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Phd Figure Designer

Community
Immortalqx
phd-figure-designer

Advises on the design of the three core figures in a technical paper: the Motivated Example (Figure 1), the Solution Overview (Methodology), and the Experimental Results figures. Recommends the right design paradigm, layout, labelling, and tool for each figure type, then runs a quality-control audit. Use when the user asks to 'design a figure', 'draw Figure 1', 'plot experiment results', 'choose the right chart type', 'which figure tool to use', or 'figure looks unprofessional'.

Overview

PublisherImmortalqx
Repositorymy_codex_skills
Skill namephd-figure-designer
Stars
56
Forks
0
Bundled files
6
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.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Immortalqx on GitHub. Read the source before you install it.

Installation

Install the Phd Figure Designer 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/Immortalqx/my_codex_skills/tree/main/phd-figure-designer
  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/Immortalqx/my_codex_skills.git /tmp/my_codex_skills
mkdir -p .claude/skills
cp -r /tmp/my_codex_skills/phd-figure-designer .claude/skills/phd-figure-designer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Phd Figure Designer 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 Phd Figure Designer 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 Phd Figure Designer 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 Designer

Overview

A top-venue paper typically carries six to eight figures, with three carrying almost all the storytelling weight: the Motivated Example (Figure 1, on page 1 or the top of page 2), the Solution Overview (inside the Methodology section), and the Experimental Results figures (inside the Experiments section). Reviewers scan these three in under a minute to decide whether the paper is worth reading in detail; weak figures sink otherwise-strong papers.

This skill takes the user's intent (what they want to communicate) plus context (research area, method name, target venue) and returns the recommended paradigm, a layout sketch, labelling guidance, tool suggestion, and a quality-control audit against a universal rule set (vector format, font size, colour-blind-safe encoding, self-contained caption, honest axis ranges).

When to use this skill

  • Before drawing any figure in a paper.
  • The user asks to 'design a figure', 'draw Figure 1', 'plot experiment results', 'choose the right chart type'.
  • The user has drawn a figure and wants a design audit.
  • The user is unsure which figure type or paradigm to choose.
  • Preparing camera-ready figures before submission.

When NOT to use this skill

  • The user only wants generic plotting help (bar chart, line chart) outside a paper. Regular assistance suffices.
  • The paper is not yet structured; use phd-intro-drafter or phd-tech-paper-template first to decide what figures the paper needs.
  • The user wants a review of an already-finished paper. Use phd-pre-submission-reviewer.

Core procedure

Step 1: Figure-type identification

Decide which of the three core types the figure is. If the user's request does not match any, either it is a supporting figure (use the experimental-results guidance as a base) or it does not belong in the paper.

If the mode is figure-audit and the user has provided an image path, inspect the image with the available local image-viewing tool before proceeding to Step 2. Vision-based inspection enables the universal rule audit in Step 6 to check font legibility, colour palette, raster-vs-vector tells, and chartjunk directly rather than relying on user description. If no image is provided, continue in text-only mode and mark vision-only rules (font size, raster detection, colour palette) as "user must verify" in the final audit report.

Step 2: Paradigm recommendation

See: references/motivated-example.md, references/solution-overview.md, or references/experimental-results.md depending on figure type.

Each figure type has two to three canonical paradigms. Pick the one that fits the user's storytelling need, and explain why the other paradigms fit less well.

Step 3: Layout sketch

Produce a text description of the layout: panel positions, element placement, arrows, colour assignments. The goal is that the user could draw the first draft from the sketch alone.

Step 4: Labelling and annotation guidance

  • Name every visible element concretely (no "Module A", "X", "Y").
  • Annotate critical points (failure highlight, success highlight, comparison emphasis).
  • Specify font sizes and colour palette. Default colour palette: ColorBrewer Qualitative or Viridis for sequential.

Step 5: Tool suggestion

See: references/tools.md for the tool matrix and the decision heuristic.

Default recommendations:

  • Motivated Example and Solution Overview: PowerPoint (draft), Figma (polish).
  • Experimental Results: Matplotlib or Seaborn in a reusable plot_utils.py script.
  • LaTeX-integrated figures: TikZ or PGFPlots.

Step 6: Universal rule audit

See: references/design-rules.md for the full universal rule set.

Verify every proposed or existing figure against:

  • Vector format (PDF, EPS, SVG) for export.
  • Font size at least 8pt post-scaling.
  • Small canvas (not large canvas with small fonts).
  • Colour-blind-safe palette; no colour-only encoding.
  • Self-contained caption whose first sentence states the core finding.
  • Honest axis ranges.
  • No 3D effects, no chartjunk.

Flag every violation with severity.

Step 7: Integrity gate

Run the checks in the Integrity gate section below.

Step 8: Output

Emit the full design in the Output format below.

Integrity gate

Bullets tagged [inspection] are checked by the LLM from its own output. Bullets tagged [user-verify] require the user to confirm because the check depends on either the drawn figure or knowledge the skill does not have (paper context, prior Introduction).

Before returning the design:

  1. [inspection] Paradigm matches figure type (motivated example is not a pipeline; overview is not a bar chart).
  2. [inspection] Layout sketch is concrete enough that the user could draw from it.
  3. [inspection] Labels are real entity names, not placeholders.
  4. [inspection] Tool suggestion matches the figure's complexity (not Matplotlib for a multi-icon motivated example, not PowerPoint for a 20-method bar chart).
  5. [inspection] when image provided, [user-verify] text-only Universal rule audit has been run; no CRITICAL violation is left unaddressed. Vision-only rules (raster-vs-vector, font size, colour palette) are only checkable when the user supplies an image.
  6. [user-verify] For motivated examples, the example is the same running example referenced by the Introduction (no new example introduced in Figure 1). The skill does not see the Introduction; the user confirms.
  7. [inspection] For experimental results, the chart type matches the data type (time-series uses line, multi-method comparison uses grouped bar, trade-off uses scatter).

If any [inspection] check fails, mark the design as "needs user attention". For [user-verify] items, surface them to the user as items they must confirm before submission.

Output format

1. Figure type

2. Paradigm recommendation

3. Layout sketch

4. Labelling and annotations

5. Tool suggestion

6. Universal rule audit

7. Integrity gate result

8. Severity summary

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Phd Figure Designer AI skill do?

Advises on the design of the three core figures in a technical paper: the Motivated Example (Figure 1), the Solution Overview (Methodology), and the Experimental Results figures. Recommends the right design paradigm, layout, labelling, and tool for each figure type, then runs a quality-control audit. Use when the user asks to 'design a figure', 'draw Figure 1', 'plot experiment results', 'choose the right chart type', 'which figure tool to use', or 'figure looks unprofessional'.

Why use Phd Figure Designer on TypingMind?

Because you install it once and use it with any model. Phd Figure Designer 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 Phd Figure Designer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Immortalqx/my_codex_skills/tree/main/phd-figure-designer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Phd Figure Designer?

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 Phd Figure Designer?

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

Is the Phd Figure Designer AI skill free?

Yes. It is published on GitHub by Immortalqx 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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