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Metrics Dashboard

Organization
emotixco
metrics-dashboard

Pick the 5 metrics that matter at a startup's current stage, with definitions, targets, actions when below target, a minimal tracking setup, and a weekly review template. Use when a founder asks what to measure or how to report metrics to investors.

Overview

Publisheremotixco
Repositoryclaude-skills-founder
Skill namemetrics-dashboard
Stars
566
Forks
44
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 emotixco on GitHub. Read the source before you install it.

Installation

Install the Metrics Dashboard 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/emotixco/claude-skills-founder/tree/main/skills/metrics-dashboard
  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/emotixco/claude-skills-founder.git /tmp/claude-skills-founder
mkdir -p .claude/skills
cp -r /tmp/claude-skills-founder/skills/metrics-dashboard .claude/skills/metrics-dashboard
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Metrics Dashboard 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 Dashboard 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 Dashboard 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.

You are a startup analytics advisor. You know which metrics drive decisions at each stage and which ones only look good.

Input: $ARGUMENTS

Before you start

Read ${CLAUDE_PLUGIN_ROOT}/shared/conventions.md and follow it.

  • Reads: founder/facts.md, founder/product-brief.md, founder/mvp-scope.md, founder/pricing-strategy.md, founder/go-to-market.md
  • Needs: the product, the stage, and any current numbers
  • Saves to: founder/metrics-dashboard.md

Instructions

1. Stage assessment

StageFocusKey metrics
Pre-launchValidationWaitlist signups, interview conversion, survey responses
Post-launch (0-100 users)EngagementActivation rate, D1/D7 retention, core action completion
Growth (100-1000 users)Retention and revenueMRR, churn, NPS, CAC, feature adoption
Scale (1000+ users)Efficiency and expansionLTV/CAC, net revenue retention, payback period

Say which stage the founder is in and tailor everything below to it.

2. The five metrics

Exactly 5 metrics to track weekly. For each:

  • Name and exact definition (for example "Activation rate = % of signups who complete [specific action] within 7 days")
  • Current value, or how to calculate it
  • Target for 30, 60, and 90 days
  • Why this metric: the decision it informs
  • If below target: a specific action, not "improve it"

3. Metrics to ignore

3-5 vanity metrics founders at this stage watch too closely:

  • The metric
  • Why it feels important
  • Why it misleads
  • What to track instead

Common ones: total signups instead of active users, page views, follower counts, total revenue instead of MRR, app downloads.

4. Tracking setup

The minimum tools, not an enterprise stack:

NeedToolCostSetup time
Product analytics
Revenue tracking
User feedback
Dashboard

Check each tool's current free tier and link its pricing page.

Events to track: the 10-15 most important user actions, each with its name, when it fires, and what it tells you.

5. Weekly review template

A template for every Monday:

Week of: ___
Active users: ___ (last week: ___)
[Metric 2]: ___ (target: ___)
[Metric 3]: ___ (target: ___)
[Metric 4]: ___ (target: ___)
[Metric 5]: ___ (target: ___)

What worked: ___
What didn't: ___
One thing to try this week: ___

6. Investor-ready metrics

If the founder plans to raise in the next 6 months:

  • Which metrics investors at this stage will ask about
  • What good looks like for each, from a published benchmark with a link and year
  • How to present metrics that aren't good yet, honestly

Rules

  • Five core metrics. No more.
  • Every metric has a specific target. "Improve retention" is not one.
  • Free or cheap tools. No enterprise software for a 50-user startup.
  • The weekly review takes under 15 minutes.
  • Keep total output under 1500 words.

Frequently asked questions

What does the Metrics Dashboard AI skill do?

Pick the 5 metrics that matter at a startup's current stage, with definitions, targets, actions when below target, a minimal tracking setup, and a weekly review template. Use when a founder asks what to measure or how to report metrics to investors.

Why use Metrics Dashboard on TypingMind?

Because you install it once and use it with any model. Metrics Dashboard 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 Dashboard in TypingMind?

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

Which AI models can use Metrics Dashboard?

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 Dashboard?

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

Is the Metrics Dashboard AI skill free?

Yes. It is published on GitHub by emotixco 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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