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Know If Its Working

Community
AIDevGTM
know-if-its-working

Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the user has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.

Overview

PublisherAIDevGTM
Repositorygtm-cofounder
Skill nameknow-if-its-working
Stars
323
Forks
31
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 AIDevGTM on GitHub. Read the source before you install it.

Installation

Install the Know If Its Working 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/AIDevGTM/gtm-cofounder/tree/main/skills/15-know-if-its-working
  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/AIDevGTM/gtm-cofounder.git /tmp/gtm-cofounder
mkdir -p .claude/skills
cp -r /tmp/gtm-cofounder/skills/15-know-if-its-working .claude/skills/know-if-its-working
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Know If Its Working 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 Know If Its Working 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 Know If Its Working 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.

Know if it's working

The only early metric that matters is net developer retention. Without it, you're not running a funnel; you're running a colander.

Use this when: you're tracking GitHub stars and pageviews and still can't answer "is GTM working?", or you're pouring effort into acquisition while new users quietly churn.

The core idea

Acquisition is worthless if users don't come back. Prove retention first; only then does spending on acquisition make sense. Most early founders optimize the top of the funnel while the bottom leaks. Fix that order.

Framework: net developer retention (Frankl)

Of all the developers who first used the product in Month 1, how many used it in Month 2? Month 3?

  • Hold it above 100% (meaning existing cohorts grow through internal referral/expansion).
  • Below solid retention, do not focus on acquisition: you're filling a leaky bucket.
  • This single cohort question tells you more than every vanity chart combined.

Framework: the DREAM metrics (Frankl)

Measure one honest number per stage, not pageviews, not stars.

StageThe metric that counts
Discoveryunique human visitors / month
Researchnewsletter subs + community joins + follows
Evaluationfree-tier signups / downloads / active free users
Activationmonthly active users · frequency · session depth
Membershipcommunity members actively posting & answering

The gate before all of it, the weekend test: can a new developer get to first value over a weekend from docs + Stack Overflow, no support call? Time-to-value target: < 1 hour ideal, 1 day max. If Evaluation/Activation fails here, no channel work will save you.

Growth benchmarks (non-ARR, Frankl)

  • Pre-seed: ~30% month-over-month user growth
  • Post-Series-A: ~10% MoM
  • First $1M ARR: within 12 months is good, 9 is excellent

Framework: attribution philosophy (Czakon)

Developer marketing is hard to attribute and that's normal. A dev sees your HN post, reads a tutorial, lurks for two months, then signs up direct.

  • Don't over-trust last-touch; it will tell you "direct/organic" and hide the real work.
  • Add a "how did you hear about us?" free-text field. Self-reported attribution beats a broken model.
  • Judge channels on trend and directional signal, not spurious precision.

Decision tree: what to fix first

Is month-2 cohort retention healthy (users come back)?
├─ NO  → STOP optimizing acquisition. Fix Evaluation/Activation (the weekend test, time-to-value).
└─ YES → is a channel reliably producing retained users?
         ├─ YES → pour more in (and only now consider paid to amplify).
         └─ NO  → go back to first-50-users; find the channel before scaling spend.

Mistakes that look reasonable

  • Vanity metrics: stars, pageviews, impressions. They feel like progress and predict nothing.
  • Acquisition over a leaky bucket: buying users who never return.
  • Demanding clean attribution: chasing a perfect model instead of acting on directional signal.
  • Ignoring the weekend test: a beautiful funnel that dies at first-value.

Your next 30 minutes

  • Compute one number: of the devs who first used it 8 weeks ago, what % used it in the last 2 weeks?
  • Pick one honest metric per DREAM stage; delete the vanity charts from your dashboard.
  • Time yourself doing your own onboarding cold. Over an hour? That's your #1 GTM problem.
  • Add a "how did you hear about us?" field to signup this week.

Built from real dev-tool GTM experience, with frameworks from Adam Frankl (The Developer-Facing Startup) and Jakub Czakon (markepear.dev). If your numbers are telling a story you can't read, get a human to look with you. Reach out to me on LinkedIn.

Frequently asked questions

What does the Know If Its Working AI skill do?

Measure GTM with the metrics that matter (net developer retention, DREAM funnel) instead of vanity numbers. Use when the user has dashboards full of stars and pageviews but can't tell if go-to-market is working, or is optimizing acquisition over a leaky bucket.

Why use Know If Its Working on TypingMind?

Because you install it once and use it with any model. Know If Its Working 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 Know If Its Working in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AIDevGTM/gtm-cofounder/tree/main/skills/15-know-if-its-working. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Know If Its Working?

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 Know If Its Working?

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

Is the Know If Its Working AI skill free?

Yes. It is published on GitHub by AIDevGTM 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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