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 JoseArron/skills --skill brainstorm-namesgit clone --depth 1 https://github.com/JoseArron/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/josearron/skills/brainstorm-names)<a href="https://agentmods.dev/skills/josearron/skills/brainstorm-names"><img src="https://agentmods.dev/badge/skills/josearron/skills/brainstorm-names/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/josearron/skills/brainstorm-names"><img src="https://agentmods.dev/badge/skills/josearron/skills/brainstorm-names.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 | $0.00054 | $0.00673 |
| Opus 5.5 | $0.00022 | $0.00269 |
| Sonnet 5.5 | $0.00011 | $0.00135 |
| Haiku 4.5 | $0.00005 | $0.00067 |
Grade A, and why
brainstorm-names 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.
How it starts
The opening of the file, as written. The whole thing — 39 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brainstorm and evaluate names
Find names whose sound, meaning, and visual possibilities fit the audience. Clever spelling is not automatically a spoken joke or a viable brand.
Establish the brief
Read the concept, purpose, audience, languages, tone, prior batches, selected identity, and rejected directions. Derive naming criteria from this brief. Examples demonstrate traits or mechanisms; identify which traits matter rather than inheriting their culture, subject, humor, or source vocabulary. Distinguish primary names, titles, character names, jokes, and feature labels; they tolerate different levels of obscurity and reference dependence.
Choose analysis, a reusable naming prompt, generation, or shortlist research according to the request. Do not replace a naming-prompt request with candidates.
Build from sources and sounds
Choose naming approaches that fit the brief: descriptive, evocative, metaphorical, abstract, invented, compound, reference-based, or playful. For wordplay, use WORDPLAY.md to distinguish mechanisms and record transformations. Build source and semantic banks from the concept and intended audience, not from prior projects.
- Compare pronunciation, rhythm, syllables, and stress, not spelling alone. Prefer recoverable changes when recognition matters.
- Preserve references accurately. Verify unfamiliar names, quotes, meanings, and cultural claims from primary sources or knowledgeable speakers; label supplied but unverified dialect words.
- Record the intended language, pronunciation variety, and audience. Treat languages and communities as distinct; a cultural reference is useful only when it serves this brief.
- Use approximate respelling when helpful. Give IPA only when supported; do not fabricate precise dialect or celebrity pronunciations.
- Explore ordinary words, double meanings, concise compounds, and invented words as well as puns. Do not force mascots into every name.
Generate and select
Explore mechanisms, then reject weak recognition, awkward pronunciation, repeated jokes, forced compounds, and the wrong product implications. Match batch size and fields to the request. Candidate counts, category quotas, and numerical scores are optional brief choices.
What ships with it
2 files 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.
- 7d ago Changed b825da95c6e5
- 8d ago First seen · 39 lines · 54 tokens per session scan A 044c4c5f4289
brainstorm-names is a skill published in the GitHub repository JoseArron/skills (2 stars, last pushed 8d ago), licensed MIT. It adds 54 tokens to every session and 673 once invoked, about $0.0002 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 2026-10-11.
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