021 Problem Framing

Community

jabrena

021-problem-framing

Use when a problem or issue needs explicit framing before deeper analysis begins — establishing the Problem statement, Current state, Desired state, Stakeholders, and Success criteria. This should trigger when an issue's Problem Framing point of view needs evaluation, or when a maintainer directly asks to frame a problem before root-cause analysis, design, or planning begins. Part of Plinth Toolkit

Overview

Publisher	jabrena
Repository	`plinth`
Skill name	`021-problem-framing`
Stars	455
Forks	95
Bundled files	1
License	Apache-2.0
Links	Skill source Repository Homepage

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.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by jabrena on GitHub. Read the source before you install it.

Installation

Install the 021 Problem Framing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/jabrena/plinth/tree/main/skills/021-problem-framing
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.

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/jabrena/plinth.git /tmp/plinth
mkdir -p .claude/skills
cp -r /tmp/plinth/skills/021-problem-framing .claude/skills/021-problem-framing

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 021 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 021 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 021 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

Guide production of a Problem statement, Current state, Desired state, Stakeholders, and Success criteria for a problem or issue under exploration. This is an interactive SKILL.

What is covered in this Skill?

Stating the problem independently of any specific solution
Describing the current state using observable facts
Describing the desired state as an outcome, not an implementation mechanism
Identifying stakeholders affected by, accountable for, or informed about the problem
Defining success criteria that make problem resolution verifiable
Feeding a clarified problem frame into 022-root-cause-analysis and the remaining Functional Specification lenses

Constraints

Frame the problem before any deeper analysis. When this technique is orchestrated by another workflow, the orchestrator owns clarifying-question sequencing; when applied standalone, ask directly.

MUST read references/021-problem-framing.md before applying Problem Framing guidance
MUST state the problem independently of any specific solution, technology, or implementation
MUST describe the current state using observable facts, not opinions or assumed causes
MUST describe the desired state as an outcome or capability, not a specific mechanism
MUST identify stakeholders affected by, accountable for, or informed about the problem, not only the requester
MUST define success criteria that are observable or measurable
MUST NOT invent a problem statement, state, stakeholder, or success criterion when the available content is vague or ambiguous; flag the gap for a clarifying question instead

When to use this skill

Frame this problem before deeper analysis
Apply problem framing to this issue
Establish the problem statement, current state, and desired state
Identify stakeholders and success criteria for this problem
Draft the Problem Framing section of a Functional Specification

Workflow

Read the Reference

Read references/021-problem-framing.md, then identify what evidence is already available for the problem statement, current state, desired state, stakeholders, and success criteria.

Separate the Problem from any Solution

Strip out wording that already assumes a fix, technology, or implementation, and restate the gap in observable terms.

Describe Current and Desired State

Describe the current state as observable facts and the desired state as a target outcome or capability.

Identify Stakeholders and Success Criteria

List stakeholders affected by, accountable for, or informed about the problem, then define observable or measurable success criteria.

Report the Problem Frame

Report the Problem statement, Current state, Desired state, Stakeholders, and Success criteria, and flag any field left open pending a clarifying answer.

Reference

For detailed guidance, examples, and constraints, see references/021-problem-framing.md.

Bundled files

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

references/021-problem-framing.md7 KB

Frequently asked questions

What does the 021 Problem Framing AI skill do?

Why use 021 Problem Framing on TypingMind?

Because you install it once and use it with any model. 021 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 021 Problem Framing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jabrena/plinth/tree/main/skills/021-problem-framing. 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 021 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 021 Problem Framing?

As many as you like. As long as a model supports skills, you can use 021 Problem Framing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the 021 Problem Framing AI skill free?

Yes. It is published on GitHub by jabrena 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Problem Framing

Constraints

When to use this skill

Workflow

Reference

Bundled files

Frequently asked questions

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