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Write Tbp

OrganizationPopular
tldraw
write-tbp

Writing technical blog posts about tldraw features and implementation details. Use when creating blog content about how tldraw solves interesting problems.

Overview

Publishertldraw
Repositorytldraw
Skill namewrite-tbp
Stars
50.4K
Forks
3.5K
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 tldraw on GitHub. Read the source before you install it.

Installation

Install the Write Tbp 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/tldraw/tldraw.git /tmp/tldraw
mkdir -p .claude/skills
cp -r /tmp/tldraw/skills/write-tbp .claude/skills/write-tbp
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Write Tbp 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 Write Tbp 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 Write Tbp 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.

Write technical blog post

This skill covers how to write technical blog posts about tldraw's implementation details.

Process

1. Create the workspace

Create an assets folder for this topic in this skill directory:

assets/<topic>/
├── research.md   # Gathered context and notes
└── draft.md      # The blog post draft

Use a short, kebab-case name for the topic (e.g., scribbles, arrow-routing, dash-patterns).

2. Research the topic

Use an Explore subagent to gather all relevant information:

Task (subagent_type: Explore, thoroughness: very thorough)

Find all code, documentation, and context related to [TOPIC] in the tldraw codebase.

Look for:
- Implementation files in packages/editor and packages/tldraw
- Type definitions in packages/tlschema
- Related examples in apps/examples
- Any existing documentation in apps/docs/content
- Tests that reveal behavior
- Comments explaining why things work the way they do

For each relevant file, note:
- What it does
- Key functions/classes
- Interesting implementation details
- Any "why" comments or non-obvious decisions

Output a comprehensive summary of how [TOPIC] works. This document will be read by another agent. No need to over-optimize for human readability.

Save the research output to assets/<topic>/research.md.

3. Identify the interesting angle

Before writing, answer these questions from the research:

  • What problem does this solve? Not "what does it do" but "what would go wrong without it?"
  • What's surprising or unintuitive? The obvious approach that doesn't work, or the hidden complexity.
  • What's the key insight? The "aha" that makes the solution work.
  • What did we try first? Any journey or iteration visible in the code or comments.

If you can't find an interesting angle, the topic may not be suitable for a technical blog post.

4. Write the draft

Create assets/<topic>/draft.md following the blog-guide structure:

  1. State the problem — What the system does and what would go wrong without it
  2. Show the insight — The key idea that makes it work
  3. Walk through the implementation — Code and explanation, building complexity
  4. Wrap up — Where it lives, tradeoffs, links to files

Target 800-1500 words.

Draft plainly

The draft is raw material for a human editor, not the finished post. Keep it clean and let the content carry it. The editor will add the personality, anecdotes, and punch where they want them; that is much easier to do on top of a plain, correct draft than to strip out of an overwritten one.

Concretely:

  • Open by saying what the system does and what the post covers. Do not open with a scene, an anecdote, or a hypothetical user ("your train goes into a tunnel..."). Those read as AI filler and the editor will cut them.
  • Build the story from the mechanism: what problem each piece solves, in the order the pieces depend on each other. Tension comes from the design (a scan can't see deletions; the pruner used to leave a hole), not from prose.
  • State journeys and bugs flatly. "An earlier version of the pruner did not advance the watermark, so stale clients received diffs with no deletes" is enough. Skip "we learned this the hard way" and similar narration.
  • No punchline sentences, no chiasmus, no closing zingers. If a sentence exists to sound good rather than to say something, cut it.
  • One em dash per paragraph at most; usually zero.
  • Keep opinions to the wrap-up, and keep them short and specific ("we accept a full redownload for clients gone long enough, in exchange for bounded metadata").
  • Where the human parts belong (opening, provenance, tradeoffs), leave the paragraph plain rather than inventing color. Mention those spots in your handoff.

5. Self-evaluate

Check the draft against the blog-guide checklist:

  • Opening — Does it state the problem plainly before the solution, without a staged anecdote?
  • Insight — Is there a clear key idea, and does the structure build toward it?
  • Specificity — Is this grounded in tldraw's actual implementation?
  • Code — Do examples build understanding, not just show syntax?
  • Tone — Plain and direct? No pathos, no punchlines, nothing an editor would recognize as AI cruft?
  • Links — Points to actual code in the repo?
  • Length — Appropriate depth for the topic?

Revise the draft to address any gaps.

6. Output

Present the final draft to the user for review, and point out where a human editor is most likely to want to add color (usually the opening, any provenance or bug story, and the tradeoffs). The draft remains in assets/<topic>/draft.md until the user is satisfied, at which point they can move it to the appropriate location.

References

  • Style guide: See ../shared/blog-guide.md for voice, tone, and structure.
  • Writing guide: See ../../VOICE.md for general writing conventions.

Frequently asked questions

What does the Write Tbp AI skill do?

Writing technical blog posts about tldraw features and implementation details. Use when creating blog content about how tldraw solves interesting problems.

Why use Write Tbp on TypingMind?

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

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

Which AI models can use Write Tbp?

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 Write Tbp?

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

Is the Write Tbp AI skill free?

It is published on GitHub by tldraw. 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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