Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ZensInk/zens-ink-seo-package --skill keyword-researchgit clone --depth 1 https://github.com/ZensInk/zens-ink-seo-packageWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/zensink/zens-ink-seo-package/keyword-research)<a href="https://agentmods.dev/skills/zensink/zens-ink-seo-package/keyword-research"><img src="https://agentmods.dev/badge/skills/zensink/zens-ink-seo-package/keyword-research/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/zensink/zens-ink-seo-package/keyword-research"><img src="https://agentmods.dev/badge/skills/zensink/zens-ink-seo-package/keyword-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00108 | $0.00894 |
| Opus 5.5 | $0.00043 | $0.00358 |
| Sonnet 5.5 | $0.00022 | $0.00179 |
| Haiku 4.5 | $0.00011 | $0.00089 |
Grade A, and why
keyword-research scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword research without paid tools
Discover keywords, cluster them into topics, score them for opportunity — all from free data sources (Google Autocomplete, Bing Webmaster API). No Ahrefs/Semrush subscription required.
When to use
- "Find keyword ideas for my niche / my SaaS / my blog"
- "Which long-tail keywords should I target?"
- "Cluster this keyword list into topics"
- "Score these keywords — which ones are worth writing?"
When NOT to use
- The user already pays for Ahrefs/Semrush and wants their numbers — use their tool.
- They want backlink data — this skill has none (that is a paid-data problem).
Setup (one-time)
pip install zens-ink
Zero dependencies beyond Python 3.9+. All commands run as python3 -m zens_ink.<tool>.
Commands
1. Discover keywords (no key needed)
python3 -m zens_ink.keyword_research "ai seo tools"
Real output:
============================================================
Keyword: ai seo tools (en)
============================================================
In autocomplete : YES
Activity level : high-activity (10 variants)
Suggestions:
ai seo tools
ai seo tools for small business
ai seo tools free
ai seo tools reddit
ai seo tools comparison
...
Add --expand for a-z long-tail discovery (slower, much deeper). --lang zh
for Chinese autocomplete. --json for machine-readable output.
2. Cluster a keyword list into topics
python3 -m zens_ink.keyword_cluster --file keywords.txt
Groups semantically related keywords so one page can target one cluster. Use the output of step 1 (save suggestions to a file, one per line).
3. Volume lookup (optional, free Bing Webmaster key)
python3 -m zens_ink.keyword_volume "ai seo tools free" "ai seo tools reddit" --country us --lang en-us
Language must be a full locale (en-us, not en), country lowercase — the
CLI normalizes this for you. Needs BING_WMT_API_KEY in .env.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 120 lines · 108 tokens per session scan A 7bff3b6aa13b
keyword-research is a skill published in the GitHub repository ZensInk/zens-ink-seo-package (53 stars, last pushed yesterday), licensed MIT. It adds 108 tokens to every session and 894 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-15.
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