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Problem Framing

Community
UnpaidAttention
problem-framing

Shape a problem before solving it — check the premise (XY problems, false dichotomies), find the canonical name for the problem shape, locate the hard kernel, and factor fuzzy asks into orthogonal decisions. Trigger this BEFORE solving whenever a request specifies a mechanism rather than an outcome ("add a retry here"), offers a this-or-that choice ("should we use A or B?"), poses a fuzzy design/diagnostic question, or feels like something that must have been solved a thousand times before. Do NOT trigger for turning an already-well-framed build request into an ordered plan (that is task-planning, which runs AFTER this) or for choosing between candidates you've already scoped (architecture-decisions). This skill decides whether you're solving the right problem in the right frame; its successors decide how.

Overview

PublisherUnpaidAttention
Repositoryfable5-methodology
Skill nameproblem-framing
Stars
94
Forks
21
Bundled files
Instructions only
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 UnpaidAttention 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/UnpaidAttention/fable5-methodology/tree/main/skills/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/UnpaidAttention/fable5-methodology.git /tmp/fable5-methodology
mkdir -p .claude/skills
cp -r /tmp/fable5-methodology/skills/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

Weaker models answer the question as handed to them. The frame — what's actually being asked, what shape the problem is, where the difficulty lives — is accepted without examination, and a perfectly-executed answer to the wrong frame is worthless. Run these four moves, in order, before solving anything non-trivial. Total cost: a minute. Then commit to the frame and solve.

Move 1: Check the premise — once, cheaply

  • XY problem tell: the request names a mechanism, not an outcome ("add a retry here", "make this a singleton"). The stated mechanism may be a poor path to the unstated goal. Response: deliver what was asked AND name the suspected goal with a sturdier path in one line — "this adds the retry; if the underlying goal is zero lost orders, an outbox pattern is sturdier — say the word."
  • False dichotomy tell: "A or B?" Spend one beat checking for a dominant option C. If C exists, present it as an option — and still answer A-vs-B on its merits. Never refuse the question that was asked.
  • Discipline: question the frame exactly ONCE. If the user reaffirms their frame, execute it fully and drop the reframe. Relitigating a settled premise is nagging, not insight.

Move 2: Name the problem

Strip the problem to one sentence with the domain nouns removed, and ask: what is the canonical name for this shape?

  • "Many workers all retry at the same instant" → thundering herd.
  • "Check if it exists, then create it" → TOCTOU race / upsert problem.
  • "The same message may arrive twice" → idempotency.
  • "One query per row of the parent query" → N+1.
  • "Two systems each think they're primary" → split brain.

If the shape has a name, it has a literature: known solutions AND known pitfalls. Solving a named problem from scratch reinvents the bugs along with the wheel. Adopt the canonical solution unless a real constraint forbids it — and name that constraint explicitly when you deviate. If you can't find a name after a genuine attempt, note that: truly nameless problems deserve more design caution.

Move 3: Locate the hard kernel

Most tasks are ~80% mechanical, ~20% genuinely hard — and the outcome is decided by the 20%. Triage each part with one question: "could I write this straight through without backtracking?"

  • Yes → mechanical. Execute cheaply, later.
  • No — you can't yet see the solution's shape → that's the kernel. It gets solved FIRST, with full explicit reasoning (candidates, assumptions, devil's advocate), before any boilerplate is typed.

If you finish and nothing ever made you slow down, either the task was trivial or you missed the kernel — re-scan before delivering.

Move 4: Factor fuzzy asks into orthogonal decisions

A fuzzy problem ("make our exports better") is usually 2–4 independent decisions tangled together (format? delivery mechanism? sync-vs-async? permissions?). List the axes; confirm they're independent (a choice on one doesn't force a choice on another); decide or ask per axis. A question that seemed vague becomes 3 small crisp ones — and the user can answer each with one word.

Worked example

Request: "Should we use Redis or Postgres for the job queue?"

  • Move 1 (premise): dichotomy tell → one beat for C: the codebase already runs on Postgres and the volume is ~200 jobs/hour. A dedicated queue (C: keep it in Postgres with SKIP LOCKED — or D: a managed queue) may dominate. Present C, still answer A-vs-B.
  • Move 2 (name): the shape is a work queue with at-least-once delivery — canonical patterns: SELECT ... FOR UPDATE SKIP LOCKED (Postgres), Redis streams, SQS. Known pitfalls attached to each: visibility timeouts, poison messages, redelivery.
  • Move 3 (kernel): the hard part isn't the store — it's failure semantics (what happens when a worker dies mid-job). That decision gets the explicit reasoning; the client library choice is mechanical.
  • Move 4 (axes): (a) delivery guarantee needed, (b) throughput, (c) operational budget for a new stateful service. Three crisp questions instead of one vague one.
  • Outcome: "At 200 jobs/hour with Postgres already deployed: SKIP LOCKED queue in Postgres, no new infra; the real decision is at-least-once + idempotent handlers (named pattern), here's that design. If you specifically want Redis anyway, it's fine — say so and I'll build that."

Done when

The premise has been checked exactly once (and any reframe offered in one line, not litigated); the problem's canonical name is identified or its absence noted; the hard kernel is named and scheduled first; a fuzzy ask is factored into independent axes each decidable in one word — and you can state, in one sentence, the frame you are now committing to solve. Then hand off to task-planning / architecture-decisions to execute within that frame.

Frequently asked questions

What does the Problem Framing AI skill do?

Shape a problem before solving it — check the premise (XY problems, false dichotomies), find the canonical name for the problem shape, locate the hard kernel, and factor fuzzy asks into orthogonal decisions. Trigger this BEFORE solving whenever a request specifies a mechanism rather than an outcome ("add a retry here"), offers a this-or-that choice ("should we use A or B?"), poses a fuzzy design/diagnostic question, or feels like something that must have been solved a thousand times before. Do NOT trigger for turning an already-well-framed build request into an ordered plan (that is task-pl...

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/UnpaidAttention/fable5-methodology/tree/main/skills/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?

It is published on GitHub by UnpaidAttention. Check the repository for licensing terms. 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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