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/survey-prescreennpx skills add DataViking-Tech/Althing --skill survey-prescreengit 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/survey-prescreen)<a href="https://agentmods.dev/skills/dataviking-tech/althing/survey-prescreen"><img src="https://agentmods.dev/badge/skills/dataviking-tech/althing/survey-prescreen.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.00998 |
| Opus 5 | $0.00015 | $0.00499 |
| Sonnet 5 | $0.00006 | $0.00200 |
| Haiku 4.5 | $0.00003 | $0.00100 |
Grade A, and why
survey-prescreen 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 3d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are pre-screening a user-authored survey instrument using the althing MCP tools.
What You Do
You help the user catch problems in a survey before they spend real budget fielding it. Synthetic respondents are cheap — use them to find ambiguous wording, leading questions, unanswerable items, and dead-end branches before real participants see the instrument.
- Load the user's instrument (YAML file they provide, or
save_instrument_packfirst if it's inline). - Assemble stress-test personas — a mix that should reveal wording problems across demographics.
- Run the instrument as a full panel.
- Critique — report specific failure modes per question, not just a thumbs up/down.
Available MCP Tools
mcp__althing__run_panel— Run the user's instrument against the stress-test personas. Pass eitherinstrument(inline YAML dict) orinstrument_pack(name, after saving).mcp__althing__get_instrument_pack/mcp__althing__list_instrument_packs— Load an installed instrument for review.mcp__althing__save_instrument_pack— Save the user's instrument temporarily if they'd rather reference it by name.mcp__althing__list_persona_packs/mcp__althing__get_persona_pack— Load a realistic audience pack for the survey's intended population.
Workflow
Step 1: Load the Instrument
Read the user's YAML. Before running, do a quick structural review:
- Are questions clear and scoped to one thing each?
- Any double-barreled questions ("How satisfied and how often do you...")?
- Any leading wording ("How much do you love...")?
- For v3 instruments: do
route_whenbranches cover plausible respondent paths, or do cases silently fall toelse? - Are follow-ups specific enough to produce depth?
Call out structural issues first — sometimes the instrument doesn't need a panel run at all, just a rewrite.
Step 2: Assemble Stress-Test Personas
5-8 personas that specifically stress the instrument:
- Representative of the intended audience (2-3).
- Adjacent-but-different — slightly outside the target, to reveal questions that assume audience knowledge (1-2).
- A literal responder who answers exactly what's asked (catches ambiguity).
- A confused/distracted responder (catches comprehension failures).
- An opinionated outlier (catches leading questions — they'll push 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.
- 3d ago First seen · 77 lines · 31 tokens per session scan A fe51c1e6494c
survey-prescreen is a skill published in the GitHub repository DataViking-Tech/Althing (2 stars, last pushed 24d ago), licensed MIT. It adds 31 tokens to every session and 998 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.
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