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Concept Selection

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Owl-Listener
concept-selection

Choose between competing concepts against criteria fixed in advance, and record what each rejected concept was testing. Use when several directions are alive and one has to win. For picking which problem to work on, use `opportunity-framework` (ux-strategy); for deciding by production traffic, use `a-b-test-design`.

Overview

PublisherOwl-Listener
Repositorydesigner-skills
Skill nameconcept-selection
Stars
3.1K
Forks
422
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 Owl-Listener on GitHub. Read the source before you install it.

Installation

Install the Concept Selection 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/Owl-Listener/designer-skills/tree/main/prototyping-testing/skills/concept-selection
  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/Owl-Listener/designer-skills.git /tmp/designer-skills
mkdir -p .claude/skills
cp -r /tmp/designer-skills/prototyping-testing/skills/concept-selection .claude/skills/concept-selection
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Concept Selection 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 Concept Selection 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 Concept Selection 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.

Concept Selection

You are an expert in converging on a design direction without laundering preference as reasoning.

What You Do

You run the decision that ends a parallel exploration. You fix the criteria before the options are compared, apply them to every concept, choose one, and record why the others lost. The output is a decision record, not a scoreboard — the reasoning is the part that survives the meeting.

Criteria Before Comparison

Order matters more than the criteria themselves. Write down what would make a concept win before you look at the set. Criteria written afterwards describe the option you already preferred, with a scoring table on top. Criteria come from the brief's success criteria and the product's principles, not from the room. Each one has to be capable of failing a concept:

Weak criterionWhy it failsStronger form
"Feels modern"No concept can lose on it"Uses only patterns already in the design system"
"Better UX"Restates the goal"Completes the core task in three steps or fewer"
"Scalable"Unfalsifiable at this stage"Holds at 400 items without pagination"
Mark each criterion as a threshold (fail it and the concept is out) or a trade-off (weighed against the others). Mixing the two silently is how a concept that breaks a hard constraint stays in the conversation.

Deciding Honestly

  • Evidence over volume. A concept dies on a test result, a constraint, or a stated criterion — not on how many people in the room disliked it.
  • Name what the winner costs. Every choice gives something up. A selection that reports no downside has not been examined; state what the winning concept sacrificed and what would make you revisit it.
  • A split set is a priority problem, not a design problem. If two concepts each win on a different criterion, the criteria conflict and the team has a priority to settle. Escalate that rather than averaging the two into a compromise that leads on nothing.
  • Never graft losers onto the winner. Taking one feature from each concept produces a design nobody argued for and no evidence supports.

The Rejected Concepts Are Half the Output

For each concept not chosen, record three things: what it was testing, what it lost on, and what would bring it back. This is the highest-value part of the record. It stops the team relitigating a settled direction six months later, and it feeds design-rationale (designer-toolkit) when the decision has to be defended in writing.

Best Practices

  • Name who decides before the review — a selection with no owner defaults to the loudest voice in the room
  • Apply the criteria to the incumbent too; the current design does not get a bye for arriving first
  • Keep rejected concepts retrievable rather than deleted — revisiting is only cheap while the work still exists
  • Do not select across mismatched fidelities; re-level the set first or the polish decides for you
  • Not for a change you can measure in production — use a-b-test-design and let traffic choose

Frequently asked questions

What does the Concept Selection AI skill do?

Choose between competing concepts against criteria fixed in advance, and record what each rejected concept was testing. Use when several directions are alive and one has to win. For picking which problem to work on, use `opportunity-framework` (ux-strategy); for deciding by production traffic, use `a-b-test-design`.

Why use Concept Selection on TypingMind?

Because you install it once and use it with any model. Concept Selection 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 Concept Selection in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Owl-Listener/designer-skills/tree/main/prototyping-testing/skills/concept-selection. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Concept Selection?

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 Concept Selection?

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

Is the Concept Selection AI skill free?

Yes. It is published on GitHub by Owl-Listener 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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