跳到正文
cccskills
免费在 GitHub 公开

math-figure-generator

Generate and render-verify publication-quality mathematical-modeling figures from saved evidence, using the approved figure plan, source data, claim, type, and consistent visual system.

中文简介正在准备中。当前显示原文说明。

查看安装方法

包含的文件(5)

  • SKILL.md2.3 KB
  • references/chart-patterns.md17.0 KB
  • references/color-systems.md4.7 KB
  • references/layout-guide.md7.0 KB
  • references/render-check.md730 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Preconditions

  • Figure type, source artifacts, and target claim are known.
  • Type 3 claim is human-confirmed.
  • Submission figures use final/frozen evidence.

References

Load only what the requested chart needs:

  • references/chart-patterns.md
  • references/color-systems.md
  • references/layout-guide.md
  • references/render-check.md

Workflow

  1. Verify source files and the exact variables/units to plot.
  2. Choose the smallest chart form that communicates the claim.
  3. Generate with deterministic code, preferably matplotlib.
  4. Save editable source code and the requested output format.
  5. Apply the shared color, typography, sizing, and labeling conventions.
  6. Render the final output and inspect it visually.
  7. Check clipping, overlap, illegible text, misleading axes, legends, empty panels, and source/claim mismatch.
  8. Iterate until render checks pass.

Output Locations

  • Type 1/2 exploration: results/Qx/experiments/roundN/figures/
  • Type 3/4 submission: paper/figures/

Use stable descriptive filenames. Do not copy Type 1 diagnostics into the paper directory.

Figure Requirements

  • Labels include units where applicable.
  • Captions state what is shown and the evidence-backed takeaway without overstating causality.
  • Baseline is visually distinct but not exaggerated.
  • Uncertainty is shown when it is part of the claim.
  • Type 3 raster output is at least 300 dpi; vector output is preferred when compatible.
  • Accessibility and grayscale differentiation are considered.

Rules

  • Do not fabricate or manually alter plotted values.
  • Do not use a chart type that hides concentration, uncertainty, or negative results.
  • Do not truncate axes misleadingly.
  • Do not create decorative 3D effects.
  • Do not treat code execution as render verification.
  • Keep diagnostic and paper roles separate.

Verification

  • Source, claim, type, and target section agree.
  • Render inspection passed.
  • Text is readable at final paper size.
  • Legends, colors, markers, axes, units, and captions are consistent.
  • Final output path exists and is recorded in the figure plan.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.

中文简介正在准备中。当前显示原文说明。

zhnnky329/MathModeling-skills10602026年9月24日 更新

Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.

中文简介正在准备中。当前显示原文说明。

zhnnky329/MathModeling-skills10602026年9月24日 更新

Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.

中文简介正在准备中。当前显示原文说明。

zhnnky329/MathModeling-skills10602026年9月24日 更新

Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.

中文简介正在准备中。当前显示原文说明。

zhnnky329/MathModeling-skills10602026年9月24日 更新

Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.

中文简介正在准备中。当前显示原文说明。

zhnnky329/MathModeling-skills10602026年9月24日 更新

Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.

中文简介正在准备中。当前显示原文说明。

zhnnky329/MathModeling-skills10602026年9月24日 更新

查看 zhnnky329 的全部技能

报告此技能的问题