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 glebis/claude-skills --skill name-auditiongit clone --depth 1 https://github.com/glebis/claude-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/glebis/claude-skills/name-audition)<a href="https://agentmods.dev/skills/glebis/claude-skills/name-audition"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/name-audition/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/glebis/claude-skills/name-audition"><img src="https://agentmods.dev/badge/skills/glebis/claude-skills/name-audition.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 | $0.00137 | $0.02091 |
| Opus 5 | $0.00068 | $0.01045 |
| Sonnet 5 | $0.00027 | $0.00418 |
| Haiku 4.5 | $0.00014 | $0.00209 |
Grade A, and why
name-audition 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 8d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Name Audition — «Кастинг имён»
Audition candidate names before you cast one. Brandability is not availability, and a free domain is not a safe name — the audition separates the three. Candidates try out; the best one gets cast; the rest simply don't make the cut.
The core lesson this skill encodes
A name can sound perfect, score well, have every domain free — and still be the wrong choice. Three ways a candidate fails its screen test, worst first:
- Adjacent-domain collision is the worst kind. A product already operating in the target vertical means a name doesn't make the cut even when the string is free to register — confusion and SEO dilution are fatal in the same space.
- Descriptive / generic names are domain-free but weak. Easy to register, hard to own — bad for trademark, bad for SEO, easy for competitors to crowd.
- Search visibility ≠ availability. "I didn't see it in results" is not proof a name is free. Verify with authoritative sources before casting.
Workflow — the casting call
Run these stages in order. Stages 3a and 3b run together.
- Brief. Establish: (a) what is being named (product / app / company / feature / benchmark),
(b) scope + one-line description, (c) the adjacent domain — the vertical it lives in
(healthcare, coaching, privacy/security, dev tooling); the user supplies this, (d) tone / vibe,
(e) which TLDs matter (default
.com .org .ai .io .app .co). If (a)–(c) is missing, ask first — the adjacent domain is what makes collision research meaningful. - The audition. Generate 4–8 candidate names matching the tone. Favor short, pronounceable, ownable coinages over descriptive compounds. Note for each what it means / why it fits.
- The screen test (run 3a and 3b together):
- 3a — Domains (authoritative). Run
scripts/check_domains.sh NAME [NAME ...] -- com ai io ...for a name × TLD availability table. WHOIS no-match + no NS = registrable; Creation Date / Registrar / NS present = taken; ambiguous = verify by hand. Authoritative for registration, never for trademark. - 3b — Collision research. For each candidate, use the
firecrawlskill or web search (never beautifulsoup) to check the sources below.
- 3a — Domains (authoritative). Run
- Callbacks. Build a per-candidate risk table and rank by safety + ownability.
- Casting report. Use the
presentskill to build an interactive HTML deck — one slide per finalist plus a ranked comparison and a "cast it?" slide. - Branding (optional, gated). Only if the user wants it: draft a wordmark/logo per finalist
with
nano-bananaorgpt-image-2(draft quality), embed in the slides. - Cast → user decides. Give a clear top pick with reasoning; the user makes the final call. Names that fail "didn't make the cut" — never "killed".
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 140 lines · 137 tokens per session scan A ed37c03238ed
name-audition is a skill published in the GitHub repository glebis/claude-skills (375 stars, last pushed 10d ago), licensed MIT. It adds 137 tokens to every session and 2,091 once invoked, about $0.0007 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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