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 prioritization-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/prioritization-rigor)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/prioritization-rigor"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/prioritization-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/prioritization-rigor"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/prioritization-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 | $0.00118 | $0.02371 |
| Opus 5 | $0.00059 | $0.01185 |
| Sonnet 5 | $0.00024 | $0.00474 |
| Haiku 4.5 | $0.00012 | $0.00237 |
Grade A, and why
pm-prioritization-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 12d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prioritization Rigor Audit Skill
What this skill changes vs. default behavior
By default, Claude performs prioritization confidently — it fills RICE tables with invented numbers, ranks features by plausible-sounding impact, and presents the result with unearned precision. Reviewing someone else's prioritization, it tends to accept scores at face value. This audit forces four things: every score is traced to its evidence (or flagged as decoration), every ranking is traced to a strategic outcome (or flagged as unmoored), every "must-have" is challenged, and every decision is checked for an explicit, stress-tested trade-off. Findings come severity-rated with concrete fixes.
This is an evaluative skill: it auto-runs whenever a prioritized list, scoring table, backlog order, or build-next decision appears in work being reviewed or generated.
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 an artifact-specific lens — offered (when work is prioritized or roadmapped). Explicit scope always wins.
The framework — what to check and what a violation looks like
1. Evidence behind scores (the false-precision check)
A RICE/ICE score is only as good as its inputs. Reach should come from real usage data, impact from a hypothesis someone can defend, confidence from the quality of evidence — not from optimism. Scores invented to two decimal places are intuition wearing a lab coat.
Flag when: scores appear without sources; confidence is uniformly high; effort estimates lack engineering input; the precision of the output (e.g., "RICE 847.5") exceeds the precision of any input; scoring was applied after the decision to justify it.
- ❌ "Reach: 9, Impact: 8, Confidence: 90% — RICE says build it" (no data named anywhere)
- ✅ "Reach: ~5,000 new users/mo (signup analytics); Impact: medium (2) — activation, not revenue; Confidence: 80% (interview evidence for the problem, none yet for the solution)"
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.
- 12d ago First seen · 151 lines · 118 tokens per session scan A 04f73a092677
pm-prioritization-rigor-audit is a skill published in the GitHub repository Uxcel-Lab/product-skills (12 stars, last pushed 2mo ago), licensed MIT. It adds 118 tokens to every session and 2,371 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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