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Figure It Out

OrganizationPopular
cursor
figure-it-out

Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.

Overview

Publishercursor
Repositoryplugins
Skill namefigure-it-out
Stars
8K
Forks
728
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 cursor on GitHub. Read the source before you install it.

Installation

Install the Figure It Out 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.
  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/cursor/plugins.git /tmp/plugins
mkdir -p .claude/skills
cp -r /tmp/plugins/pstack/skills/figure-it-out .claude/skills/figure-it-out
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Figure It Out 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 Figure It Out 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 Figure It Out 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.

Figure it out

When the task matches no playbook, design one. The deliverable before any code is the workflow itself: a sequence of phases that scales rigor to the task, runs the scientific method, and leaves a decision trail a human can audit after stepping away. Bias toward more rigor. The cost of building the wrong thing dwarfs the cost of being careful.

Start

Open a todolist whose first item is to read the Principles section of the poteto-mode skill. Then add the phases below as todos.

Phase A: Frame

Ground first, then commit. Don't start the run until you can state:

  • The definition of done as a falsifiable predicate (the prove-it-works principle skill).
  • Scope, quantified: rough units and effort, plus the blockers grounding surfaced.
  • The rigor level, biased high. One-way doors and high blast radius get more. Reversible low-stakes steps get less. Rigor is gates and artifacts, not "try harder".

Present the framing and tradeoffs before committing to a long run. Reversible work proceeds (the never-block-on-the-human principle skill), but a multi-hour run earns one checkpoint.

Phase B: Design the workflow

Decompose into atomic, independently-landable units. Sequence riskiest-unknown-first. Scaffold and verification come before features (the foundational-thinking principle skill).

  • Build the verification harness before the work, with the baseline captured from the pre-change state, so the check reads as "old value vs new value".
  • For one-way-door design decisions, run the architect skill (it runs arena). Skip it for mechanical work whose shape is already concrete. A second arena over a settled design is over-engineering (the laziness-protocol principle skill).
  • Decide what fans out. Parallelize only across seams, and give each worker its own worktree or branch (the separate-before-serializing-shared-state principle skill). Don't over-fan.
  • Write the designed phase list down. That list is what the human reviews.

Then execute the design. Add its steps to the todolist as concrete items, after the Phase C entry and before Phase D. Run each under the Phase C loop discipline, and weave the Phase D log through them, a row as each step lands, rather than saving the whole trail for the end.

Phase C: Run the loop

Each unit is an experiment. State the hypothesis, make the smallest change, measure against the predicate on the real artifact, keep it if it advanced, revert it if it didn't. Apply the sequence-verifiable-units principle skill, verifying each unit before starting the next instead of batching checks at the end.

  • Verify by inspecting the artifact, never a self-report. When something passes too easily, suspect the observation method before the system.
  • Pair delegated work with a judge and audit the delegates' artifacts yourself before trusting them. If a worker games the gate, reset and harden the contract. If the gate itself is wrong, fix the gate in its own change rather than routing around it.
  • A verdict is VERIFIED, NOT VERIFIED, or INCONCLUSIVE. Inconclusive is not a pass. Don't hide a negative.

Phase D: Keep the audit trail

Log the run via the show-me-your-work skill, one canonical TSV with a row per decision and per unit, evidence as links. figure-it-out's work is usually ambitious enough to commit the trail so the reviewer can read it in the PR. Commit it when confidence has to be shown. Prefer evidence produced by committed scripts. The trail plus the diff is what lets the human come back and trust the work.

Phase E: Verify and hand back

Check the whole against the Phase A predicate on the real product, not just the harness. Encode any recurring correction as a gate, a lint rule, a check, or a script (the encode-lessons-in-structure principle skill).

Reply: the playbook you designed, the rigor level and why, the decision-trail path, what's verified against the predicate, and what's still open.

Frequently asked questions

What does the Figure It Out AI skill do?

Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions via show-me-your-work. Use for /figure-it-out, 'figure it out', a large migration, or when no narrower playbook applies.

Why use Figure It Out on TypingMind?

Because you install it once and use it with any model. Figure It Out 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 Figure It Out in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cursor/plugins/tree/main/pstack/skills/figure-it-out. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Figure It Out?

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 Figure It Out?

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

Is the Figure It Out AI skill free?

It is published on GitHub by cursor. 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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