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 superamped/ai-marketing-skills --skill keyword-researchgit clone --depth 1 https://github.com/superamped/ai-marketing-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/superamped/ai-marketing-skills/keyword-research)<a href="https://agentmods.dev/skills/superamped/ai-marketing-skills/keyword-research"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-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/superamped/ai-marketing-skills/keyword-research"><img src="https://agentmods.dev/badge/skills/superamped/ai-marketing-skills/keyword-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00049 | $0.01705 |
| Opus 5 | $0.00024 | $0.00852 |
| Sonnet 5 | $0.00010 | $0.00341 |
| Haiku 4.5 | $0.00005 | $0.00170 |
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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Keyword Research
Usage
Use when planning content around a topic before writing, building a keyword map for a new content area, finding question-based keywords for GEO optimization, or identifying gaps where competitors rank and you don't.
Process
Step 1: Gather Inputs
Ask the user for:
- Seed topic or keyword — e.g., "AI search optimization", "B2B SaaS customer acquisition"
- Country code (optional) — for localised volumes (default: "us")
- Competitor domain(s) (optional) — to identify keyword gaps
- Number of clusters (optional) — default: let the data dictate
Step 2: Validate & Prepare
- Confirm the Keywords Everywhere MCP server is connected. If not configured, tell the user — this skill requires it.
- Check credit balance with Get Credit Balance before starting — warn the user if credits are low.
Credit estimate formula: (related_count + pasf_count) * 2 credits for expansion + total_unique_keywords * 1 credit for metrics. A typical run with 100 related + 100 PASF keywords costs ~500 credits. Warn if balance would drop below 1,000 after the run.
Step 3: Expand the Seed
Run two Keywords Everywhere tools against the seed keyword:
- Get Related Keywords (num: 100) — returns a list of keyword strings (no metrics yet)
- Get "People Also Search For" Keywords (num: 100) — returns a list of keyword strings (no metrics yet)
If PASF returns empty results: This is common for newer or niche terms. Proceed with the related keywords only. If the combined list is thin (< 30 keywords), consider running a second expansion on a broader variant of the seed.
Combine the results into a single deduplicated keyword list.
If the seed returns fewer than 20 keywords total, it may be too narrow. Suggest broader alternatives to the user.
Step 3b: Pull Metrics
Run Get Keyword Data on the deduplicated keyword list to get volume, CPC, competition, and trend data for every keyword.
Batch in groups of 50 keywords per API call to avoid oversized responses. Run batches in parallel where possible.
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.
- 12d ago First seen · 165 lines · 49 tokens per session scan A 23dfc377fd32
keyword-research is a skill published in the GitHub repository superamped/ai-marketing-skills (67 stars, last pushed 25d ago), licensed MIT. It adds 49 tokens to every session and 1,705 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
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