phd-figure-designer
Skill54 starsChanged 5 months agoMITView on GitHub
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'.
In this repository27 other files it gives its agents.
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The file
We split this skill into the rules it gives an agent
6 rules. About 0 are common, 6 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 24
Before drawing any figure in a paper.
- Distinctive: line 74
Verify every proposed or existing figure against:
- Distinctive: line 82
No 3D effects, no chartjunk.
- Distinctive: line 88
Run the checks in the Integrity gate section below.
- Distinctive: line 98
Before returning the design:
- Distinctive: line 108
If any [inspection] check fails, mark the design as "needs user attention".
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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-drafterorphd-tech-paper-templatefirst 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.pyscript. - 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:
- [inspection] Paradigm matches figure type (motivated example is not a pipeline; overview is not a bar chart).
- [inspection] Layout sketch is concrete enough that the user could draw from it.
- [inspection] Labels are real entity names, not placeholders.
- [inspection] Tool suggestion matches the figure's complexity (not Matplotlib for a multi-icon motivated example, not PowerPoint for a 20-method bar chart).
- [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.
- [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.
- [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
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