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Problem Framing

Organization
getcrew44
problem-framing

Use when the user describes a goal but the underlying problem is unclear, or when a request feels like a premature solution.

Overview

Publishergetcrew44
Repositorycrew44
Skill nameproblem-framing
Stars
357
Forks
12
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 getcrew44 on GitHub. Read the source before you install it.

Installation

Install the Problem Framing 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/getcrew44/crew44/tree/main/daemon/internal/presets/defaultcrew/skills/partner/problem-framing
  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/getcrew44/crew44.git /tmp/crew44
mkdir -p .claude/skills
cp -r /tmp/crew44/daemon/internal/presets/defaultcrew/skills/partner/problem-framing .claude/skills/problem-framing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Problem Framing 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 Problem Framing 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 Problem Framing 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.

Problem Framing

Translate a fuzzy request into a clear problem statement before any work begins.

When to use

  • The user describes what they want but not why.
  • The request jumps to a specific solution.
  • Multiple interpretations are possible and they diverge in effort or outcome.

Steps

  1. Restate the request in plain language. Confirm with the user.
  2. Ask: who is the user, what are they trying to accomplish, what is in their way today?
  3. Identify hidden assumptions in the request and name them.
  4. Offer 2-3 framings of the problem if more than one fits. Let the user pick.
  5. State the chosen framing back as a single sentence the user can confirm.

Anti-patterns

  • Restating jargon back without unpacking it.
  • Asking five clarifying questions at once.
  • Skipping confirmation and proceeding on a guess.

Frequently asked questions

What does the Problem Framing AI skill do?

Use when the user describes a goal but the underlying problem is unclear, or when a request feels like a premature solution.

Why use Problem Framing on TypingMind?

Because you install it once and use it with any model. Problem Framing 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 Problem Framing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/getcrew44/crew44/tree/main/daemon/internal/presets/defaultcrew/skills/partner/problem-framing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Problem Framing?

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 Problem Framing?

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

Is the Problem Framing AI skill free?

Yes. It is published on GitHub by getcrew44 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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