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Figure Designer

Free

Design impactful figures for technical papers.

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Free · Opens the source repo

What Figure Designer does

The Figure Designer skill is tailored for researchers and academics who need to create effective visual representations for their technical papers. It focuses on the three essential figures that significantly influence a paper's reception: the Motivated Example, the Solution Overview, and the Experimental Results. These figures are critical in conveying complex ideas succinctly, and the skill provides guidance on how to design them effectively based on the user's intent, research area, and target venue.

When a user requests assistance with figure design, the skill evaluates the context and recommends the most suitable design paradigm, layout, labeling, and tools for each figure type. It also conducts a quality-control audit to ensure the figures meet universal design standards, such as using vector formats, appropriate font sizes, and color-blind-safe palettes. This structured approach helps users avoid common pitfalls that can undermine the quality of their visual data presentation.

Ideal for researchers preparing figures for submission to high-impact journals, the Figure Designer skill is used at various stages of figure creation. Whether the user is starting from scratch or seeking a design audit for an existing figure, this skill provides comprehensive support. It emphasizes the importance of clear communication through visuals, ensuring that figures not only look professional but also effectively convey the intended message.

However, this skill is not suitable for users needing generic plotting help outside the context of a technical paper or those who have not yet structured their paper. For users looking for a review of a completed paper, other skills may be more appropriate. Overall, the Figure Designer skill is an essential tool for academics aiming to enhance the visual impact of their research presentations.

When to use it

Use this skill when creating core figures for a technical paper or when needing a design audit for existing figures.

When not to use it

Avoid this skill for general plotting assistance or when the paper's structure is not yet defined.

What you can build with it

Designing a Motivated Example

A researcher needs to create a compelling Motivated Example figure for their paper and uses the skill to get layout and labeling recommendations.

Quality Audit of Experimental Results

After creating figures for their experiments, a user runs a quality audit to ensure compliance with design standards.

Choosing the Right Chart Type

An author is unsure which chart type to use for their results and consults the skill for guidance based on their data.

How to install Figure Designer

View source

1. Install with the skills CLI

npx skills add hkustdial/supervisor-skills/figure-designer --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by hkustdial

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 intro-drafter or tech-paper-template first to decide what figures the paper needs.
  • The user wants a review of an already-finished paper. Use 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, load the image with the Read 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

  • Type: <motivated-example or solution-overview or experimental-results>
  • Reason: <one sentence>

2. Paradigm recommendation

  • Paradigm: <name>
  • Why this paradigm: <rationale>
  • Alternatives considered and rejected: <list>

3. Layout sketch

  • Canvas: <size>
  • Panels: <list with positions and contents>
  • Arrows and connections: <list>
  • Colour assignment: <mapping>

4. Labelling and annotations

  • Element names: <list>
  • Critical highlights: <list>
  • Font sizes: <target>
  • Colour palette: <name>

5. Tool suggestion

  • Primary: <tool>
  • Alternative: <tool>
  • Reason: <rationale>

6. Universal rule audit

  • Vector format: <pass or fail>
  • Font size: <pass or fail>
  • Colour-blind safe: <pass or fail>
  • Self-contained caption: <pass or fail>
  • Honest axis range (if applicable): <pass or fail>
  • No chartjunk: <pass or fail>

7. Integrity gate result

  • Gate 1-7: <pass or fail>

8. Severity summary

  • <n> CRITICAL, <m> MAJOR, <k> MINOR
  • Top three actions first: ...

Frequently asked questions about Figure Designer

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