A quick name-testing skill that asks a small synthetic group of people to assess one to three possible product or feature names. It checks whether names are understandable, easy to say, and memorable.

It is for comparing candidate names for products, features, or brands and identifying the concern that most affects each option.

It exposes likely confusion or pronunciation problems before a name is adopted.

name-test — Skill by DataViking-Tech

Install

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.

agentmods

npx agentmods add skills/dataviking-tech/althing/name-test

Any agent

npx skills add DataViking-Tech/Althing --skill name-test

Clone the repo

git clone --depth 1 https://github.com/DataViking-Tech/Althing

Made for: Claude Code, Codex.

Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 893 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.00034	$0.00893
Opus 5	$0.00017	$0.00447
Sonnet 5	$0.00007	$0.00179
Haiku 4.5	$0.00003	$0.00089

Measured 2d ago against content hash a9a609fdb8b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

name-test 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 2d 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.

site/.well-known/agent-skills/name-test/SKILL.md · 63 lines

How it starts

The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are running a quick name test using the althing MCP tools.

What You Do

You help the user decide between candidate names for a product, feature, or brand. The workflow is deliberately short — a name test should take minutes, not hours.

Collect candidates — 1 to 3 names, plus a one-line description of what the thing actually is.
Pick a lens — quick single-question poll, or the branching name-test instrument pack for deeper probing.
Run the panel — small, diverse personas; keep it cheap.
Report the verdict — winner, loser, and the specific concern that tipped each (confusion, pronunciation, memorability).

Available MCP Tools

mcp__althing__run_quick_poll — Single-question poll across personas (fastest, cheapest).
mcp__althing__run_panel — Full panel run using the bundled name-test branching instrument that probes meaning, pronounceability, or memorability based on first reactions. Its opening question has a {candidates} placeholder; fill it with the vars argument (see Step 2).
mcp__althing__list_persona_packs / mcp__althing__get_persona_pack — Reuse saved personas instead of inventing new ones.
mcp__althing__list_instrument_packs — Confirm the name-test pack is available.

Workflow

Step 1: Gather Inputs

Ask for:

The candidate names (comma-separated).
A one-sentence description of what the product/feature does.
Target audience (so personas are relevant).

Step 2: Choose Depth

Quick gut check → run_quick_poll with a question that has the description and candidate names written directly into the question string, e.g. "Which of these names best fits a budget travel app — Wander, Roamly, Tr9? Why?"
Full branching evaluation → run_panel with instrument_pack="name-test" and vars={"candidates": "Name A, Name B"} — the comma-separated candidate names are substituted into the opening question's {candidates} placeholder before the panel runs. (CLI equivalent: althing panel run --instrument name-test --var 'candidates=Name A, Name B'.) If you omit vars, the call fails fast with a typed INVALID_TOOL_ARG error naming the missing placeholder. The instrument branches into meaning-probe, pronounce-probe, or memorability-probe based on what surfaces first.

Read the full file on GitHub · 63 lines

Changes

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.

2d ago First seen · 63 lines · 34 tokens per session scan A a9a609fdb8b0

Subscribe to this mod's changes

name-test is a skill published in the GitHub repository DataViking-Tech/Althing (2 stars, last pushed 23d ago), licensed MIT. It adds 34 tokens to every session and 893 once invoked, about $0.0002 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 2026-08-31.

Data and AI Research Testing llm-evaluation mcp persona-simulation python survey-research synthetic-respondents

deepeval

DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI…

confident-ai/deepeval · 229 tokens

deepeval-otel

Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span. or confident.trace. attributes…

confident-ai/deepeval · 226 tokens

deepeval-tracing

Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents…

confident-ai/deepeval · 208 tokens

source-ingest

Normalize a raw source — PDF, DOCX, XLSX, URL, article text, or transcript — into a structured source record compatible with SOURCEPOLICY.md evidence modes and per-claim provenance tags. Use before analysis when the user provides a document and you need to extract metadata, classify source type, pull key excerpts, and…

vassiliylakhonin/agenda-intelligence-md · 90 tokens

agenda-intelligence

Turn public agenda, news, policy moves, geopolitical developments, sanctions, trade disputes, regulation, elections, diplomatic signals, and strategic-risk events into decision-ready briefs. Use when the user asks an agent to analyze what changed, why it matters, who is affected, what is uncertain, scenarios…

vassiliylakhonin/agenda-intelligence-md · 78 tokens

Crypto AML Auditor

This profile is dynamically generated by the Agent Factory. It evaluates Crypto AML Auditor checklists for evidence readiness.

vassiliylakhonin/agenda-intelligence-md · 3 tokens