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/concept-testnpx skills add DataViking-Tech/Althing --skill concept-testgit 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 | $0.00033 | $0.00937 |
| Opus 5 | $0.00016 | $0.00468 |
| Sonnet 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 yesterday.
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.
- yesterday 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 23d ago), licensed MIT. It adds 33 tokens to every session and 937 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.
Other skills, from other repositories
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…
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…
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…
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…
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…
Crypto AML Auditor
This profile is dynamically generated by the Agent Factory. It evaluates Crypto AML Auditor checklists for evidence readiness.