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Metrics Framework

Community
assimovt
metrics-framework

Define product metrics with a North Star, input/output tree, and counter-metrics. Use when asked to define metrics, set up measurement, choose KPIs, pick a north star metric, or build a metrics framework. Prevents vanity metrics and gaming.

Overview

Publisherassimovt
Repositoryproductskills
Skill namemetrics-framework
Stars
71
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by assimovt on GitHub. Read the source before you install it.

Installation

Install the Metrics Framework AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/assimovt/productskills/tree/main/skills/metrics-framework
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/assimovt/productskills.git /tmp/productskills
mkdir -p .claude/skills
cp -r /tmp/productskills/skills/metrics-framework .claude/skills/metrics-framework
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Metrics Framework in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Metrics Framework on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Metrics Framework is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Define metrics that drive decisions, not dashboards that collect dust. Every metric must answer: "If this number changes, what will we do differently?" If nothing changes, it's a vanity metric.

North Star Metric

One metric that captures the core value your product delivers to customers. Not revenue (that's an output). Not signups (that's vanity). The North Star reflects the moment customers get value.

Examples:

  • Slack: Messages sent in channels with 3+ members
  • Spotify: Time spent listening
  • Airbnb: Nights booked

Test: If this metric goes up, does the company sustainably grow? If yes, it's your North Star.

Input/Output Tree

Build a tree connecting your North Star to actionable inputs:

North Star: Weekly active teams with 3+ collaborators
  |
  +-- Acquisition: New signups/week
  |     +-- Website visitors
  |     +-- Signup conversion rate
  |
  +-- Activation: % who invite a teammate in 7 days
  |     +-- Onboarding completion rate
  |     +-- Time to first shared project
  |
  +-- Engagement: Documents edited per active team/week
  |     +-- Feature adoption (comments, mentions, sharing)
  |     +-- Return frequency
  |
  +-- Retention: % active at day 30
        +-- Weekly return rate
        +-- Feature breadth used

Each leaf is a metric a team can directly influence. The tree shows HOW inputs drive the North Star.

Counter-Metrics

EVERY metric gets a counter-metric. This prevents gaming and ensures you're not optimizing one thing at the expense of another.

MetricCounter-Metric
Signup conversion rateActivation rate (don't lower the bar to get signups)
Time to first value30-day retention (don't rush users past learning)
Feature adoptionTask completion rate (don't push features that confuse)
Revenue per userChurn rate (don't squeeze users into leaving)

If the metric improves but the counter-metric degrades, you've optimized the wrong thing.

Guidelines

  • CRITICAL: ALWAYS pair every metric with a counter-metric. No exceptions.
  • NEVER use vanity metrics: total signups, page views, total users, app downloads. These only go up and tell you nothing.
  • NEVER track more than 5-7 metrics actively. If your dashboard has 20 charts, nobody looks at any of them.
  • ALWAYS define metrics with exact formulas. "Activation rate" means nothing. "Percentage of new signups who invite at least one teammate within 7 days of account creation" is a metric.
  • ALWAYS set a baseline before setting a target. You can't improve what you haven't measured.
  • NEVER set targets without understanding the current number and why it's where it is.
  • ALWAYS measure outcomes (user behavior) over outputs (features shipped).

Built on Amplitude's North Star framework and the Linear Method. Skills from productskills.

Frequently asked questions

What does the Metrics Framework AI skill do?

Define product metrics with a North Star, input/output tree, and counter-metrics. Use when asked to define metrics, set up measurement, choose KPIs, pick a north star metric, or build a metrics framework. Prevents vanity metrics and gaming.

Why use Metrics Framework on TypingMind?

Because you install it once and use it with any model. Metrics Framework is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Metrics Framework in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/assimovt/productskills/tree/main/skills/metrics-framework. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Metrics Framework?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Metrics Framework?

As many as you like. As long as a model supports skills, you can use Metrics Framework with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Metrics Framework AI skill free?

Yes. It is published on GitHub by assimovt under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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