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 DataViking-Tech/Althing --skill concept-testgit clone --depth 1 https://github.com/DataViking-Tech/AlthingWrote 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/dataviking-tech/althing/concept-test)<a href="https://agentmods.dev/skills/dataviking-tech/althing/concept-test"><img src="https://agentmods.dev/badge/skills/dataviking-tech/althing/concept-test/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/dataviking-tech/althing/concept-test"><img src="https://agentmods.dev/badge/skills/dataviking-tech/althing/concept-test.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 | $0.00033 | $0.00937 |
| Opus 5.5 | $0.00013 | $0.00375 |
| Sonnet 5.5 | $0.00007 | $0.00187 |
| Haiku 4.5 | $0.00003 | $0.00094 |
Grade A, and why
concept-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 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.
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are running a concept test using the althing MCP tools.
What You Do
You help the user pressure-test an early-stage product concept, value prop, or feature idea against a specific target audience before they build or ship it. The goal is to surface whether the problem is real and whether the proposed solution actually addresses it — not to validate a decision that's already been made.
- Clarify the concept and the audience.
- Design concept-oriented personas drawn from (or biased toward) the target audience.
- Run the panel with questions that probe pain, fit, willingness-to-adopt, and objections.
- Synthesize — is the problem real, does the concept resonate, and what blocks adoption?
Available MCP Tools
mcp__althing__run_panel— Primary tool. Run a multi-question panel with target-audience personas.mcp__althing__run_quick_poll— Use for a single "would you try this?" temperature check.mcp__althing__list_persona_packs/mcp__althing__get_persona_pack— Reuse saved target-audience packs.mcp__althing__save_persona_pack— Save a new audience pack if the user is likely to re-test.mcp__althing__list_instrument_packs— Check for bundled packs that fit (e.g.product-feedback,market-research).
Workflow
Step 1: Frame the Concept
Ask the user for:
- A 2-3 sentence description of the concept (what it is, who it's for, what problem it solves).
- The target audience (role, demographic, or psychographic).
- What decision this test is meant to inform ("should we keep exploring?" vs. "which direction?").
Step 2: Build or Load Personas
- 4-8 personas, biased toward the target audience but with at least one skeptic and at least one adjacent non-target to stress-test the boundaries.
- Each persona should have a plausible reason the problem may or may not apply to them.
Step 3: Design the Instrument
Include questions in this order:
- Problem probe — "Does this describe something you've actually experienced?" (don't lead with the solution)
- Concept reveal — present the concept, ask for gut reaction.
- Fit — "Who do you know this would be for?" (reveals whether they see themselves in it)
- Adoption blockers — "What would stop you from trying this?"
- Willingness — rough price sensitivity or alternative-they'd-pick.
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 · 73 lines · 33 tokens per session scan A ad9da62d47fd
concept-test is a skill published in the GitHub repository DataViking-Tech/Althing (2 stars, last pushed 2d ago), licensed MIT. It adds 33 tokens to every session and 937 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.
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