pm-decision-quality-audit

pm-decision-quality-audit is a skill for Claude Code from Uxcel-Lab/product-skills. It costs 124 tokens per session (2,985 once invoked), scanned A, original, MIT.

A structured review of an important product decision or its reasoning, such as whether to build, buy, retire, re-platform, or change pricing.

In plain words
What is it for?
Use it to audit decision records, retrospectives, product calls, and statements explaining why a team chose a particular option.
Why use it?
It separates the quality of the reasoning from the eventual result, and identifies bias, missing failure cases, and process problems with severity ratings and fixes.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the uxcel plugin — 58 skills shipped together

Good fit Use it to audit decision records, retrospectives, product calls, and statements explaining why a team chose a particular option.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/uxcel-lab/product-skills/decision-quality
Install

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.

Any agent
npx skills add Uxcel-Lab/product-skills --skill decision-quality
Clone the repo
git clone --depth 1 https://github.com/Uxcel-Lab/product-skills

Made for: Claude Code.

Or install uxcel, the plugin that ships this one along with the rest of its 58 skills.

Wrote 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.

agentmods badge for pm-decision-quality-audit

README.md
[![agentmods](https://agentmods.dev/badge/skills/uxcel-lab/product-skills/decision-quality/github.svg)](https://agentmods.dev/skills/uxcel-lab/product-skills/decision-quality)
Your own site
<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.

agentmods 80×15 button for pm-decision-quality-audit

Your own site · 80×15
<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>
Per session 124 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,985 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash cd96994b393b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

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.

pm/audits/decision-quality/SKILL.md · 155 lines

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).

Read the full file on GitHub · 155 lines

Changes

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.

  1. 11d ago First seen · 155 lines · 124 tokens per session scan A cd96994b393b

Subscribe to this mod's changes

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.