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/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

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ieee-skills
511 skills
Install
$ npx -y skills add TenWalk/ieee-skills --skill ieee-figure --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/ieee-figure

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

SKILL.md

ieee-figure.SKILL.md
name: ieee-figure
description: >-
  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 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](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) |

Workflow

1. **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 format

1. `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.

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Ships withieee-skills

This repository contains two platform-adapted packages for the same IEEE communications manuscript skills.

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Repo: TenWalk/ieee-skills

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