fig

name	fig
description	Create and manage publication figures: framework diagrams via draw.io/Figma MCP, data plots via code, and figure assembly
argument-hint	[framework|plot|assemble|list]

Figure Management

Create, iterate, and organize publication-quality figures for papers.

Sub-commands

$ARGUMENTS[0] determines the action. If omitted, show help.

---

/swf:fig framework <description>

Create a framework/architecture diagram (typically Fig.1 of a paper).

1. Choose tool based on need:

   • draw.io MCP — default choice. Best for flowcharts, pipelines, architecture diagrams. Claude has full control: create, edit, export.
   • Figma MCP — use when the user asks, or when working with existing Figma designs. Best for polished visual design and FigJam collaboration.

2. Ask the user: "用 draw.io 还是 Figma 画？"

   • If user has no preference, default to draw.io (Claude has more control)

3. draw.io workflow:

   • Call start_session to open browser preview
   • Ask the user to describe what the diagram should show
   • Call create_new_diagram with well-structured XML:
     • Use large shapes (width >= 200, height >= 60)
     • Center layout on canvas
     • Use thick lines (strokeWidth >= 2)
     • Use a consistent color palette
     • Add proper spacing (150-200px between shapes)
   • Iterate: user says what to change → call edit_diagram
   • When satisfied, call export_diagram to save:
     • .drawio → for further editing in draw.io desktop
     • .svg → for paper inclusion (vector, scalable)
     • .png → for quick preview
   • Save to docs/paper/figures/

4. Figma workflow:

   • Use generate_diagram to create in FigJam
   • Or use create_new_file + use_figma for Figma design files
   • Use get_screenshot to preview
   • Export from Figma UI

5. Record in experiment log:

   • Note which figure this is for (e.g., Fig.1 — Method Overview)
   • Record the source file path

---

/swf:fig plot <description>

Generate a data visualization figure from experiment results.

1. Read experiment data:

   • Check results/ for metrics files
   • Check docs/experiments/ for relevant experiment records
   • Identify what data to plot

2. Generate matplotlib/seaborn code:

   • Create a Python script in src/plots/ or scripts/
   • Follow publication conventions:
     • Font size >= 12pt for labels
     • Clear axis labels with units
     • Legend if multiple series
     • Color-blind friendly palette
     • High DPI (300+)
   • Save figure to docs/paper/figures/

3. Run and iterate:

   • Execute the script
   • Show the user the result
   • Adjust based on feedback
   • Commit the plotting script (reproducible)

---

/swf:fig assemble

Plan figure assembly, create tasks in Linear for manual assembly, and generate legends.

1. Read paper outline from docs/paper/outline.md

2. List all available sub-figures:

   • Framework diagrams in docs/paper/figures/
   • Data plots in docs/paper/figures/
   • Experiment result figures from docs/experiments/

3. Generate assembly plan:

   ## Figure Assembly Plan
   
   ### Fig.1 — Method Overview (框架图)
   - Source: docs/paper/figures/framework.svg
   - Status: Done
   - Tool: draw.io
   
   ### Fig.2 — Benchmark Comparison (数据图)
   - Panel a: docs/paper/figures/benchmark-accuracy.pdf
   - Panel b: docs/paper/figures/benchmark-speed.pdf
   - Status: Panel a done, Panel b missing
   - Tool: matplotlib
   
   ### Fig.3 — Case Study Results
   - Panel a-c: results/case-001/figures/
   - Status: Not started
   

4. Identify gaps:

   • Missing sub-figures → suggest /swf:fig plot or /swf:fig framework
   • Missing legends → will generate in step 6

5. Create Linear issues for manual assembly work (user confirms before creating):

   • Each Figure that has all sub-figures ready → one issue:

     Title: 拼装 Fig.2 — Benchmark Comparison
     Labels: figures, manual
     Milestone: Phase 4
     Description:
       子图文件：
       - Panel a: results/figures/benchmark-accuracy.pdf
       - Panel b: results/figures/benchmark-speed.pdf
       拼装要求：
       - 加 a/b panel 标签（大写，粗体，左上角）
       - 统一字体和字号
       - 对齐面板边距
       - 导出为 docs/paper/figures/fig2-benchmark.pdf (300 dpi+)
     

   • Figures with missing sub-figures → skip, note in plan

6. Generate legends for all figures with sub-figures ready:

   • Call the same logic as /swf:fig legend
   • Save to docs/paper/figure-legends.md

---

/swf:fig list

List all figures in the project with their status.

1. Scan docs/paper/figures/ and experiment result directories
2. Cross-reference with paper outline to map to Fig.1, Fig.2, etc.
3. Show status table:

   | Figure | Description | Source | Format | Status |
   |--------|-------------|--------|--------|--------|
   | Fig.1  | Framework   | draw.io | .svg  | Done   |
   | Fig.2a | Benchmark   | matplotlib | .pdf | Done |
   | Fig.2b | Performance | — | — | Missing |
   

/swf:fig legend [fig-number]

Write figure legends for publication.

1. If fig-number given (e.g., fig1), write legend for that figure only. If omitted, write legends for all figures.

2. Gather information for each figure:

   • Read the figure file (get dimensions, panels)
   • Read related experiment records for methods/data details
   • Read paper outline for context on what the figure demonstrates

3. Generate legend following journal conventions (Nature-style):

   **Figure 1 | Method overview.**
   **a**, Data preprocessing pipeline. Raw expression matrices are filtered
   for quality control (Methods) and normalized using ... **b**, Model
   architecture. The encoder takes ... and produces ... **c**, Training
   procedure. The model is trained on ... with ... loss function.
   Data shown are from Dataset A (n = 5,000 cells).
   

   Rules:

   • Title sentence: bold, describes the WHOLE figure, ends with period
   • Panel descriptions: bold letter, comma, then description
   • Must define ALL error bars (e.g., "error bars represent s.d.")
   • Must state ALL n values (e.g., "n = 3 biological replicates")
   • Must define ALL statistical tests (e.g., "two-tailed Student's t-test")
   • Total length < 250 words per figure
   • Describe what is SHOWN, not the result or conclusion

4. Save to docs/paper/figure-legends.md

5. Validate against checklist:

   • Every panel mentioned?
   • All error bars defined?
   • All n values stated?
   • All statistical tests described?
   • Under 250 words?
   • Describes depiction, not interpretation?

---

Color Palettes for Publication

When generating figures, use these color-blind friendly palettes:

Qualitative (categorical data):

#4477AA, #EE6677, #228833, #CCBB44, #66CCEE, #AA3377, #BBBBBB

Sequential (continuous data): Use matplotlib's viridis, plasma, or cividis colormaps.

Notes

• Framework diagrams: iterate with Claude via MCP, then optionally refine in draw.io desktop or Illustrator
• Data plots: always generate via code (reproducible), never draw manually
• All plotting scripts should be committed to git
• Final figures for submission should be in docs/paper/figures/ with clear naming: fig1-framework.svg, fig2a-benchmark.pdf, etc.