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 dalroot/agentic-seo --skill keyword-researchgit clone --depth 1 https://github.com/dalroot/agentic-seoWrote 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/dalroot/agentic-seo/keyword-research)<a href="https://agentmods.dev/skills/dalroot/agentic-seo/keyword-research"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/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/dalroot/agentic-seo/keyword-research"><img src="https://agentmods.dev/badge/skills/dalroot/agentic-seo/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 | $0.00086 | $0.02517 |
| Opus 5.5 | $0.00034 | $0.01007 |
| Sonnet 5.5 | $0.00017 | $0.00503 |
| Haiku 4.5 | $0.00009 | $0.00252 |
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 today.
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 — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SEO Content: Keyword Research
Guides keyword research for SEO: finding target keywords, assessing difficulty, understanding search intent, and building topical maps. ~95% of keywords get fewer than 10 searches/month; low-volume, high-intent terms often yield faster rankings and conversion.
When invoking: On first use, if helpful, open with 1–2 sentences on what this skill covers and why it matters, then provide the main output. On subsequent use or when the user asks to skip, go directly to the main output.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read it for product, audience, and positioning.
Identify:
- Product/service: What you offer
- Audience: Who searches for it
- Goals: Traffic, conversions, brand
- Tool access: Google Keyword Planner, Google Trends, or SEO tools
Discovery Methods
Base Discovery
| Method | Purpose |
|---|---|
| User perspective | What pain points? What would they search? Customer language from product context |
| Tool expansion | Related keywords, questions, suggestions; Google autocomplete, PAA, Related Searches |
| Competitor reverse | Analyze competitor titles, H1, URL; identify topics they rank for; find gaps (#4–10 = opportunity) — see competitor-research |
| Google PAA | People Also Ask and Related Searches; high-value signals from real user behavior |
| Extract from article | When auditing existing content: extract seed keywords from title, H1, H2s, meta keywords, first 100 words; then search "[primary keyword]" or "[primary keyword] related keywords" for opportunities; use "[primary keyword]" site:competitor.com if competitors known |
Google Autocomplete (Long-Tail Discovery)
Google autocomplete reflects real user searches; suggestions only appear if queries have actual traffic. Free; often uncovers low-volume long-tail that keyword tools miss. ~70% of search traffic is long-tail; lower competition, higher conversion.
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.
- today First seen · 196 lines · 86 tokens per session scan A 409fe20bce8b
keyword-research is a skill published in the GitHub repository dalroot/agentic-seo (25 stars, last pushed 6d ago), licensed MIT. It adds 86 tokens to every session and 2,517 once invoked, about $0.0003 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 2026-10-26.
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