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Using Opentikz

Organization
opentikz
using-opentikz

Use when a user wants a TikZ figure for a paper — an icon, an editable architecture/pipeline/flow template, or an example — and wants you to find, edit, and verify it. The single repo-wide skill for working with the OpenTikZ library.

Overview

Publisheropentikz
Repositoryopentikz
Skill nameusing-opentikz
Stars
229
Forks
5
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 opentikz on GitHub. Read the source before you install it.

Installation

Install the Using Opentikz 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/opentikz/opentikz/tree/main/skills/using-opentikz
  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/opentikz/opentikz.git /tmp/opentikz
mkdir -p .claude/skills
cp -r /tmp/opentikz/skills/using-opentikz .claude/skills/using-opentikz
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Using Opentikz 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 Using Opentikz 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 Using Opentikz 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.

Using OpenTikZ

OpenTikZ is a library of copyable TikZ icons, editable templates (neural nets, encoder-decoder, training pipelines, system block diagrams, flowcharts), and full examples. This skill is how you go from a user's request to a finished, compiling .tex figure — reliably, and without guessing.

There is one skill (this file). Per-template knowledge lives in each template's edit_contract (inside its meta.json), which you read at edit time. Do not look for per-template skill.md files; they no longer exist.

0. First: which mode are you in, and where is the library?

Pick the mode:

  • Mode A — produce a figure for the user's own project (the default). The OpenTikZ library is read-only and lives elsewhere; the user is working in their own paper/LaTeX project. You will copy a figure out of the library and edit the copy in their project. Never modify the library.
  • Mode B — contribute back to the OpenTikZ repo itself. The user is editing the library: their working directory is the repo. Edit in place and run the repo tooling (see §6).

Locate the library root (OTROOT) for Mode A:

  1. If the environment variable ${CLAUDE_PLUGIN_ROOT} is set, you were installed as a Claude Code plugin: OTROOT = ${CLAUDE_PLUGIN_ROOT}.
  2. Otherwise (cloned repo / another agent), OTROOT is the OpenTikZ repo root — the directory two levels above this SKILL.md (skills/using-opentikz/SKILL.md → ../../).
  3. If you can only read the repo remotely (e.g. over GitHub, no local clone), treat the GitHub repo as OTROOT and fetch raw file contents on demand.

Confirm OTROOT once (e.g. list OTROOT/catalog.json) before relying on it. The output target is always the user's current working directory, never OTROOT.

1. Communicate precisely (do this first, throughout)

Your job is to produce the figure the user actually wants, not a plausible guess. Classify every ambiguity before acting:

Material ambiguity → ask, or offer two concrete alternatives. Trigger when the answer changes the figure's structure or identity, is hard to reverse, or you would otherwise be guessing between genuinely different intents. Ask one crisp question with concrete options; never a vague "what do you want?".

  • "draw a transformer" → encoder-only, decoder-only, or encoder-decoder? (ask)
  • "add attention" → self-attention or cross-attention, and where? (ask)
  • "highlight the new part" → which part is new? (ask)
  • Use offer-two-alternatives when seeing beats describing — an aesthetic or layout fork (two color schemes, two layouts). Produce both and let the user pick. Don't use it for continuous quantities (widths, counts).

Safe-default ambiguity → proceed, state the assumption in one line, keep it cheap to reverse. When there is a sensible default and changing it is a one-line tweak, don't interrupt — just decide and tell the user what you assumed.

  • palette: default light Okabe-Ito unless "dark" is mentioned.
  • width: default standalone (no \resizebox) unless a venue/column width is named.
  • spacing / size / an unspecified-but-conventional count: use the template default (e.g. a 4-layer net 4,6,6,2).

Anti-interrogation guardrail. Ask at most ~2–3 questions up front, batched; resolve everything else with assume-and-state. Every delivery ends with a one-line summary: "Assumptions: light palette, single-column width, 4 layers — say the word to change any."

2. Workflow

  1. Understand & classify. Restate the goal in one sentence; classify ambiguity per §1; ask the (few) material questions, batched.
  2. Discover. Read catalog.json at ${OTROOT} (see §0). Match the request to icons / templates / examples by name, tags, domain. If several fit, present the candidates (with their ids) and let the user choose; if one clearly fits, name it and proceed.
  3. Select & confirm. Confirm the chosen base plus the material parameters (e.g. layer counts, labels, which part is highlighted).
  4. Copy, then edit (Mode A) / edit in place (Mode B).
    • Mode A: copy the chosen item's .tex from OTROOT into the user's project (a sensible path like figures/<name>.tex); tell the user where you put it. Edit that copy, never the file under OTROOT. If the figure is already in the user's project, edit it there. For a template, read its edit_contract from OTROOT's catalog.json (or the item's meta.json) and: edit only the contract's parameters; follow its operations as the recipe; keep every invariant; preserve the node_naming scheme; colours via palette names only. For an icon/example (no contract), edit under the same hard rules.
    • Mode B: edit the item in place inside the repo, under the same rules.
  5. Verify by compiling — in the user's project (Mode A). Run the user's LaTeX on the copied/edited file (latexmk -pdf <file>.tex, or pdflatex). Fix failures before returning — never hand back a figure you didn't compile. (Regenerating .svg previews and catalog.json is a Mode B / contributor task — skip it when producing a figure for a user.)
  6. Deliver. Return the edited .tex and the one-line assumptions summary.

3. Hard rules (never violate)

  • The .tex must stay standalone-compilable (\documentclass{standalone}).
  • Colors only via the five palette names (otblue otorange otteal otpurple otgray); tints/shades like otblue!15 are fine. Never inline a hex value or a stock xcolor name (blue, red). See reference/color-palettes/.
  • Preserve node names / the node_naming scheme — they are the contract that lets edits target the right parts. Give any new node a clear semantic name.
  • Keep figures parametric — drive counts/spacing/labels through the \def block at the top; don't hard-code what a parameter already controls.
  • Separate icon from label — never overlay a \pic on a node's centred text. For any box that carries both a glyph and a label, make the box node empty ({}), then place the icon with a standalone \pic in the box's upper half and the label as a separate text node in the lower half (or icon-left / text-right for a wide chip). Size the box tall enough for both bands. Dropping a \pic on top of a node's own {text}, or faking the offset with \hspace, collides every time — this is the single most common rework.
  • Budget padding — size a box to its content plus margin, never flush to it. Set minimum width/minimum height to the widest/tallest content plus a per-side padding budget of ≥0.25 cm. For a multi-column in-box layout (e.g. two icon+label stacks), also give an explicit inter-column gap (≥0.3 cm) and keep each column's widest line clear of the frame. If any text touches the border, widen the box or push the columns inward — do not shrink the text.

4. Common cross-template operations

These work the same across templates; the per-template edit_contract lists the specific parameter/style names to touch.

Recolor. Change the palette color name in the relevant style or node, never a hex. "Make it teal" = otblue → otteal in that style's fill/draw.

Switch to the dark palette. Replace the light \definecolor block with the dark block from reference/color-palettes/color-palettes.md — the names are identical, so the body is unchanged. The dark palette is for dark backgrounds: also set \pagecolor{otpaper} (\definecolor{otpaper}{HTML}{1E1E1E}), or the tints render washed-out grey on a white page.

Adapt to a venue / column width. Wrap the whole tikzpicture in \resizebox (needs \usepackage{graphicx}):

\resizebox{\columnwidth}{!}{\begin{tikzpicture} ... \end{tikzpicture}}

In a paper use \columnwidth (single column) or \textwidth (full width); to test a target in the standalone file give an explicit width (\resizebox{8.4cm}{!}{...}). Common targets: CVPR/ICCV/ACL single column ≈ 8.4cm, full/double width ≈ 17.8cm; NeurIPS/ICML text ≈ 13.9cm. Caveat: \resizebox scales text too — if the figure is far wider than the column, first reduce content/spacing (the template's spacing parameters) and resize the rest.

5. Reference material

  • reference/color-palettes/ — the canonical five-color Okabe-Ito palette (light
    • dark blocks), the single source of truth for colors.
  • reference/annotations/ — how to add callouts, braces, and highlight labels.
  • reference/layout/ — positioning, alignment, and spacing patterns.
  • docs/DESIGN_GUIDE.md — global conventions (line width, node naming, metadata).

6. Mode B procedure — contributing back (editing the repo itself)

If the user is adding/changing library content (not just producing a figure for their paper), the repo tooling applies:

  • regenerate the preview: python3 tools/render_preview.py <file>.tex -o <dir>/preview.svg
  • regenerate the catalog: python3 tools/build_catalog.py
  • validate: python3 tools/validate.py --strict
  • a new/edited template needs an edit_contract in its meta.json (see §4 of docs/DESIGN_GUIDE.md); validate.py checks its parameters/styles exist in the .tex.

Frequently asked questions

What does the Using Opentikz AI skill do?

Use when a user wants a TikZ figure for a paper — an icon, an editable architecture/pipeline/flow template, or an example — and wants you to find, edit, and verify it. The single repo-wide skill for working with the OpenTikZ library.

Why use Using Opentikz on TypingMind?

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

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

Which AI models can use Using Opentikz?

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 Using Opentikz?

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

Is the Using Opentikz AI skill free?

It is published on GitHub by opentikz. 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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