A simulated focus group made up of AI personas, or fictional participants designed to represent different kinds of people. It lets you define the participants and questions, run the discussion, and summarize the responses.

Use it to test a research question, compare viewpoints, identify recurring patterns, and collect feedback about a product or idea.

It gives you structured qualitative feedback when you need to explore opinions but do not want to organize a live research session.

focus-group — Skill for Claude Code by DataViking-Tech/Althing

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.

Any agent

npx skills add DataViking-Tech/Althing --skill focus-group

Clone the repo

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

Made for: Claude Code.

Or install althing, the plugin that ships this one along with the rest of its 6 skills, 1 command.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/dataviking-tech/althing/focus-group/github.svg)](https://agentmods.dev/skills/dataviking-tech/althing/focus-group)

Your own site

<a href="https://agentmods.dev/skills/dataviking-tech/althing/focus-group"><img src="https://agentmods.dev/badge/skills/dataviking-tech/althing/focus-group/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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/dataviking-tech/althing/focus-group"><img src="https://agentmods.dev/badge/skills/dataviking-tech/althing/focus-group.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 945 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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 5.1	$0.00025	$0.00945
Opus 5.5	$0.00010	$0.00378
Sonnet 5.5	$0.00005	$0.00189
Haiku 4.5	$0.00003	$0.00094

Measured 1mo ago against content hash 78fec5eed54d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

focus-group 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 1mo 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/focus-group/SKILL.md · 78 lines

How it starts

The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are orchestrating a synthetic focus group using the althing MCP tools.

What You Do

You help the user design and run synthetic focus groups — structured qualitative research using AI-powered personas. You handle the full workflow:

Understand the research question — What does the user want to learn?
Define personas — Assemble a list of realistic, diverse participants.
Design the instrument — Write targeted questions and follow-ups.
Run the panel — Execute the focus group via MCP tools.
Synthesize results — Summarize findings, identify patterns, and highlight insights.

Available MCP Tools

mcp__althing__run_prompt — Send a single prompt to an LLM (no personas). Use for a quick smoke test before building a panel.
mcp__althing__run_panel — Run a full panel: pass personas (an inline list of persona dicts) plus one question source — inline questions, an inline instrument dict, or an instrument_pack name.
mcp__althing__list_persona_packs — List saved persona packs you can reuse via run_panel's pack_id.
mcp__althing__list_instrument_packs — List installed instrument packs you can reference via run_panel's instrument_pack.

These MCP tools take inline data or pack names, not file paths. Personas are a JSON list; instruments are an inline dict or an installed pack name. There is no "load this YAML path" argument — read any file yourself and pass its contents inline.

Workflow

Step 1: Clarify the Research Goal

Ask the user what they want to test. Examples:

"What do people think of the name 'Traitprint' for a career app?"
"How would different demographics react to this pricing page?"
"Pre-screen this survey before we send it to real participants."

Step 2: Assemble Personas

Build a list of 3-6 diverse personas. Each persona is a dict with:

name (required), age, occupation
background — 2-3 sentences of life context
personality_traits — 3-5 traits that shape their perspective

Read the full file on GitHub · 78 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.

1mo ago First seen · 78 lines · 25 tokens per session scan A 78fec5eed54d

Subscribe to this mod's changes

focus-group is a skill published in the GitHub repository DataViking-Tech/Althing (2 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 945 once invoked, about $0.0001 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 2026-08-31.

Marketing and SEO Python Research llm-evaluation mcp persona-simulation survey-research synthetic-respondents

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