "ai tools"

You’ll see real suggestions pulled from Google Autocomplete — the same predictions you see when typing into Google’s search bar:

Output

ai tools for writing
ai tools for students
ai tools free
ai tools for coding
ai tools for productivity
ai tools for business
ai tools for marketing
ai tools 2026
best ai tools for small business
ai tools like chatgpt

Expand the search

Add --expand to dig deeper:

python3 -m zens_ink keyword_research "ai tools" --expand

This appends each letter of the alphabet (a–z) after your seed keyword, surfacing suggestions you’d never think of manually.

Input

1 seed

Queries

26 lookups

Output

150–300 keywords

Save to a file

Pipe the output for later use:

python3 -m zens_ink keyword_research "ai tools" --expand > my_keywords.txt

What just happened? — You discovered 150+ keywords that real humans type into Google — in under a minute, without spending a cent or signing up for anything.

Integrate with your tools

ZensInk is plain Python scripts — they run anywhere with a shell. Here's how to wire them into popular AI coding tools:

Drop the zens_ink/ folder and the Pro SKILL.md into your project root. Claude Code auto-discovers the skill and can run the full pipeline conversationally.

# Claude Code terminal
> Run keyword research for "fintech" and cluster the results

# Claude reads SKILL.md, runs keyword_research + keyword_cluster,
# and hands you back a prioritized content plan.

Clone the repo into your project and reference it in your .cursorrules file. Cursor figures out which script to call from context.

# .cursorrules
This project uses ZensInk for SEO.
Scripts are in ./zens_ink/
Run: python3 -m zens_ink <tool> <args>

Open the repo in Windsurf and use Cascade. The scripts output clean text that Cascade can parse and chain together.

# Windsurf Cascade
Run keyword_research with --expand for "python",
then pipe the output to keyword_volume

Install the Cline extension in VS Code, open the repo folder, give it terminal access. It runs each script and reads output files directly.

# Cline prompt
Run the full ZensInk pipeline:
1. keyword_research --expand
2. keyword_volume
3. competitor_gap
Save all outputs to ./seo-output/

Just run them. Any shell, any OS — macOS, Linux, WSL, even Termux on Android.

git clone https://github.com/respectevery01/zens-ink-seo-package.git
cd zens-ink-seo-package
python3 -m zens_ink keyword_research "your niche" --expand

What's next?

You now have a keyword list. The next question is always: **which of these actually get searched?** That's where search volume comes in.

Or jump ahead to competitor analysis to see what your rivals are already ranking for.