How do I install the Ieee Figure skill?

Run npx skillmds@latest add tenwalk/ieee-figure in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ieee Figure safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ieee Figure?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ieee Figure free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ieee Figure?

TenWalk (@tenwalk) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ieee Figure by tenwalk — Agent Skill

Name: Ieee Figure
Author: TenWalk

Ieee Figure

Produce publication-grade result figures for IEEE communications papers (JSAC, TWC, TC/TCOM, WCL, CL) — correct column widths, embedded fonts, vector output (PDF/EPS), grayscale- and colorblind-safe palettes, readable fonts, and the comms-standard plot types: BER/outage vs SNR on a log y-axis, achievable/sum rate vs SNR or antennas, convergence curves, CDFs, the analysis-line + simulation-marker overlay, learning curves (NMSE vs SNR, training/validation loss), and ISAC plots (rate–CRB tradeoff, beampattern, ROC). Use whenever the user wants to make or fix a figure for a comms paper: "make a result plot", "BER curve", "误码率曲线", "中断概率图", "和速率曲线", "收敛曲线", "仿真图", "NMSE曲线", "训练损失曲线", "CRB折中图", "波束方向图", "IEEE figure", "single/double column figure", "图太小看不清". For deciding which metrics to plot, use ieee-experiments; for captions-as-argument, see ieee-writing.

TenWalk 3fb2268 5 files · 21.4 KB Updated Sep 17, 2026

File contents

IEEE Publication Figures

Use this skill to generate or repair figures that will survive IEEE production: vector, correctly sized for the column, with embedded fonts and readable text at print size.

Core stance

Size to the column from the start. Single-column ≈ 3.5 in (88.9 mm) wide; double-column ≈ 7.16 in (181.6 mm). Design at final size so fonts end up readable — never shrink a big figure.
Vector first. Output PDF or EPS for line/vector art; embed fonts. Use high-res raster only for photographic content (≥ 300 dpi color/grayscale, ≥ 600 dpi for line art / combinations).
Readable and grayscale-safe. Minimum ~8 pt text at final size; distinguish series by marker/linestyle/hatch as well as colour so the figure survives B/W printing; use a colorblind-safe palette.
Comms axis conventions. Error/outage curves use a log y-axis (semilogy); keep the SNR range wide enough to show the high-SNR slope (diversity order); define the SNR axis (transmit vs receive). Plot a derived expression as a line and its Monte-Carlo check as markers.
One panel, one question. No two panels answer the same thing. The figure must be legible and self-explanatory with its caption.
No fabricated data. Plot only the user's real numbers; if data is missing, ask or stub with a clearly labelled placeholder.

When to open extra files

File	Open when
references/ieee-figure-spec.md	Sizing, resolution, fonts, colour/grayscale, file format, multi-panel layout, caption rules, submission checklist
references/matplotlib-patterns.md	Concrete matplotlib setup (rcParams for embedded fonts, sizing, palettes) and ready patterns for BER/outage-vs-SNR (semilog), rate vs SNR/antennas, analysis-vs-simulation overlay, convergence, CDF, NMSE-vs-SNR, training/validation loss, and ISAC plots (rate–CRB tradeoff, beampattern, ROC)

Workflow

Target column: ask single- or double-column; set the width accordingly.
Pick the chart type that matches the result (see matplotlib-patterns.md): BER/outage vs SNR (semilog y) for reliability, line vs SNR/antennas for rate/efficiency, analysis-line + simulation-marker overlay to validate a derivation, objective-vs-iteration for convergence, CDF for distributions.
Set the rendering rcParams first (embedded fonts, sans-serif, sizes) — before any plotting.
Plot the real data; encode each scheme redundantly (colour + marker/linestyle). Use semilogy for error/outage; floor the y-axis at the lowest reliably simulated value.
Label fully: axis titles with units (dB, bps/Hz), defined SNR axis, legend; text ≥ 8 pt.
Export .pdf (or .eps) as primary and a 300–600 dpi .png preview.
Grayscale check: convert to gray and confirm series are still distinguishable.
Return the script, the output paths, and a one-line caption draft.

Output format

Script: a self-contained, runnable plotting script (matplotlib) using the user's data.
Outputs: the vector file path + a PNG preview path.
Caption: a one-sentence draft stating what the figure shows (the question it answers).
Checks: confirmation of size, font embedding, and grayscale legibility.

---name: ieee-figuredescription: Produce publication-grade result figures for IEEE communications papers (JSAC, TWC, TC/TCOM, WCL, CL) — correct column widths, embedded fonts, vector output (PDF/EPS), grayscale- and colorblind-safe palettes, readable fonts, and the comms-standard plot types: BER/outage vs SNR on a log y-axis, achievable/sum rate vs SNR or antennas, convergence curves, CDFs, the analysis-line + simulation-marker overlay, learning curves (NMSE vs SNR, training/validation loss), and ISAC plots (rate–CRB tradeoff, beampattern, ROC). Use whenever the user wants to make or fix a figure for a comms paper: "make a result plot", "BER curve", "误码率曲线", "中断概率图", "和速率曲线", "收敛曲线", "仿真图", "NMSE曲线", "训练损失曲线", "CRB折中图", "波束方向图", "IEEE figure", "single/double column figure", "图太小看不清". For deciding which metrics to plot, use ieee-experiments; for captions-as-argument, see ieee-writing.---# IEEE Publication FiguresUse this skill to generate or repair figures that will survive IEEE production: vector, correctlysized for the column, with embedded fonts and readable text at print size.## Core stance- **Size to the column from the start.** Single-column ≈ 3.5 in (88.9 mm) wide; double-column ≈ 7.16 in (181.6 mm). Design at final size so fonts end up readable — never shrink a big figure.- **Vector first.** Output PDF or EPS for line/vector art; embed fonts. Use high-res raster only for photographic content (≥ 300 dpi color/grayscale, ≥ 600 dpi for line art / combinations).- **Readable and grayscale-safe.** Minimum ~8 pt text at final size; distinguish series by marker/linestyle/hatch as well as colour so the figure survives B/W printing; use a colorblind-safe palette.- **Comms axis conventions.** Error/outage curves use a log y-axis (`semilogy`); keep the SNR range wide enough to show the high-SNR slope (diversity order); define the SNR axis (transmit vs receive). Plot a derived expression as a line and its Monte-Carlo check as markers.- **One panel, one question.** No two panels answer the same thing. The figure must be legible and self-explanatory with its caption.- **No fabricated data.** Plot only the user's real numbers; if data is missing, ask or stub with a clearly labelled placeholder.## When to open extra files| File | Open when ||---|---|| [references/ieee-figure-spec.md](references/ieee-figure-spec.md) | Sizing, resolution, fonts, colour/grayscale, file format, multi-panel layout, caption rules, submission checklist || [references/matplotlib-patterns.md](references/matplotlib-patterns.md) | Concrete matplotlib setup (rcParams for embedded fonts, sizing, palettes) and ready patterns for BER/outage-vs-SNR (semilog), rate vs SNR/antennas, analysis-vs-simulation overlay, convergence, CDF, NMSE-vs-SNR, training/validation loss, and ISAC plots (rate–CRB tradeoff, beampattern, ROC) |## Workflow1. **Target column:** ask single- or double-column; set the width accordingly.2. **Pick the chart type** that matches the result (see matplotlib-patterns.md): BER/outage vs SNR (semilog y) for reliability, line vs SNR/antennas for rate/efficiency, analysis-line + simulation-marker overlay to validate a derivation, objective-vs-iteration for convergence, CDF for distributions.3. **Set the rendering rcParams first** (embedded fonts, sans-serif, sizes) — before any plotting.4. **Plot the real data;** encode each scheme redundantly (colour + marker/linestyle). Use `semilogy` for error/outage; floor the y-axis at the lowest reliably simulated value.5. **Label fully:** axis titles with units (dB, bps/Hz), defined SNR axis, legend; text ≥ 8 pt.6. **Export** `.pdf` (or `.eps`) as primary and a 300–600 dpi `.png` preview.7. **Grayscale check:** convert to gray and confirm series are still distinguishable.8. **Return** the script, the output paths, and a one-line caption draft.## Output format1. `Script:` a self-contained, runnable plotting script (matplotlib) using the user's data.2. `Outputs:` the vector file path + a PNG preview path.3. `Caption:` a one-sentence draft stating what the figure shows (the question it answers).4. `Checks:` confirmation of size, font embedding, and grayscale legibility.

Frequently asked questions