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 Uxcel-Lab/product-skills --skill assumption-testinggit clone --depth 1 https://github.com/Uxcel-Lab/product-skillsWrote 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/uxcel-lab/product-skills/assumption-testing)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/assumption-testing"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/assumption-testing/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/uxcel-lab/product-skills/assumption-testing"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/assumption-testing.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 | $0.00099 | $0.02385 |
| Opus 5 | $0.00049 | $0.01192 |
| Sonnet 5 | $0.00020 | $0.00477 |
| Haiku 4.5 | $0.00010 | $0.00238 |
Grade A, and why
pm-assumption-testing 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 9d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assumption Testing Skill
How this skill behaves (read first)
This is a generative skill, and "validate our idea" is a default trap. The instinctive move is to jump to a method — build an MVP, run a survey, ship an A/B test — or to test the idea ("do you like this?", which everyone says yes to) instead of the assumptions the idea depends on. The other failure is investment mismatch: writing code to answer a question a one-day test would settle, or betting the quarter on a hallway poll. So this skill gates:
- Establish the decision and its stakes — what choice does this inform, and how reversible/expensive is it? That sets how much rigor is warranted.
- Apply the always-true core — decompose into testable assumptions, prioritize the riskiest, phrase them falsifiably, set success criteria up front.
- Choose the method deliberately — surface the fidelity/method decision with trade-offs; don't default to "build it and see."
Then it hands the plan to pm-assumption-rigor-audit to validate (and pm-okr-metric-validity-audit for the success metric). Applying every test type to every assumption is the failure mode — match one good method to each risky belief.
Step 0 — Establish context before planning tests
Ask if not known; state the assumption if proceeding without an answer:
- What decision does this inform, and how reversible is it? A cheap, reversible tweak needs a fast low-fi check; a costly, hard-to-undo bet earns stronger evidence. Rigor scales to stakes — over-testing a trivial call is as wrong as under-testing a big one.
- What stage are we at? Testing the problem (does anyone care?) vs. the solution (can they use/engage with it?) vs. viability/scale (will they pay, does it hold up?). Each stage points to a different method tier.
- What's the leap-of-faith belief? The one assumption that, if false, sinks the idea. Name it before anything else.
- Resources & timeline — what can realistically be run now? The cheapest method that answers the question wins.
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.
- 9d ago First seen · 100 lines · 99 tokens per session scan A 5bb7cd3b2e7b
pm-assumption-testing is a skill published in the GitHub repository Uxcel-Lab/product-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 99 tokens to every session and 2,385 once invoked, about $0.0005 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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