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 haru0416-dev/quaere --skill quaere-naminggit clone --depth 1 https://github.com/haru0416-dev/quaereWrote 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/haru0416-dev/quaere/quaere-naming)<a href="https://agentmods.dev/skills/haru0416-dev/quaere/quaere-naming"><img src="https://agentmods.dev/badge/skills/haru0416-dev/quaere/quaere-naming/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/haru0416-dev/quaere/quaere-naming"><img src="https://agentmods.dev/badge/skills/haru0416-dev/quaere/quaere-naming.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 | $0.00178 | $0.02538 |
| Opus 5 | $0.00089 | $0.01269 |
| Sonnet 5 | $0.00036 | $0.00508 |
| Haiku 4.5 | $0.00018 | $0.00254 |
Grade A, and why
quaere-naming scanned grade A with 1 finding 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 9d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**No name reaches the user without three things named: the metaphor it compresses (its origin story), a tool-verified availability status (real `whois` / `curl` / `npm` / WebSearch checks, never recalled from memory), an How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Naming Probe
Iron Law
No name reaches the user without three things named: the metaphor it compresses (its origin story), a tool-verified availability status (real whois / curl / npm / WebSearch checks, never recalled from memory), and the anti-pattern gate it survived. A name that is only a nice-sounding word with none of the three is slop, not a candidate — do not present it.
This is not a branding ritual. Asked to name something, a model regresses toward the mean of its training distribution and emits plausible-sounding -ly / -ify slop, thesaurus synonyms, and empty compounds (Smart-, Cloud-, -Hub) that sound finished but carry no metaphor and were never checked for availability. The gate changes the question from does this sound like a product name to which concrete image does it compress, and is it actually free to use. Presenting names from memory is the failure this skill exists to stop — an unavailable name shipped as available is worse than no name. Full method: references/metaphor.md and references/availability.md.
Stop now — do not present any name you have not run a real availability check on (competitor search + platform commands); memory is not evidence. If fewer than 3 candidates survive the gate, loop back and generate more — do not lower the bar. Full conditions: ## Stop condition.
When to use
- The user needs to name a product, SaaS, brand, library, open source project, CLI, bot, or app.
- The user wants to rename something, find a brand name, or escape generic / AI-slop names.
- A name must work across platforms: domain, npm, PyPI, GitHub, app store, or social handles.
When NOT to use
- Naming code symbols, variables, functions, or files inside a codebase — use
quaere-semantic/quaere-execution, not brand naming. - The name is already chosen and only needs a multi-platform availability sweep — run the availability gate (Step 4) directly and skip metaphor exploration.
- A single authoritative claim about one name (one registry, trademark status) — use
quaere-grounding.
What ships with it
4 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.
- 9d ago First seen · 151 lines · 178 tokens per session scan A 291f42a34012
quaere-naming is a skill published in the GitHub repository haru0416-dev/quaere (5 stars, last pushed 1mo ago), licensed MIT. It adds 178 tokens to every session and 2,538 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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