Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/robroyhobbs/marketing-skillsnpx agentmods add skills/robroyhobbs/marketing-skills/keyword-researchWrote 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/robroyhobbs/marketing-skills/keyword-research)<a href="https://agentmods.dev/skills/robroyhobbs/marketing-skills/keyword-research"><img src="https://agentmods.dev/badge/skills/robroyhobbs/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/robroyhobbs/marketing-skills/keyword-research"><img src="https://agentmods.dev/badge/skills/robroyhobbs/marketing-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 | $0.00164 | $0.12119 |
| Opus 5.5 | $0.00066 | $0.04848 |
| Sonnet 5.5 | $0.00033 | $0.02424 |
| Haiku 4.5 | $0.00016 | $0.01212 |
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 2mo 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 — 1,499 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/keyword-research -- Data-Backed Keyword Strategy
Most keyword research is backwards. People start with tools, get overwhelmed by data, and end up with a spreadsheet they never use.
This skill starts with strategy. What does your business need? Who are you trying to reach? What would make them find you? Then it validates with live search data and builds a content plan that actually makes sense.
No expensive tools required. Systematic thinking plus web search.
Read ./brand/ per _system/brand-memory.md
Follow all output formatting rules from _system/output-format.md
Brand Memory Integration
On every invocation, check for existing brand context.
Reads (if they exist)
| File | What it provides | How it shapes output |
|---|---|---|
| ./brand/positioning.md | Market angles, differentiators | Aligns keyword selection with brand positioning -- a rebel brand targets different keywords than a trusted advisor |
| ./brand/audience.md | Buyer profiles, sophistication level | Informs search intent mapping -- beginner audience means more "what is" and "how to" keywords |
| ./brand/competitors.md | Named competitors, their positioning | Seeds competitive content gap analysis -- search what they rank for, find what they miss |
Writes
| File | What it contains |
|---|---|
| ./brand/keyword-plan.md | The complete prioritized keyword plan (profile file, create-or-overwrite) |
| ./campaigns/content-plan/*.md | Individual content briefs for top-priority keywords |
| ./brand/assets.md | Appends entries for each content brief created |
Context Loading Behavior
- Check whether
./brand/exists. - If it exists, read
positioning.md,audience.md, andcompetitors.mdif present. - If loaded, show the user what you found:
Brand context loaded: ├── Positioning ✓ "{primary angle summary}" ├── Audience ✓ "{audience summary}" └── Competitors ✓ {N} competitors profiled Using this to shape keyword strategy. - If files are missing, proceed without them. Note at the end:
→ /positioning-angles would sharpen keyword alignment → /audience-research would tune intent mapping → /competitive-intel would unlock gap analysis - If no brand directory exists at all:
No brand profile found — this skill works standalone. I'll ask what I need as we go. Run /start-here or /brand-voice later to unlock personalization.
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.
- 2mo ago First seen · 1,499 lines · 164 tokens per session scan A 0e041e820f29
keyword-research is a skill published in the GitHub repository robroyhobbs/marketing-skills (5 stars, last pushed 7mo ago), licensed MIT. It adds 164 tokens to every session and 12,119 once invoked, about $0.0007 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 2026-08-31.
keyword-research compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| keyword-researchrobroyhobbs/marketing-skills | Skill | 5 | 7mo ago | MIT | A | 164 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 134k | today | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 314k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |