visualization-check（可视化检查）

Visualization Check

Iterative review cycle using a context-free subagent as a naive reader. The subagent sees ONLY the image — no study context, no axis explanations, no expected conclusions. If the subagent can't understand the figure, a real reader won't either.

When to Use

• After generating a figure (R, Python, or any tool)
• Before submitting figures for publication
• When a plot feels unclear but you can't pinpoint why
• When you want objective "fresh eyes" feedback

Workflow

digraph viz_check {
  rankdir=TB;
  node [shape=box];

  start [label="User provides image path\n(and optionally the generating script)" shape=doublecircle];
  preprocess [label="Downscale to preview\n(sips → /tmp)"];
  launch [label="Dispatch review subagent\n(read-only, context-free)"];
  feedback [label="Subagent returns structured report:\ncomprehension / issues / rating"];
  decide [label="Clarity >= 4 AND\nmain message correct?" shape=diamond];
  analyze [label="Map feedback to\nactionable script changes"];
  fix [label="Edit generating script\n+ apply viz best practices"];
  render [label="Re-render the figure"];
  done [label="Figure passes naive reader test\n— DONE" shape=doublecircle];

  start -> preprocess;
  preprocess -> launch;
  launch -> feedback;
  feedback -> decide;
  decide -> done [label="yes"];
  decide -> analyze [label="no"];
  analyze -> fix;
  fix -> render;
  render -> preprocess [label="downscale new\nrender + dispatch\nfresh subagent"];
}

Step 0: Preprocess Image for Subagent

High-DPI publication figures (300 DPI, 2000-4500px wide) consume excessive context tokens and can cause subagents to hit context limits. Always downscale before dispatching.

Preprocessing Command

Use sips (built into macOS) to create a preview copy:

sips --resampleWidth 800 "{ORIGINAL_PATH}" --out "/tmp/viz_check_preview.png"

Rules

• Always downscale before dispatching, regardless of file size. This is cheap and prevents failures.
• Preview goes to /tmp/ — never modify the original figure.
• Use the preview path (/tmp/viz_check_preview.png) in the subagent prompt, not the original.
• Re-preprocess after each re-render — the original changes, so the preview must be regenerated.
• For multi-figure parallel mode, use unique filenames: /tmp/viz_check_preview_{basename}.png

Multi-Figure Preprocessing

When reviewing multiple figures in parallel, batch-preprocess first:

for fig in path/to/figures/*.png; do
  basename=$(basename "$fig")
  sips --resampleWidth 800 "$fig" --out "/tmp/viz_check_preview_${basename}"
done

Then dispatch each subagent with its corresponding /tmp/viz_check_preview_{basename}.png.

Step 1: Dispatch Naive Reader Subagent

You MUST dispatch a review subagent for each review round. Do not attempt to self-review — the whole point is context-free eyes.

Subagent Configuration

Subagent Prompt Template

You are a naive reader seeing this figure for the first time. You have NO context about
the study, data, or purpose. Read the image file, then produce a structured review.

## Image
Read the image at: {PREVIEW_PATH}

## Structured Review Template

### A. Comprehension (what you understood)
1. What is this figure showing? Describe in your own words.
2. What are the axes/labels? Summarize each.
3. What is the main message or takeaway?

### B. Confusion Points (what is unclear)
List EVERY element that is unclear, ambiguous, or hard to read. Be specific —
reference position (top-left, bottom panel, etc.) and quote any problematic text.

### C. Visual Quality Checklist
For each item, mark PASS / FAIL / N/A:
- [ ] Colors distinguishable (also in grayscale?)
- [ ] All text readable at this size (no overlapping labels?)
- [ ] Reference lines / thresholds labeled
- [ ] No unnecessary chart junk (gridlines, borders, decorations)
- [ ] Legend (if any) self-explanatory without context
- [ ] Good data-ink ratio (data vs non-data elements)
- [ ] Abbreviations spelled out or self-evident

### D. Clarity Rating
Rate 1 (incomprehensible) to 5 (instantly clear). Justify in one sentence.

### E. Top 3 Recommended Fixes
Prioritized list of the most impactful improvements.

Context Contamination Rules

Do NOT include in the subagent prompt:

• Study description or purpose
• What the axes represent
• Expected conclusions or trends
• Variable names or domain terminology explanations
• Prior feedback from previous iterations
• The generating script or its path

Even saying "this is a survival curve" gives away too much. Let the subagent figure it out.

Step 2: Analyze Feedback

Map the subagent's confusion points to specific script changes:

Step 3: Apply Data Visualization Best Practices

Hierarchy of Fixes (Most Impact First)

1. Labels and titles: Self-explanatory axis labels WITH units. No abbreviations.
2. Reference lines: Label directly on plot (not just in legend).
3. Color: Colorblind-safe, distinguishable in grayscale. Use scale_color_viridis_d() or Okabe-Ito.
4. Simplify: Remove redundant gridlines, borders, backgrounds. theme_minimal() or theme_classic().
5. Text size: All text readable at final print size (typically 8-10pt minimum).
6. Data-ink ratio: Maximize data, minimize decoration.
7. Direct labeling: Label lines/points directly instead of using legends when possible.

R-Specific Patterns

# Good defaults for publication figures
theme_publication <- theme_minimal(base_size = 14) +
  theme(
    axis.title = element_text(size = 14, face = "bold"),
    axis.text = element_text(size = 12),
    legend.position = "bottom",
    panel.grid.minor = element_blank(),
    plot.title = element_text(size = 16, face = "bold")
  )

# Colorblind-safe palettes
scale_color_viridis_d()           # Sequential
scale_color_brewer(palette = "Set2")  # Categorical

Step 4: Re-render and Iterate

1. Edit the generating script with fixes (fix the SOURCE, not the PNG)
2. Re-run the script to produce updated figure
3. Re-preprocess: sips --resampleWidth 800 the new render to /tmp/ (the old preview is stale)
4. Dispatch a NEW subagent (fresh context, same template) pointing at the new preview
5. Exit condition: Subagent rates clarity >= 4/5 AND identifies the main message correctly

Typical iterations: 2-3 rounds.

Multi-Figure Mode

When checking multiple independent figures, dispatch review subagents in parallel — one per figure. Each subagent touches only its own image (no shared state).

digraph multi_fig {
  rankdir=LR;
  node [shape=box];
  dispatch [label="Dispatch N subagents\nin parallel"];
  fig1 [label="Subagent 1\nreviews fig1.png"];
  fig2 [label="Subagent 2\nreviews fig2.png"];
  fig3 [label="Subagent 3\nreviews fig3.png"];
  collect [label="Collect all reports\nand fix sequentially"];

  dispatch -> fig1;
  dispatch -> fig2;
  dispatch -> fig3;
  fig1 -> collect;
  fig2 -> collect;
  fig3 -> collect;
}

For figures that share visual consistency requirements (same color scheme, axis style), do a final synthesis step after individual reviews to check cross-figure consistency.

Common Pitfalls

• Leaking context to subagent: The #1 failure mode. Review your prompt before dispatching.
• Reusing the same subagent: Prior feedback contaminates. Always dispatch fresh.
• Over-iterating: If the subagent gets the main message right but nitpicks aesthetics, you're done.
• Ignoring the generating script: Fix the SOURCE (R/Python script), not the PNG. Ensures reproducibility.
• Using general-purpose subagent: Use Explore (read-only) to prevent the reviewer from accidentally modifying files.

Quick Start

/visualization-check path/to/figure.png

If you also know the generating script:

/visualization-check path/to/figure.png --script path/to/plot_script.R

The agent will:

1. Find the generating script (if not provided, search for it)
2. Downscale to /tmp/ preview (sips --resampleWidth 800)
3. Dispatch naive reader subagent with preview path (read-only, context-free)
4. Collect structured feedback report
5. Edit the script and re-render
6. Re-preprocess + dispatch new subagent for re-review
7. Repeat until clarity >= 4/5