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Loft

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Yeachan-Heo
loft

Loft the shape before cutting steel — answer a design question that prose cannot settle by building a throwaway artifact: a pure logic module in a clickable shell, or structurally different UI variants behind one route. The captain reacts to the artifact; the answer folds into the decision; the artifact never docks. Use when a design question stalls in words, when a navigator map carries a loft ticket, or when a spec discussion reaches "we would have to see it".

Overview

PublisherYeachan-Heo
Repositoryoh-my-claudecode
Skill nameloft
Stars
39.8K
Forks
3.6K
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 Yeachan-Heo on GitHub. Read the source before you install it.

Installation

Install the Loft 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/Yeachan-Heo/oh-my-claudecode/tree/main/skills/loft
  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/Yeachan-Heo/oh-my-claudecode.git /tmp/oh-my-claudecode
mkdir -p .claude/skills
cp -r /tmp/oh-my-claudecode/skills/loft .claude/skills/loft
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Loft 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 Loft 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 Loft 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.

Loft

Cut no steel until the shape is fair. Shipwrights loft the hull's lines full-scale and fair them before a single plate is cut. Loft applies the same order to design questions: when words have run out — two plausible answers, neither falsifiable by talk — build a cheap runnable artifact, let the captain react to it, and fold that reaction into the decision.

The role contract. The crew builds the artifact; the captain's only job is to react — and that reaction is the decision. The artifact is an instrument for seeing, never a head start on the deliverable.

When to loft

  • a design question stalls in prose: repeatedly rephrased, still not settleable ("how should the path page look", "does this state model feel right")
  • a navigator map carries a loft ticket
  • a spec discussion reaches "we would have to see it first"

Do not loft when:

  • the question is answerable from repo evidence or by asking the captain (that is interviewing, not lofting)
  • the destination itself is unclear (that is fog — /oh-my-claudecode:ask-navigator)
  • the answer already lives in an ADR (do not re-loft settled decisions)
  • the "design question" is actually a bug (that is the debugger's jurisdiction)

Choose the fork

Pick by what the question is asking. A wrong fork wastes the whole artifact.

Logic fork — is the model right?

For questions about state, business rules, data shape, or algorithms ("does unlocking regress on a retake?", "does this reducer hold at the edges?"):

  1. Isolate the decision-bearing core — the state machine, reducer, schema, or algorithm — as a pure module: no DOM, no I/O, no framework. Written so that, once the shape is confirmed, it moves into the real codebase unchanged.
  2. Wrap it in a single clickable HTML shell: one button per scenario, the tricky edge cases walked through in order, full state visible after every click.
  3. On confirmation the module is ready to lift — it re-enters as real work through launch. During the loft itself main stays untouched and the shell stays behind on the branch.

UI fork — what should it look like?

For questions of shape and arrangement ("steps or cards?", "where does progress live?"):

  1. One route, three variants that differ in structure — information architecture, navigation model, density. Variants that differ only in styling answer nothing: three reskins are wallpaper.
  2. A switcher on the page cycles the variants; embed into the existing product where possible, so real data density stresses the design instead of three tidy fake rows.
  3. On confirmation, the decision transfers and the variants stay behind on the branch as the record of why it looks this way.

The discipline

  • One command to run — the repo's simplest existing serve command; inline the module into the shell if file:// module CORS would bite. Loads in seconds; no build step beyond what the repo already has.
  • No persistence, no tests, no abstractions, no error handling beyond the happy path. The artifact should be something you would not want to ship — the rules exist to keep it that way.
  • Expect the first shape to be argued with: revise on the branch within the same session. The captain confirming the final shape is the decision; an artifact nobody argues with usually answered a question nobody had.
  • The artifact's whole life is minutes to build and one session to decide. If it starts growing into production code, stop: that work is a launch effort, not a loft. Sunk cost is the failure mode this skill exists to prevent.
  • One loft answers one question. Two questions are two lofts — or one question that was actually two.

Fold the answer

The artifact is evidence, not a landing. When the captain reacts:

  1. Record the decision where it lives — the issue comment, the spec's Implementation Decisions, or the pending-decision note — in the captain's own words plus one line of why. A fragment more precise than prose (the reducer, the state machine, the schema) may be inlined there, marked as lofted.
  2. Push the artifact to a loft/<name> branch. It never merges: it stays as the primary source a future reader can open when the decision's "why" matters.
  3. Nothing from the artifact lands in main — for the logic fork the confirmed module re-enters as real work through launch, for the UI fork the real page is built fresh from the decision.

Scope and non-goals

  • Loft produces answers, not deliverables: no runtime, no state files, nothing always-on.
  • It does not diagnose (debugger), does not chart fog (/oh-my-claudecode:ask-navigator), does not approve its own answer (the captain's reaction is the only acceptance).
  • It does not replace seam approval: a lofted UI shows the shape; the test seams for it are still approved at C2.

Completion definition

The captain reacted, the answer sits where the decision lives, the artifact sits on its branch — and no line of the artifact is in main.

Frequently asked questions

What does the Loft AI skill do?

Loft the shape before cutting steel — answer a design question that prose cannot settle by building a throwaway artifact: a pure logic module in a clickable shell, or structurally different UI variants behind one route. The captain reacts to the artifact; the answer folds into the decision; the artifact never docks. Use when a design question stalls in words, when a navigator map carries a loft ticket, or when a spec discussion reaches "we would have to see it".

Why use Loft on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yeachan-Heo/oh-my-claudecode/tree/main/skills/loft. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Loft?

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 Loft?

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

Is the Loft AI skill free?

Yes. It is published on GitHub by Yeachan-Heo 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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