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 decision-qualitygit 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/decision-quality)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/decision-quality"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/decision-quality/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/decision-quality"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/decision-quality.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 | $0.02985 |
| Opus 5 | $0.00062 | $0.01492 |
| Sonnet 5 | $0.00025 | $0.00597 |
| Haiku 4.5 | $0.00012 | $0.00298 |
Grade A, and why
pm-decision-quality-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.
Decision Quality Audit Skill
What this skill changes vs. default behavior
By default, Claude evaluates a decision by its result — if the launch worked, the call was good — and accepts a confident rationale at face value. It rarely separates the quality of the reasoning from the luck of the outcome, checks whether the deliberation matched how reversible the decision was, names the bias quietly driving the choice, or asks whether failure modes were surfaced before commitment. This audit forces four things: every finding names the violated principle; decisions are judged on the reasoning available at the time, not hindsight; the bias or process gap is named explicitly with a structural countermeasure; and findings come severity-rated by decision damage and reversibility with a concrete fix.
This is an evaluative skill: it auto-runs whenever a significant product decision, decision log, or retrospective is being reviewed — and as a validation step after a consequential call is made.
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 (for high-stakes, hard-to-reverse decisions). Explicit scope always wins.
The framework — what to check and what a violation looks like
1. Process judged on its merits, not the outcome (resulting)
"Resulting" is grading a decision by what happened next. But outcomes are shaped by forces the team didn't control (a competitor launch, a platform change, timing). The right question is: given what was known at the time, was this the best reasoning available? Use the process × outcome matrix — good process + bad outcome is bad luck (don't punish it); bad process + good outcome is dumb luck (the most dangerous quadrant — the team learns the shortcut works).
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 cd96994b393b
pm-decision-quality-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 2,985 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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