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 h4vzz/awesome-ai-agent-skills --skill keyword-researchgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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/h4vzz/awesome-ai-agent-skills/keyword-research)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/keyword-research"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/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/h4vzz/awesome-ai-agent-skills/keyword-research"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/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 | $0.00029 | $0.01800 |
| Opus 5.5 | $0.00012 | $0.00720 |
| Sonnet 5.5 | $0.00006 | $0.00360 |
| Haiku 4.5 | $0.00003 | $0.00180 |
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 1mo 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
94% identical to keyword-research — 2 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword Research
This skill enables an AI agent to perform end-to-end keyword research for any niche, product, or content initiative. The agent starts from seed keywords, expands into long-tail variations, classifies search intent, analyzes competitor keyword portfolios, and delivers a prioritized keyword strategy. The output helps content teams, SEO specialists, and product marketers target the right search terms to drive qualified organic traffic.
Workflow
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Collect seed keywords and define scope. Gather initial seed keywords from the user's product description, existing content, and business goals. Identify the target market, geographic region, and language. Clarify whether the research is for blog content, landing pages, product pages, or paid campaigns, as this affects intent priorities.
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Expand into long-tail and related keywords. Use autocomplete patterns, "People Also Ask" queries, and semantic variations to build a broad keyword list. Generate question-based keywords (who, what, how, why), comparison keywords ("X vs Y"), and modifier keywords (best, top, free, cheap, review). Aim for 50–200 candidate keywords per seed term depending on niche competitiveness.
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Gather search metrics for each keyword. Estimate monthly search volume, keyword difficulty (0–100 scale), cost-per-click for paid reference, and trend direction (rising, stable, declining). Pull click-through rate estimates where available. Note seasonal patterns — for example, "tax software" peaks in January–April while "sunscreen" peaks in May–July.
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Classify search intent for every keyword. Categorize each keyword as informational (learn), navigational (find a specific site), commercial investigation (compare options), or transactional (buy/sign up). This mapping determines the correct content format: blog posts for informational, comparison pages for commercial, and product/landing pages for transactional.
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Perform competitor keyword gap analysis. Identify 3–5 organic competitors and compare their ranking keywords against the user's current keyword portfolio. Highlight keywords where competitors rank but the user does not — these are content gap opportunities. Also flag keywords where the user ranks on page 2 (positions 11–20) that could move to page 1 with targeted optimization.
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.
- 1mo ago First seen · 92 lines · 29 tokens per session scan A cdc33477f941
keyword-research is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 29 tokens to every session and 1,800 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to keyword-research, differing in 2 lines, and is treated as a copy.
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