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Product Taste

Community
somnus0x
product-taste

Structured feature evaluation before enthusiasm or inertia makes the decision. Use for 'should we build this' calls: what to build, what to cut, and the test that separates the two.

Overview

Publishersomnus0x
Repositoryagt-skill-pack
Skill nameproduct-taste
Stars
74
Forks
17
Bundled files
2
LicenseApache-2.0
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by somnus0x on GitHub. Read the source before you install it.

Installation

Install the Product Taste 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/somnus0x/agt-skill-pack/tree/main/skills/product-taste
  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/somnus0x/agt-skill-pack.git /tmp/agt-skill-pack
mkdir -p .claude/skills
cp -r /tmp/agt-skill-pack/skills/product-taste .claude/skills/product-taste
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Taste 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 Product Taste 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 Product Taste 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.

Product Taste — What to Build, What to Cut

Taste isn't opinion. It's pattern recognition compressed into instinct. This skill builds product taste by forcing structured evaluation before enthusiasm or inertia makes the decision for you.

The Taste Test

When evaluating a feature, idea, or product decision — run it through five filters in order. Stop at the first filter that kills it.

Filter 1: Who suffers without this?

Name the specific person. Not "users" — one person with a context and a pain. If you can't name them, the feature is a solution looking for a problem.

Bad: "Users would benefit from group functionality." Good: "A user who found a hot take on Twitter wants to share it with friends, but can't because the flow only supports solo use."

If the suffering is hypothetical or vague — kill the feature.

Filter 2: What does this replace?

Every feature competes with doing nothing. The user's current workaround — however ugly — is working. What are they doing today instead? How painful is that workaround?

Rate the workaround pain:

  • Unbearable → Build it
  • Annoying → Maybe
  • Fine → Don't build it

If the workaround is tolerable — the feature is a vitamin, not a painkiller. Vitamins lose to painkillers every time in early-stage products.

Filter 3: Does this make the core loop stronger?

Every product has one core loop. Define yours first (e.g., discover → create → engage → resolve). Every feature should make one step of this loop faster, easier, or more frequent.

If the feature doesn't touch the core loop — it's a distraction. A settings page, an analytics dashboard, a profile customization system — none of these make more core actions happen.

Ask: "Does this make someone do the core action faster?" If no — cut it or defer it.

Filter 4: What's the cost of being right?

Assume the feature works perfectly. Users love it. Now what?

  • Does it increase surface area you have to maintain?
  • Does it add new state to manage?
  • Does it create a new failure mode?
  • Does it split attention across two things instead of focusing on one?

The best features have low cost-of-being-right. They fold into existing flows without adding new edges. The worst features succeed and then demand permanent maintenance.

Filter 5: Would you use the product WITHOUT this?

If yes — it's not essential. Ship without it. Add it when someone screams.

The products with the best taste are defined by what they refused to build, not what they shipped.

Output Format

TASTE CHECK — [feature name]

Filter 1 (Who suffers): [pass/fail] — [1 sentence]
Filter 2 (Replaces what): [pass/fail] — workaround pain: [unbearable/annoying/fine]
Filter 3 (Core loop): [pass/fail] — [which step it strengthens, or "none"]
Filter 4 (Cost of being right): [low/medium/high] — [what it adds permanently]
Filter 5 (Ship without it): [yes/no]

Verdict: [BUILD / DEFER / KILL]
[1 sentence — the real reason]

Taste-Building Exercises

Exercise 1: Teardown

Pick a product you admire. List its features. For each one: "If they removed this tomorrow, would you switch to a competitor?" The features where the answer is "no" reveal what's decoration vs load-bearing.

Exercise 2: Subtraction Game

Take your current product's feature set. Remove one feature. Which removal hurts users most? That's your most important feature. Keep removing until only the core loop remains. The order of removal IS your priority stack.

Exercise 3: The "1000 Users" Filter

Imagine your product has exactly 1000 daily active users. Which feature request would 900 of them care about? Which would 50? Build for the 900. The 50 who want niche features are loud but not representative.

Exercise 4: Study What Ships

Follow 3-5 products in your space. Track what they actually ship each month (not what they announce or roadmap). After 3 months, look at the pattern. What did they prioritize? What did they cut? What does that tell you about what the market actually values?

What This Skill Is NOT

  • Not a prioritization framework (use RICE or ICE for that — this is upstream of frameworks)
  • Not a product management process
  • Not market research
  • This is about developing the INSTINCT that precedes all of those tools

From AGT Skill Pack — free skills from Agent Dev Thailand

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Product Taste AI skill do?

Structured feature evaluation before enthusiasm or inertia makes the decision. Use for 'should we build this' calls: what to build, what to cut, and the test that separates the two.

Why use Product Taste on TypingMind?

Because you install it once and use it with any model. Product Taste 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 Product Taste in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/somnus0x/agt-skill-pack/tree/main/skills/product-taste. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Product Taste?

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 Product Taste?

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

Is the Product Taste AI skill free?

Yes. It is published on GitHub by somnus0x under the Apache-2.0 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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