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Writing Shape

Community
stevesolun
writing-shape

Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.

Overview

Publisherstevesolun
Repositoryctx
Skill namewriting-shape
Stars
588
Forks
69
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 stevesolun on GitHub. Read the source before you install it.

Installation

Install the Writing Shape 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/stevesolun/ctx/tree/main/imported-skills/mattpocock/writing-shape
  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/stevesolun/ctx.git /tmp/ctx
mkdir -p .claude/skills
cp -r /tmp/ctx/imported-skills/mattpocock/writing-shape .claude/skills/writing-shape
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Writing Shape 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 Writing Shape 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 Writing Shape 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.

The user has passed (or will pass) a markdown file of raw material. Treat it as the input pile — anything from a tidy list of fragments to a wall of unstructured prose to a transcript. The format does not matter. Read it end-to-end before doing anything else.

Then run a shaping session that produces a separate article document. Do not edit the raw material file — it is read-only to this skill.

If the user did not say where to save the article, ask once and remember the path. The user will be editing the article file during the session; always re-read it before writing so their edits are preserved.

The loop

  1. Read the pile. Read the input file in full. Form a sense of what's in it.
  2. Draft 2–3 candidate openings. Each opening should imply a different thesis or angle for the article. Show all of them. Force the user to pick or compose a hybrid. The chosen opening defines what the rest of the article must do.
  3. Grow paragraph by paragraph. After the opening lands, ask "given this opening, what does the reader need to hear next?" Pull material from the pile to answer. Argue about whether the next beat is a paragraph, a list, a table, a callout, a quote, a code block. Each format choice should be deliberate and defensible.
  4. Append to the article file as you go. Don't batch. Write each agreed paragraph or block immediately so the user can see the article taking shape.
  5. Loop step 3 until the article is done. The user decides when it's done.

Conversational feel

This is a grilling session inverted. In ideation, the question was "what are you actually noticing?" Here it's "what is this article actually arguing, and in what order does the reader need to hear it?" Push back. Refuse to let weak transitions slide. If a paragraph doesn't earn its place, cut it.

Specific moves to keep using:

  • "What does this paragraph do for the reader that the previous one didn't?"
  • "If I cut this, what breaks?"
  • "Is this prose, or should it be a list? Why prose?"
  • "This sentence is doing two jobs — split it or pick one."
  • "The opening promised X. We've drifted to Y. Either re-thread it or change the opening."

Pulling from the pile

Treat the raw material as a quarry, not a script. Pull a fragment, rework it to fit the surrounding paragraph, and place it. A fragment may be split across multiple paragraphs, merged with another, or paraphrased. The pile's job is to be mined; the article's job is to read as one voice.

If the pile lacks something the article needs, name the gap explicitly: "We need an example here and the pile doesn't have one — give me one now or we cut this section."

Format arguments to actually have

When choosing how to render a beat, weigh these tradeoffs out loud with the user, not silently:

  • Prose vs. list. Prose carries argument; lists carry parallel items. If items aren't truly parallel, prose is better. If they are, a list is faster to scan.
  • Inline vs. callout. Tips, warnings, and asides go in callouts (> [!TIP], > [!NOTE]) — but only if they'd genuinely derail the main argument inline. Otherwise leave them inline.
  • Table vs. repeated structure. If the same shape repeats 3+ times with the same fields, a table. Otherwise prose with bold leads.
  • Quote vs. paraphrase. Quote when the original wording is the point. Paraphrase when only the idea matters.
  • Code block vs. inline code. Multi-line, runnable, or illustrative → block. Single token or identifier → inline.

Writing rhythm

Append to the article file as each block is agreed. Re-read the file from disk before every write — the user may have edited between turns. Never overwrite blindly. If the user wants a paragraph rewritten, edit that specific paragraph in place; leave the rest alone.

Out of scope

  • Mining for new fragments that aren't in the pile (the pile is the input — if it's incomplete, name the gap and either get the user to fill it or cut the section).
  • Editing the raw material file.
  • Publishing, formatting for a specific platform, or adding frontmatter the user didn't ask for.

Frequently asked questions

What does the Writing Shape AI skill do?

Take a markdown file of raw material and shape it into an article through a conversational session — drafting candidate openings, growing the piece paragraph by paragraph, arguing about format (lists, tables, callouts, quotes) at each step. Use when the user has a pile of notes, fragments, or a rough draft and wants help turning it into something publishable.

Why use Writing Shape on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/stevesolun/ctx/tree/main/imported-skills/mattpocock/writing-shape. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Writing Shape?

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 Writing Shape?

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

Is the Writing Shape AI skill free?

Yes. It is published on GitHub by stevesolun 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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