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Elsevier Figure Style

Community
guhou-hvi
elsevier-figure-style

Generate, revise, and audit source-backed quantitative manuscript figures, and visually audit exported schematic/conceptual figures and graphical abstracts without redrawing them in v0.1. Use for Elsevier-style manuscript figure generation, matplotlib or ggplot2 result plots, line charts, heatmaps, bar charts, scatter or Pareto plots, artwork export checks, pre-submission figure QA, mechanism/workflow diagram audits, graphical-abstract audits, or equivalent Chinese-language requests for paper figures and submission checks.

Overview

Publisherguhou-hvi
Repositoryelsevier-figure-style
Skill nameelsevier-figure-style
Stars
89
Forks
1
Bundled files
21
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.

  • 21 bundled files

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

  • Open source

    Published by guhou-hvi on GitHub. Read the source before you install it.

Installation

Install the Elsevier Figure Style 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/guhou-hvi/elsevier-figure-style/tree/main/skills/elsevier-figure-style
  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/guhou-hvi/elsevier-figure-style.git /tmp/elsevier-figure-style
mkdir -p .claude/skills
cp -r /tmp/elsevier-figure-style/skills/elsevier-figure-style .claude/skills/elsevier-figure-style
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Elsevier Figure Style 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 Elsevier Figure Style 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 Elsevier Figure Style 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.

Elsevier Figure Style

Purpose

Apply this unofficial, source-backed workflow while generating quantitative result figures and while auditing manuscript figure exports. Verify the target journal's current Guide for Authors before submission. Never claim that this skill guarantees acceptance.

Use two tracks:

  • result: generate, revise, and audit line, heatmap, bar/barh, scatter, Pareto/frontier, ablation, and metric figures.
  • schematic: visually audit principle, mechanism, workflow, conceptual, and graphical-abstract exports. In v0.1, do not redraw or edit these figures unless the user separately requests an editing workflow.

Match the report language to the user's language. Keep command names, field names, and rule IDs unchanged.

Select the Profile

Use assets/elsevier_figure_style.json by default. It is the single manifest entry point for identity, resources, profiles, style values, export thresholds, and supported detectors.

Before first use of the bundled Python scripts, run python scripts/check_environment.py. The skills CLI copies Skill files but does not install Python packages. If dependencies are missing, ask before running the install command reported by the checker; do not modify the user's Python environment silently. R workflows require ggplot2 and jsonlite.

  • official: A-level official publisher and target-journal rules. Tool-input and style-integration findings use STYLE-* IDs and must not be described as official policy.
  • editor: official plus authorized redacted revision evidence and core D-level visual QA. Use by default.
  • strict: editor plus public cases and the complete visual QA checklist.

When the user supplies another manifest, pass it through --config or config_path. A new journal may reuse the built-in detectors without code changes; a genuinely new detector type still requires code.

Treat a manifest and its referenced files as one profile bundle. bundle_root defines the bundle boundary; schema and resource paths must be relative and remain inside it. Never weaken this boundary for an untrusted profile.

Establish the Figure Contract

Record:

  • Claim: the one-sentence conclusion the figure supports.
  • Track: result, schematic, or graphical-abstract.
  • Family: line, heatmap, bar, scatter, mixed, mechanism, workflow, graphical abstract, or R/ggplot2.
  • Profile: official, editor, or strict.
  • Target layout: minimal, single, one-half, double, or graphical abstract when known.
  • Artwork type: line, combination, or halftone.
  • Export target and target-journal overrides.

Generate Result Figures

For Python/matplotlib, load the selected profile and use the helper functions:

python
from elsevier_plot_style import apply_journal_style, figure_size, line_style_kwargs, style_axis, finalize_figure, save_figure

spec = apply_journal_style(config_path="path/to/profile.json")
fig, ax = plt.subplots(figsize=figure_size(spec=spec))
ax.plot(x, y, label="Model", **line_style_kwargs("model", spec=spec))
style_axis(ax, spec)
finalize_figure(fig, spec)
save_figure(fig, "figure_1", spec=spec, formats=("pdf", "tiff"), artwork_type="line")

For reproducible project-local imports, initialize a vendored module:

bash
python scripts/init_figure_style_project.py --target <project> --config assets/elsevier_figure_style.json

Then import helpers from figure_style in the target project.

For R/ggplot2, source scripts/elsevier_theme.R, then use theme_journal(), journal_palette(), and save_journal(). The compatibility aliases theme_elsevier(), elsevier_palette(), and save_elsevier() remain available. R has no static checker in v0.1.

Revise Result Figures

  1. Load references/python-workflow.md or references/r-workflow.md.
  2. Replace ad hoc fonts, colors, widths, markers, DPI, and export options with the selected profile.
  3. Keep semantic overrides only when they encode meaning, such as thresholds, baselines, intervention windows, highlighted frontier points, or discrete classes.
  4. Mark an intentional static-checker override on the same or preceding line:
python
# figure-style: allow STYLE-01 - benchmark threshold must remain red
ax.axhline(limit, color="red")
  1. Run the Python checker and inspect every remaining warning.

Audit Figures

Load references/submission-checklist.md. When an export is available, also load references/visual-audit-workflow.md.

Run the Python source checker:

bash
python scripts/check_elsevier_figure_style.py --path <script-or-dir> --config assets/elsevier_figure_style.json --profile editor

Run bitmap metadata checks with an explicit artwork type when known:

bash
python scripts/check_exported_figure_metadata.py --path <figure-or-dir> --config assets/elsevier_figure_style.json --figure-type result --artwork-type combination --target-layout single --profile editor

Use --format json for machine-readable findings and --fail-on warn for a stricter CI gate. Both checkers fail by default on FAIL findings or invalid inputs. The source checker also returns an input error when Python files are present but no supported matplotlib figure calls are detected; do not interpret that case as a style PASS.

For visual QA:

  1. Actually open or render the final export at the intended display size.
  2. Do not report a visual PASS for any figure that was not viewed.
  3. For PDF/SVG/EPS, render a preview when the environment cannot inspect the format directly; do not alter the original.
  4. For result figures with code and exports, report source findings, metadata findings, then visual findings.
  5. Report FAIL, then WARN, then notable PASS items with location, rule ID, and a concrete fix.

Resources

  • assets/elsevier_figure_style.json: default versioned manifest.
  • assets/journal_figure_profile.schema.json: manifest schema.
  • scripts/elsevier_plot_style.py: Python profile loader and plotting helpers.
  • scripts/elsevier_theme.R: R profile loader and ggplot2 helpers.
  • scripts/check_elsevier_figure_style.py: profile-aware Python static checker.
  • scripts/check_exported_figure_metadata.py: bitmap and export metadata checker.
  • scripts/init_figure_style_project.py: project-local helper/profile initializer.
  • scripts/check_environment.py: Python version and runtime-dependency preflight.
  • requirements.txt: runtime dependencies copied with the Skill.
  • references/submission-checklist.md: profile-aware submission checklist.
  • references/visual-audit-workflow.md: exported-figure visual QA workflow.
  • references/source-basis/rule-registry.md: rule IDs, evidence levels, and implementation coverage.

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 Elsevier Figure Style AI skill do?

Generate, revise, and audit source-backed quantitative manuscript figures, and visually audit exported schematic/conceptual figures and graphical abstracts without redrawing them in v0.1. Use for Elsevier-style manuscript figure generation, matplotlib or ggplot2 result plots, line charts, heatmaps, bar charts, scatter or Pareto plots, artwork export checks, pre-submission figure QA, mechanism/workflow diagram audits, graphical-abstract audits, or equivalent Chinese-language requests for paper figures and submission checks.

Why use Elsevier Figure Style on TypingMind?

Because you install it once and use it with any model. Elsevier Figure Style 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 Elsevier Figure Style in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/guhou-hvi/elsevier-figure-style/tree/main/skills/elsevier-figure-style. 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 Elsevier Figure Style?

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 Elsevier Figure Style?

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

Is the Elsevier Figure Style AI skill free?

Yes. It is published on GitHub by guhou-hvi 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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