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 agentmods add skills/dataviking-tech/althing/focus-groupnpx skills add DataViking-Tech/Althing --skill focus-groupgit clone --depth 1 https://github.com/DataViking-Tech/AlthingWhat 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 | $0.00025 | $0.00945 |
| Opus 5 | $0.00013 | $0.00473 |
| Sonnet 5 | $0.00005 | $0.00189 |
| Haiku 4.5 | $0.00003 | $0.00094 |
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 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.
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: passpersonas(an inline list of persona dicts) plus one question source — inlinequestions, an inlineinstrumentdict, or aninstrument_packname.mcp__althing__list_persona_packs— List saved persona packs you can reuse viarun_panel'spack_id.mcp__althing__list_instrument_packs— List installed instrument packs you can reference viarun_panel'sinstrument_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,occupationbackground— 2-3 sentences of life contextpersonality_traits— 3-5 traits that shape their perspective
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 · 78 lines · 25 tokens per session scan A 78fec5eed54d
focus-group is a skill published in the GitHub repository DataViking-Tech/Althing (2 stars, last pushed 23d ago), licensed MIT. It adds 25 tokens to every session and 945 once invoked, about $0.0001 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.
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