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 kostja94/marketing-skills --skill domain-selectiongit clone --depth 1 https://github.com/kostja94/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/kostja94/marketing-skills/domain-selection)<a href="https://agentmods.dev/skills/kostja94/marketing-skills/domain-selection"><img src="https://agentmods.dev/badge/skills/kostja94/marketing-skills/domain-selection/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/kostja94/marketing-skills/domain-selection"><img src="https://agentmods.dev/badge/skills/kostja94/marketing-skills/domain-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00111 | $0.01274 |
| Opus 5 | $0.00056 | $0.00637 |
| Sonnet 5 | $0.00022 | $0.00255 |
| Haiku 4.5 | $0.00011 | $0.00127 |
Grade A, and why
domain-selection 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 7d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- domain-selection — 100% identical, 0 lines differ
- domain-selection — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Strategy: Domain Selection
Guides initial domain choice for a single site: Brand vs Partial Match vs Exact Match domains, TLD selection (.ai, .com, .io), length, readability, history check, and defensive registration. A good domain affects SEO, brand perception, and UX. See domain-architecture when planning for multiple products; rebranding-strategy when changing domain.
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.
Reference: Alignify: Domain SEO – How to Choose SEO-Friendly Domains — detailed guide, AI brand naming, TLD recommendations, rebrand cases.
Initial Assessment
Check for project context first: If .claude/project-context.md or .cursor/project-context.md exists, read Sections 2 (Positioning), 3 (Target Audience), 8 (Brand & Voice).
Identify:
- Product type: Tool, content, e-commerce, AI product, etc.
- Brand stage: New brand vs established; solo vs team
- Goals: Quick SEO traffic vs long-term brand building
Domain Type: Brand vs PMD vs EMD
| Type | Description | SEO | Brand | Best For |
|---|---|---|---|---|
| Branded Domain | Domain = brand; no functional keywords (Notion, Canva, Perplexity) | Long-term; Google favors brands | High | Teams; long-term brand building |
| Partial Match (PMD) | Part of domain relates to function (FlowGPT, Dify, Reportify) | Balance; signals topic | Medium | AI tools; balance SEO + brand |
| Exact Match (EMD) | Domain = search query (png2jpg.com, aiartgenerator.cc) | Fast early traffic; ceiling lower | Low | Solo devs; tool sites; site networks |
Google stance: Keywords in domain no longer directly affect ranking; domain still matters for UX and brand. EMDs work when paired with quality content; branded domains with entity recognition matter more long-term.
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.
- 7d ago First seen · 82 lines · 111 tokens per session scan A c91b7feb297c
domain-selection is a skill published in the GitHub repository kostja94/marketing-skills (963 stars, last pushed 3mo ago), licensed MIT. It adds 111 tokens to every session and 1,274 once invoked, about $0.0006 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 2026-09-03.
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