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.
git 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/commands/dataviking-tech/althing/althing-poll)<a href="https://agentmods.dev/commands/dataviking-tech/althing/althing-poll"><img src="https://agentmods.dev/badge/commands/dataviking-tech/althing/althing-poll/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/commands/dataviking-tech/althing/althing-poll"><img src="https://agentmods.dev/badge/commands/dataviking-tech/althing/althing-poll.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 | $0.00014 | $0.00711 |
| Opus 5.5 | $0.00006 | $0.00284 |
| Sonnet 5.5 | $0.00003 | $0.00142 |
| Haiku 4.5 | $0.00001 | $0.00071 |
Grade A, and why
althing-poll 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run a quick synthetic poll using the Althing MCP server. One question, a handful of AI personas, one synthesized writeup.
Question: $ARGUMENTS
What to do
-
Resolve the question. If
$ARGUMENTSis empty, ask the user for the question before continuing. Do not invent one. -
Pick personas. Default to the built-in zero-config persona set by omitting the
personasargument. Only build a custom list when the user names a specific audience (e.g. "three enterprise SRE personas"). In that case, either:- Call
mcp__althing__list_persona_packsandget_persona_packto pull an installed pack, or - Hand-author 3–5 dicts with
name,age,occupation,background, andpersonality_traits.
Keep panels small —
run_quick_pollcaps at 3 personas when running in sampling mode (no API key). - Call
-
Run the poll. Call
mcp__althing__run_quick_pollwith:question: the user's questionpersonas: omit for the default set, or pass your custom listsynthesis: leave astrue(default) — the synthesis pass is the point of this command
Do not pass
modelunless the user explicitly asked for a specific one. The MCP server picks a sensible default (haiku in BYOK mode, or the host's own model in sampling mode). -
Report the result. Present the output in this shape:
Question: <the question> Panel: <n> personas (<mode: BYOK model-name | sampling>) ## Themes <bulleted list from the synthesis> ## Per-persona responses - <persona name>: <one-line summary of their response> - ... ## Notable divergences <any disagreements the synthesis flagged> Cost: <$X.XX from the response's `cost` field, or "n/a" in sampling mode>Keep each persona summary to one line. The full transcripts are in the raw tool output — don't re-print them.
Guardrails
- Synthetic panels are exploratory, not validating. End the report with a single-line caveat: "Synthetic panel — directional signal only, not a substitute for real user research."
- Don't loop. If the user wants a second question, that's another
/althing-pollinvocation, not a follow-up inside this one. For multi-question structured studies, suggest the/focus-groupskill instead. - Report errors honestly. If the tool returns an
errorfield (missing API key, persona cap exceeded, etc.), show it verbatim and suggest the fix — don't silently fall back.
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 · 76 lines · 14 tokens per session scan A 9342e8351c64
althing-poll is a command published in the GitHub repository DataViking-Tech/Althing (2 stars, last pushed 2d ago), licensed MIT. It adds 14 tokens to every session and 711 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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