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pmstack-metrics

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UpdatedApril 25, 2026 at 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.

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RyanAlberts

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SKILL.md

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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.

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Grades an eval the user already has against Anthropic's eval-design principles and returns an Eval Report Card — a letter grade A–F, the failures that earned it with the offending line, the single highest-leverage fix, and a shareable verdict card. It is the inverse of /eval (which writes a suite); this checks whether a suite is any good. Use when the user says "grade my eval," "is my eval any good," "review my eval suite," "roast my eval," "what's wrong with this eval," "did I write this eval right," pastes an eval YAML or rubric and asks for feedback, or wants to check an eval against the Anthropic framework before trusting it. Catches grader leakage, missing negative cases, no target, vibes-as-metric, and uncalibrated LLM-as-judge. Accepts a file path, a pasted/attached eval, or a plain-English success metric. Output: outputs/eval-grade-<feature>-<date>.md.

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Walks a PM through Anthropic's Step 6 ritual — reading transcripts from many trials to diagnose every failed eval task as one of three things — model mistake, grader mistake, or task-spec error. Implements the practice Anthropic describes as "critical" — without it, badly-calibrated graders mask real model improvements. Use when the user has a /run-eval result with failures, asks "why did this fail?", says "let's read the transcripts," wants to debug an eval, suspects grader bugs, or mentions Anthropic's transcript-review practice. Accepts a run folder path, pasted summary + cases, or attached files. Output: a markdown memo at outputs/transcript-review-<feature>-<date>.md with verdict counts and per-trial diagnoses.

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Walks a PM through the layer-1 ritual of reading raw transcripts of an AI feature in action — what Anthropic calls "manual testing, dogfooding, and intuition" — before formalizing a structured eval. Surfaces failure patterns, drafts task candidates (including balanced negative cases per Anthropic Step 3), and produces a verdict on whether the feature is ready for /eval. Use when the user says "I want to vibe-test this," "I have transcripts of our AI feature," "before we write evals let's read the data," "we have support tickets / bug reports about this AI feature," or asks how to start an eval from scratch. Accepts pasted transcripts, attached files, or a folder path. Output: a markdown memo at outputs/vibe-test-<feature>-<date>.md.

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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.