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-rigorgit 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-rigor)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/assumption-rigor"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/assumption-rigor/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-rigor"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/assumption-rigor.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 | $0.00124 | $0.02868 |
| Opus 5 | $0.00062 | $0.01434 |
| Sonnet 5 | $0.00025 | $0.00574 |
| Haiku 4.5 | $0.00012 | $0.00287 |
Grade A, and why
pm-assumption-rigor-audit 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 11d 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 — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assumption Rigor Audit Skill
What this skill changes vs. default behavior
By default, Claude accepts validation at face value — "we ran a survey and users liked it, so it's validated" passes without challenge. It rarely asks whether the riskiest assumption was the one tested, whether the method was strong enough for the decision it's meant to support, whether "would you use this?" answers were mistaken for evidence, whether success was defined before the test, or whether the sample was big enough to mean anything. This audit forces four things: every finding names the violated principle; the decision the test is meant to support is the unit of judgment (rigor scales to what's at stake); evidence claims are tested for signal vs. noise; and findings come severity-rated by decision damage with a concrete fix.
This is an evaluative skill: it auto-runs whenever a validation plan, experiment, discovery approach, or a "we validated…" claim is being reviewed — and as the validation step after pm-assumption-testing produces a test plan.
Scope discipline. When invoked directly (the user named this audit), review only this concern — don't pull in sibling audits. It runs alongside other lenses only when the pm-product-review orchestrator or a generative skill calls it under docs/orchestration-policy.md, where it sits in the always-relevant PM lens — auto-runs. Explicit scope always wins.
The framework — what to check and what a violation looks like
1. The riskiest assumption was tested — not the comfortable one
Assumptions should be mapped on importance × certainty; the high-importance / low-certainty ones (the leap-of-faith beliefs) carry the risk and get tested first. Testing everything indiscriminately, or testing what's easy, is wasted motion.
Flag when: the load-bearing assumption ("users will pay for this," "this is even a problem") is never named or never tested while easy/safe ones are; "we're testing everything" with no prioritization; the idea was tested but the beliefs the idea depends on weren't.
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.
- 11d ago First seen · 155 lines · 124 tokens per session scan A d6472615f363
pm-assumption-rigor-audit is a skill published in the GitHub repository Uxcel-Lab/product-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 124 tokens to every session and 2,868 once invoked, about $0.0006 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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