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pmstack-metrics

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업데이트2026년 4월 25일 04:30

Design a measurement framework for an AI product or feature with North Star, supporting metrics, counter-metrics, and AI-specific quality / latency / cost metrics. Use when a PM asks "how do we measure this", "what's the North Star for X", "how would we know X is winning", or mentions instrumentation, OKRs, KPIs, or success criteria for an AI feature.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

RyanAlberts

RyanAlberts/pmstack

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Metric Framework Design

Produce a complete measurement framework for an AI feature.

Required structure

North Star — the single number that, if it goes up, the feature is winning
Supporting metrics — 2-3 (engagement, retention, quality)
Counter-metrics — 1-2 (what breaks if we over-optimize the North Star?)
AI-specific metrics — accuracy/quality (hallucination rate, user-acceptance rate), latency (p50/p95), cost-per-call

For every metric:

Definition
Formula
Instrumentation source (where does the data come from today, or what work is needed?)
Target
Alert threshold
Why this metric (justification)

Hard rules

Every metric is measurable today, OR the framework lists the instrumentation work needed
Counter-metrics must meaningfully constrain the North Star (not "user satisfaction also matters")
AI features always need accuracy + latency + cost — not just engagement

Where to write

With filesystem: outputs/metrics-<feature-slug>-<YYYY-MM-DD>.md
Inline (web/mobile): emit as markdown with the suggested filename

Tone

Data-driven, precise, analytical. Justify each metric.

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name	pmstack-metrics
description	Design a measurement framework for an AI product or feature with North Star, supporting metrics, counter-metrics, and AI-specific quality / latency / cost metrics. Use when a PM asks "how do we measure this", "what's the North Star for X", "how would we know X is winning", or mentions instrumentation, OKRs, KPIs, or success criteria for an AI feature.

Metric Framework Design

Produce a complete measurement framework for an AI feature.

Required structure

North Star — the single number that, if it goes up, the feature is winning
Supporting metrics — 2-3 (engagement, retention, quality)
Counter-metrics — 1-2 (what breaks if we over-optimize the North Star?)
AI-specific metrics — accuracy/quality (hallucination rate, user-acceptance rate), latency (p50/p95), cost-per-call

For every metric:

Definition
Formula
Instrumentation source (where does the data come from today, or what work is needed?)
Target
Alert threshold
Why this metric (justification)

Hard rules

Every metric is measurable today, OR the framework lists the instrumentation work needed
Counter-metrics must meaningfully constrain the North Star (not "user satisfaction also matters")
AI features always need accuracy + latency + cost — not just engagement

Where to write

With filesystem: outputs/metrics-<feature-slug>-<YYYY-MM-DD>.md
Inline (web/mobile): emit as markdown with the suggested filename

Tone

Data-driven, precise, analytical. Justify each metric.