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 ViryaZheng/recomby-geo --skill keyword-researchgit clone --depth 1 https://github.com/ViryaZheng/recomby-geoWrote 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/viryazheng/recomby-geo/keyword-research)<a href="https://agentmods.dev/skills/viryazheng/recomby-geo/keyword-research"><img src="https://agentmods.dev/badge/skills/viryazheng/recomby-geo/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/viryazheng/recomby-geo/keyword-research"><img src="https://agentmods.dev/badge/skills/viryazheng/recomby-geo/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 | $0.00091 | $0.02307 |
| Opus 5 | $0.00046 | $0.01154 |
| Sonnet 5 | $0.00018 | $0.00461 |
| Haiku 4.5 | $0.00009 | $0.00231 |
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 13d ago.
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.
This is a copy
97% identical to keyword-research — 14 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword Research
Discovers, analyzes, and prioritizes keywords for SEO and GEO content strategies. Identifies high-value opportunities based on search volume, competition, intent, and business relevance.
When This Must Trigger
Use this when the conversation involves any of these situations — even if the user does not use SEO terminology:
Use this whenever the task needs reusable market intelligence that should influence strategy, not just an ad hoc answer.
- Starting a new content strategy or campaign
- Expanding into new topics or markets
- Finding keywords for a specific product or service
- Identifying long-tail keyword opportunities
- Understanding search intent for your industry
- Planning content calendars
- Researching keywords for GEO optimization
What This Skill Does
- Keyword Discovery: Generates comprehensive keyword lists from seed terms
- Intent Classification: Categorizes keywords by user intent (informational, navigational, commercial, transactional)
- Difficulty Assessment: Evaluates competition level and ranking difficulty
- Opportunity Scoring: Prioritizes keywords by potential ROI
- Clustering: Groups related keywords into topic clusters
- GEO Relevance: Identifies keywords likely to trigger AI responses
Quick Start
Start with one of these prompts.
Basic Keyword Research
Research keywords for [topic/product/service]
Find keyword opportunities for a [industry] business targeting [audience]
With Specific Goals
Find low-competition keywords for [topic] with commercial intent
Identify question-based keywords for [topic] that AI systems might answer
Competitive Research
What keywords is [competitor URL] ranking for that I should target?
Data Sources
Note: All integrations are optional. This skill works without any API keys — users provide data manually when no tools are connected.
With ~~SEO tool + ~~search console connected: Automatically pull historical search volume data, keyword difficulty scores, SERP analysis, current rankings from ~~search console, and competitor keyword overlap. The skill will fetch seed keyword metrics, related keyword suggestions, and search trend data.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 13d ago First seen · 302 lines · 91 tokens per session scan A 3b45f58f2b75
keyword-research is a skill published in the GitHub repository ViryaZheng/recomby-geo (454 stars, last pushed 2mo ago), licensed MIT. It adds 91 tokens to every session and 2,307 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to keyword-research, differing in 14 lines, and is treated as a copy.
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