# Scientific Visualization > Publication-ready data plots for CS papers (systems, ML, networking, security). Use when creating figures with multi-panel layouts, error bars, colorblind-safe palettes, log-scale axes, latency CDFs, throughput curves, training curves, ablation bars, or speedup comparisons for IEEE / ACM / USENIX / NeurIPS / ICML / ICLR submissions. Orchestrates matplotlib, seaborn, and plotly with venue-specific styling and PDF output that drops cleanly into LaTeX. For one-off exploration use seaborn or plotly directly; for system architecture diagrams use tikz-figures or scientific-schematics instead. - Skill: `sipengxie2024/scientific-visualization` (Agent Skill, multi-file: 16 files) - Install (CLI): `npx skillmds@latest add sipengxie2024/scientific-visualization` - Raw SKILL.md: https://api.skillmd.com/api/skills/sipengxie2024/scientific-visualization/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Security - License: MIT license - Author: sipengxie2024 (https://skillmd.com/u/sipengxie2024) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/sipengxie2024/scientific-visualization --- # Scientific Visualization (CS Edition) ## Overview Turn benchmark and experiment data into publication-quality figures for CS venues. This skill is opinionated for systems, ML, networking, and security papers: it covers latency CDFs, throughput curves, training-loss plots, ablation bars, speedup comparisons, and Pareto fronts. It replaces the upstream biology-leaning version (microscopy, gene expression, fluorophores) with CS conventions and CS venue specs (IEEE, ACM, USENIX, NeurIPS, ICML, ICLR, arXiv). The plugin produces LaTeX-only manuscripts. **PDF is the default figure format** — it embeds losslessly into LaTeX via `\includegraphics{...}`, scales without artifacts, and matches every CS venue's preferred line-art format. Use PNG only for raster screenshots, profilers, or visualizations that already started as raster. ## Scope Use this skill when: - Creating plots for a CS paper (latency, throughput, accuracy, loss, memory, F1, CDF, etc.) - Targeting IEEE Transactions, an ACM conference (`acmart`), a USENIX venue, NeurIPS / ICML / ICLR, or arXiv - Building multi-panel figures with consistent styling - Ensuring figures stay readable in grayscale and for colorblind reviewers - Exporting at the right resolution and format for LaTeX inclusion Do not use this skill for: - Architecture / data-flow / pipeline schematics — use `superpower-writing:tikz-figures` (vector) or `superpower-writing:scientific-schematics` (image-model PNG) - Ad-hoc exploratory plots in a notebook — just import seaborn / plotly directly - Tables — use LaTeX `booktabs` directly; no Python needed ## Quick Start ### Single-column IEEE figure (most common case) ```python import matplotlib.pyplot as plt import numpy as np # Apply IEEE-tuned publication style (sets fonts, sizes, colorblind palette) plt.rcParams.update({ 'figure.dpi': 100, 'savefig.dpi': 600, 'font.family': 'sans-serif', 'font.sans-serif': ['Arial', 'Helvetica'], 'font.size': 8, 'axes.labelsize': 9, 'axes.titlesize': 9, 'xtick.labelsize': 7, 'ytick.labelsize': 7, 'legend.fontsize': 7, 'axes.linewidth': 0.5, 'lines.linewidth': 1.5, 'axes.spines.top': False, 'axes.spines.right': False, 'savefig.format': 'pdf', 'savefig.bbox': 'tight', }) plt.rcParams['axes.prop_cycle'] = plt.cycler(color=[ '#0072B2', '#D55E00', '#009E73', '#CC79A7', '#E69F00', '#56B4E9' ]) # Okabe-Ito subset, colorblind-safe # IEEE single-column = 3.5 inches fig, ax = plt.subplots(figsize=(3.5, 2.4)) x = np.array([1, 2, 4, 8, 16, 32]) baseline_p99 = np.array([12.4, 13.1, 14.8, 18.6, 27.3, 49.5]) proposed_p99 = np.array([10.2, 10.4, 11.1, 12.7, 16.4, 24.0]) ax.plot(x, baseline_p99, marker='o', label='Baseline') ax.plot(x, proposed_p99, marker='s', label='Ours') ax.set_xlabel('Concurrent clients') ax.set_ylabel(r'p99 latency (ms)') ax.set_xscale('log', base=2) ax.legend(frameon=False, loc='upper left') fig.savefig('latency_p99.pdf', bbox_inches='tight') ``` The output drops into LaTeX with `\includegraphics[width=\columnwidth]{figures/latency_p99.pdf}`. ### Using a bundled style file The plugin ships matplotlib style files under `${CLAUDE_PLUGIN_ROOT}/skills/scientific-visualization/assets/`. Load one with the absolute path: ```python import os, matplotlib.pyplot as plt PLUGIN = os.environ['CLAUDE_PLUGIN_ROOT'] plt.style.use(f'{PLUGIN}/skills/scientific-visualization/assets/ieee.mplstyle') # or 'acm.mplstyle', 'neurips.mplstyle', 'publication.mplstyle', 'presentation.mplstyle' ``` To make figures self-contained (no env-var dependency), copy the chosen style file into `.writing/figures/` once and reference it locally. ## Core Principles ### 1. Format and resolution - **Vector PDF** for every plot. Embeds in LaTeX without rasterization, scales perfectly. Use `fig.savefig('foo.pdf', bbox_inches='tight')`. - **PNG at 300 – 600 DPI** only for raster content (screenshots, profilers, heatmaps with thousands of cells where a vector explodes). Set `savefig.dpi: 600` for PNGs going into camera-ready. - **Never JPEG** for plots. The lossy artifacts around lines and text are obvious in print. - **Embed fonts** in PDFs — matplotlib does this by default with `pdf.fonttype: 42` (TrueType). Without it, IEEE / ACM sometimes flag text as "Type 3 fonts" during eXpress / paperplaza checks. ```python plt.rcParams['pdf.fonttype'] = 42 # TrueType, embedded plt.rcParams['ps.fonttype'] = 42 ``` ### 2. Color: colorblind-safe by default Roughly 8 % of male readers have a red-green color-vision deficiency. Reviewers, area chairs, and ML conference attendees in dim halls fail to distinguish poorly chosen palettes. Default to **Okabe-Ito** for categorical data and **viridis** / **cividis** for continuous data. ```python # Okabe-Ito (8 distinguishable categories under all common CVD types) okabe_ito = ['#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7', '#000000'] plt.rcParams['axes.prop_cycle'] = plt.cycler(color=okabe_ito) ``` For categorical 3 – 4-series plots, prefer the high-contrast subset `['#0072B2', '#D55E00', '#009E73', '#CC79A7']` (blue / vermillion / green / pink) — it stays distinguishable in B/W print. For heatmaps: - Sequential single-variable data (latency, accuracy, count): `viridis` or `cividis`. Both are perceptually uniform and colorblind-safe. - Diverging data (speedup vs baseline, log-fold change, error signed): `RdBu_r` or `PuOr` centered at the neutral value. Avoid `RdGn` / `RdYlGn`. - **Never** `jet` or `rainbow`. Not perceptually uniform; over-emphasizes mid-range; bad in B/W. Reviewers will flag this. Always add **redundant encoding** beyond color: line styles (`'-'`, `'--'`, `':'`), markers (`'o'`, `'s'`, `'^'`), or hatching on bars. If the figure still reads in grayscale, the color choice is solid. ### 3. Typography - Sans-serif at the final printed size: Arial, Helvetica, or DejaVu Sans (the matplotlib default fallback). - Minimums **at the size that lands on the page**: - Axis labels: 8 pt - Tick labels: 7 pt - Legend: 7 pt - Panel labels (a, b, c): 9 – 10 pt bold - Embed fonts. Use `pdf.fonttype: 42` so TrueType fonts ship inside the PDF. - Sentence case: `Throughput (req/s)` not `THROUGHPUT (REQ/S)`. - Always include units in parentheses. Use SI / IEC prefixes consistently (`KB`, `MiB`, `μs`, not `K`, `M`, `us`). ### 4. Figure dimensions for CS venues | Venue | Single column | Double column / full | Typical class file | |-------|---------------|-----------------------|--------------------| | IEEE Transactions / Conf. | 3.5 in (89 mm) | 7.16 in (182 mm) | `IEEEtran` | | ACM (sigconf, acmsmall) | 3.33 in (8.45 cm) | 7.0 in (17.78 cm) | `acmart` | | USENIX (Annual / OSDI / NSDI / Sec.) | 3.33 in | 7.0 in | `usenix2019_v3` | | NeurIPS / ICML / ICLR | n/a (one column) | 5.5 in (14.0 cm) | venue style | | arXiv | follow target venue | follow target venue | venue style | **Match `\columnwidth` exactly** when you use `\includegraphics[width=\columnwidth]`. Set `figsize=(3.5, ...)` for IEEE single-column figures and let LaTeX scale only when the figure spans both columns. See `references/venue_requirements.md` for the full per-venue spec, including `\figcomp` / `\columnsep` quirks and ACM's two-class width difference. ### 5. Multi-panel layout - Label panels with bold lowercase letters: **(a)**, **(b)**, **(c)** — IEEE and ACM both use lowercase parenthesized. - Use `GridSpec` for non-uniform layouts; `plt.subplots()` for uniform grids. - Align panels along edges; equalize axis ranges across panels comparing the same quantity. - Use `constrained_layout=True` (replaces `tight_layout`) — handles colorbars and panel labels better. ```python from string import ascii_lowercase fig = plt.figure(figsize=(7.0, 4.0), constrained_layout=True) # ACM full width gs = fig.add_gridspec(2, 3) axes = [fig.add_subplot(gs[i // 3, i % 3]) for i in range(6)] for i, ax in enumerate(axes): ax.text(-0.20, 1.05, f'({ascii_lowercase[i]})', transform=ax.transAxes, fontsize=10, fontweight='bold', va='top') ``` #### Multi-panel anti-redundancy checklist A multi-panel figure should read as one figure where each panel earns its space. Covering any one panel must leave a gap the others cannot fill. Run this before finalizing a composite: - [ ] Every panel answers a **distinct question**. Write the one-line question each panel answers; if two lines are the same, one panel is redundant. - [ ] No panel **re-displays a subset of another panel's data** in a different visual form. A bar of per-method accuracy plus a heatmap of the same accuracies is the same data twice. - [ ] No panel is a **slice of another**. A "method X only" bar that is just one group pulled out of the grouped-bar panel adds nothing; replace it with a relationship view (scatter, trade-off). - [ ] Each panel has its **own axis vocabulary** (different x/y quantities), not the same axes recomputed. Common redundancy traps and the fix: | Trap | CS example | Fix | |------|-----------|-----| | Absolute + absolute | Grouped-bar accuracy + heatmap of the same accuracies | Replace the heatmap with a deviation view (per-method delta vs baseline) | | Subset of parent | "Ours-only" latency bar that is one column of the grouped bar | Swap for a scatter: latency vs throughput across methods | | Two rankings | Two ranked bars on accuracy and on F1 (highly correlated) | Replace one with an accuracy-vs-F1 scatter | | Same slice, different chart | Pie plus stacked bar of one breakdown | Merge, or replace one with a trade-off plot | When a panel shows absolute values, a good companion panel shows **what is atypical** (a signed deviation from a baseline or mean, on a diverging colormap like `RdBu_r`) rather than the same absolutes again. A third panel that reveals **co-variation** between two of the quantities (a scatter or bubble plot, with reference lines at the medians) adds a dimension the first two lack. #### Three generalizable multi-panel techniques These transfer to almost any CS composite: - **Luminance-based in-bar text color.** When you print values inside bars, pick black or white text by the bar's luminance so the number stays readable on both light and dark fills: ```python for bar, value in zip(bars, values): r, g, b = bar.get_facecolor()[:3] luminance = 0.299 * r + 0.587 * g + 0.114 * b # 0..1 ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), f'{value:.2f}', ha='center', va='bottom', color='white' if luminance < 0.5 else 'black') ``` - **Dynamic y-limit tightening.** Never leave `0–100` when the values sit in a narrow band (e.g. accuracies of 80–95). A fixed wide range flattens the differences the figure exists to show. Tighten to the data with a small margin: ```python lo, hi = data.min(), data.max() margin = 0.05 * (hi - lo) ax.set_ylim(lo - margin, hi + margin) ``` For a bar chart this trades off against the "bars must start at zero" rule (see Common pitfalls): if you tighten a bar-chart axis, add a broken-axis indicator so the truncation is honest. - **Dedicated legend-only subplot.** For a multi-axis figure where one shared legend serves all panels, reserve the last cell for the legend alone instead of crowding one panel or repeating legends: ```python ax_legend = fig.add_subplot(gs[-1]) ax_legend.legend(handles, labels, loc='center', frameon=False) ax_legend.set_axis_off() ``` Prefer one shared legend strip (or direct labels on stable regions) over repeating a legend inside every axis. ## Common CS Figure Patterns Reference implementations live in `references/matplotlib_examples.md`. To choose among these and the standard ML charts by the question the figure answers, with scale remedies and venue cautions, read `references/chart-chooser.md`. The patterns covered: | Pattern | Use case | Notes | |---------|----------|-------| | Latency CDF / ECDF | Tail behavior of a request distribution | Log-scale x-axis; cap at p99 or p99.9; include median + tail callouts | | Throughput vs concurrency | Scaling curves | Log-scale x; mark saturation knee; pair with median latency | | Speedup bar chart | Per-benchmark improvement vs baseline | Always include `1.0 ×` reference line; group by suite | | Training / loss curves | ML convergence | Show mean over seeds + shaded std; log-scale y for loss | | Pareto front (acc vs params / FLOPs) | Model-size tradeoff | Highlight your model; annotate frontier | | Stacked bar (latency breakdown) | Where time goes | Stable color order across runs | | Roofline plot | Compute vs memory bound | Log-log axes; ridge line at peak FLOPS | | Heatmap (cache miss / attention) | 2D intensity | viridis or cividis; never jet | | Box / violin (per-seed variability) | Multi-run distribution | Overlay individual seeds as strip plot | | Significance bars | A / B / ablation comparison | Optional in CS — only when stats are central to the claim | ### Statistical rigor (CS-specific) CS reviewers care less about p-values than biomedicine reviewers, but more about **run-to-run variability** and **error bars over seeds**. Standard expectations: - Run each measurement ≥ 3 times (≥ 5 for ML, ≥ 10 for noisy systems benchmarks). Report mean and std or 95 % bootstrap CI. - Show individual data points (strip plot) when n is small (≤ 10) — averages alone hide bimodal failure modes. - State error type in the caption: `Error bars: 95 % CI over 5 seeds` or `Shaded region: ±1 std over 10 runs`. - For ML, also report median + min/max if you train fewer than 5 seeds. - For latency, report a percentile (median, p99, p99.9) — never just the mean. The mean of a heavy-tail distribution is a lie. Don't fabricate error bars. If a measurement is from a single run, say so in the caption and prefer a bar / scatter without error indicators rather than zero-width error bars. ## Choosing a library | Library | Best for | Notes | |---------|----------|-------| | matplotlib | Multi-panel, full control, anything that needs custom annotations | Default for camera-ready CS figures | | seaborn | Statistical plots with automatic CI / bootstrap | Built on matplotlib; styles compose; great for box / violin / regression | | plotly | Interactive HTML for project pages or supplementary | Use `kaleido` to export static PDF | | pgfplots / TikZ | When you want LaTeX to compile the figure | Best for diagrams that mix math and data; covered by `tikz-figures` | If a figure is reused across paper / poster / slides, render at multiple sizes from the same script — keep a single source-of-truth Python file under `.writing/figures/src/`. ## Workflow inside this plugin **Confirmation gates — pause for the user before these three points:** 1. **Before writing a generator script**, confirm the target venue, the column width (single vs full), and the data source path. If any is unknown, ask rather than assume a default — guessing the venue silently produces a figure sized for the wrong template. 2. **Never invent measurements.** If `.writing/figures/data/.{csv,jsonl}` is missing or the user hasn't supplied numbers, stop and ask for the data. Do not synthesize plausible-looking benchmark values, error bars, or trend lines to fill a plot — a polished figure built on fabricated data is worse than no figure. Placeholder values are acceptable only when the user explicitly asks for a layout mock-up and the figure is labeled as such. 3. **Before overwriting an existing `.writing/figures/.pdf`**, confirm with the user (or note that you are about to replace it) so a prior camera-ready figure isn't clobbered silently. Then proceed: 1. **Plan from the outline.** Each figure listed in `.writing/plan.md` should have a one-line claim it supports (`fig:cdf-latency` → "our system has a 2.4× lower p99 than the baseline at 32 clients"). For a figure that doubles as the paper's results-storytelling figure (the headline result a reviewer sees first), check `tikz-figures/references/figure-rhetoric.md`: it covers the three-figure storytelling model, the Figure-1 performance-teaser paradigm and its avoid-condition (do not teaser a marginal gain, it shows the weakness unkindly), and the 30-second comprehension test. That reference is venue-agnostic across our figure skills. When these plots form the full figure deck of a paper, run one **editor pass** over the whole deck before submission: the per-deck narrative review in `figure-rhetoric.md` §5 (hook verdict, figure arc, misplaced panels, missing panels, kill list), which is distinct from the per-figure defect hunt in the Final Checklist below. When one figure is itself multi-panel (an ablation grid, a schematic paired with its result), `figure-rhetoric.md` §6 covers outlining the panels from the claim, drawing each with the matching skill, and reviewing the composed image as one artifact with `verify_layout.py`. Then pick the chart from `references/chart-chooser.md` by the question the figure answers, apply its scale remedy and orientation rules, and record the chart as the brief's `family`. 2. **Write a generator script** in `.writing/figures/src/.py`. The script reads from `.writing/figures/data/.{csv,jsonl}` and writes `.writing/figures/.pdf`. This separation lets reviewers (and you) regenerate figures from raw data. 3. **Apply this skill's style** at the top of the script — copy the bundled `.mplstyle` file or use the inline rcParams snippet from Quick Start. 4. **Verify before commit.** Open the PDF in a viewer at 100 % zoom and at the column width it will appear at in the paper. If text is cramped at column width, the font sizes are wrong. For a mechanical pre-check, call `verify(fig)` from `scripts/verify_layout.py` at the end of the generator script: it flags text that collides with other text or a foreign axes spine and text clipped off the canvas, and, with `crop_dir=`, writes one PNG per panel so you (or a review agent) run the perceptual pass the geometry check cannot. Open each crop with the Read tool and check contrast, leader crossings, color-identity confusion, and legend binding. 5. **Caption discipline.** The figure caption belongs in the `.tex` file, not on the figure itself. Repeat axes labels in the caption; spell out what error bars represent; state the sample size. ## Common pitfalls (CS-specific) 1. **Y-axis in milliseconds for tail latency.** A linear axis hides the long tail. Use log y for p99 / p99.9 plots. 2. **Bar chart starting non-zero.** Misleading. Either start at zero or annotate clearly with a broken-axis indicator. 3. **One color per algorithm but identical line style.** Fails in grayscale. Add markers + line styles. 4. **Legend over data.** Use `loc='upper left'` and verify visually; or place outside with `bbox_to_anchor`. 5. **Default matplotlib font rendering.** Matplotlib's default `mathtext` uses computer-modern italics; mixed with Arial body text it looks broken. Either set `text.usetex: True` (slow) or use `mathtext.fontset: 'cm'` only when you need real LaTeX math. 6. **`tight_layout()` clipping panel labels.** Use `constrained_layout=True` instead. 7. **DPI confusion.** For PDF, `dpi` only affects rasterized embedded objects. For PNG, set both `figure.dpi` (display) and `savefig.dpi` (output) — `savefig.dpi: 600` for camera-ready. 8. **`plt.show()` in a script that runs in CI.** Will hang. Use `fig.savefig(...)` only and skip `plt.show()` in scripted figure generation. 9. **Forgetting `bbox_inches='tight'`.** Default save leaves margin around the figure. With `bbox_inches='tight'`, the saved PDF crops to the actual content — what you want for `\includegraphics`. 10. **Reusing colors across figures.** Pin one color to one method everywhere. Define a `METHOD_COLORS` dict in a shared `.writing/figures/src/_palette.py`. ## Bundled resources | Path | Purpose | |------|---------| | `references/publication_guidelines.md` | Universal best practices: typography, layout, axes, accessibility, grayscale tests | | `references/venue_requirements.md` | Per-venue specs (IEEE, ACM, USENIX, NeurIPS, ICML, ICLR, arXiv) with exact widths, font sizes, and class-file gotchas | | `references/color_palettes.md` | Categorical (Okabe-Ito, Wong, Tol Bright/Muted), sequential (viridis family), diverging (RdBu, PuOr, BrBG); with grayscale and colorblind testing recipes | | `references/chart-chooser.md` | Question-to-chart table (systems patterns plus standard ML charts), scale remedies (broken axis / log / normalized), orientation and series-count rules, and the charts reviewers object to with their replacements | | `references/matplotlib_examples.md` | Twelve runnable patterns — CDF, throughput, training curve, Pareto, ablation, stacked-bar breakdown, heatmap, roofline, box+strip, multi-panel composition, `\columnwidth` IEEE template, `\textwidth` ACM template | | `scripts/style_presets.py` | `apply_publication_style(name)`, `set_color_palette(name)`, `configure_for_venue('ieee' / 'acm' / 'usenix' / 'neurips' / 'icml' / 'iclr' / 'arxiv')`. Copy into a project to pin the rcParams. | | `scripts/figure_export.py` | `save_publication_figure()` (multi-format), `save_for_venue()` (per-venue defaults), `check_figure_size()` (warns when figsize doesn't match the venue's column width). | | `scripts/verify_layout.py` | `verify(fig)`: geometric self-check (text/text + text/spine overlap, off-canvas text) plus per-panel crops for the perceptual pass. Call at the end of a generator script before committing the PDF. | | `assets/color_palettes.py` | Importable color constants and `apply_palette('okabe_ito' / 'wong' / 'tol_bright' / …)`. | | `assets/publication.mplstyle` | Generic CS publication baseline. Use when no specific venue style applies. | | `assets/ieee.mplstyle` | IEEE Transactions / conferences (3.5 in single column, 8 pt body). | | `assets/acm.mplstyle` | ACM `acmart` sigconf / acmsmall (3.33 in single, 7.0 in two-column). | | `assets/neurips.mplstyle` | NeurIPS / ICML / ICLR (single column 5.5 in textwidth, 9 pt body). | | `assets/presentation.mplstyle` | Larger fonts and thicker lines for posters and slides. | ## Final Checklist **Frame this as defect-hunting, not confirmation.** Before adding `\includegraphics{...}`, open the rendered PDF at the column width it will print at and actively look for what is *wrong*, rather than ticking boxes to confirm the figure is fine. Walk the items below, write a short defect list, and tag each finding `high` / `medium` / `low`. Fix every `high` and every `medium` you can fix cheaply; list `low` items but do not block on them. Findings are advisory: surface them to the user and let the user decide, never silently redraw a camera-ready figure or overwrite it. `high`: misleads the reader or makes the figure look broken: - Non-PDF vector output where vector was expected, or a PNG below 300 DPI going to camera-ready. - A truncated / non-zero-baseline axis that exaggerates the gain with no broken-axis indicator. - Fabricated or unsupported data: invented error bars, a synthesized trend, zero-width error bars standing in for "single run". - Colorblind-unsafe palette or color-only encoding (jet / rainbow, red-green pairs); figure unreadable in grayscale. - Key labels or tick labels illegible at print size, or text clipped. - A `figsize` that does not match the target column width, so LaTeX rescales and distorts fonts. - An excluded or failed run (crashed, diverged, OOM'd, cold-start outlier) silently folded into a mean, bar, or error band, or silently dropped with no caption note. Excluded data is omitted or drawn with a distinct marker, never averaged in with the runs you kept. - The same quantity carrying two canonical values across figures, caption, and abstract (2.4× in one place, 2.6× in another for "the speedup"). One number per claim, everywhere. See `references/publication_guidelines.md` → Data Fidelity and Self-Consistency for both of these. `medium`: reduces professionalism or comprehension: - Chartjunk: 3D effects, decorative gridlines, drop shadows, top / right spines left on. - Missing units on axis labels, or inconsistent SI / IEC prefixes. - Panel labels missing or inconsistent (`(a) (b) (c)`, lowercase parenthesized). - Method colors not pinned consistently across every figure in the paper. - Redundant panels in a composite: re-run the multi-panel anti-redundancy checklist in §5. - Error-bar definition missing from the caption (or single-run not noted). `low`: cosmetic, does not affect comprehension: - Minor alignment or spacing imperfections. - Palette or typography refinements. - Non-embedded fonts where the venue tolerates it (still prefer `pdffonts foo.pdf` showing "yes" under "emb"). This severity framing aligns with `tikz-figures` mode-C, which already runs the strongest form of defect-hunting (independent multi-lens adversarial review). For data plots there is no independent reviewer pass; the discipline is the same, but the final oracle is the user looking at the rendered PDF.