Draw Image Diagrams

What it does

When your agent uses it

• Drawing an algorithm or code flow diagram for a report
• Sketching a system architecture figure for a paper
• Making a conceptual illustration that is not tied to real data
• Checking whether image generation credentials are available before a pipeline run

Example prompts

• “Draw a flowchart of the simulated annealing procedure for the method section of our paper.”
• “Generate an architecture sketch showing data flowing from the ingest service to the model server.”
• “Check the image generation credentials and tell me which path is available.”

Requirements

• An OPENAI_API_KEY with OpenAI organization verification, or OpenAI Codex with a ChatGPT subscription
• Python with the openai package, version 1.0 or newer

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1访问 platform.openai.com/api-keys（需科学上网）
2. 2注册/登录 OpenAI 账号（需邮箱 + 手机号验证）
3. 3点击 "Create new secret key" → 复制 sk-proj-...
4. 4去 Billing 充值（最低 $5，2025 年起无免费额度）

What it can do on your machine

Read from SKILL.md and the folder at commit 90c4727. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • python
  • pip

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • platform.openai.com

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • OPENAI_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Draw Image Diagrams loads about 1.9k tokens when it runs. Until then it costs about 135 tokens; SKILL.md has 354 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~135

When it runs · the whole SKILL.md, loaded when a task matches

~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:58

  > ⚠️ **切勿把 API Key 写入代码文件或 git commit**，`.env` 已在 `.gitignore` 中排除。

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from RealSeaberry/AutoMCM-Pro at commit 90c4727, republished under its MIT licence (© RealSeaberry). 354 words, ~1,929 tokens.

name

draw-image

description

Generate diagrams, flowcharts, and conceptual illustrations using OpenAI gpt-image-2 (default) or gpt-image-1. Use for: algorithm/code flow diagrams, system architecture sketches, conceptual illustrations, and any figure that does NOT represent actual code execution output (data plots, model results, etc. must still be generated by running Python code). Requires OPENAI_API_KEY and OpenAI organization verification when used from Claude Code / API path. OpenAI Codex subscribers can use built-in $imagegen without an API key.

# 临时（当前终端有效）
export OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxx

# 永久（写入 shell 配置）
echo 'export OPENAI_API_KEY=sk-proj-xxxxxxxxxxxxxxxx' >> ~/.bashrc
source ~/.bashrc

# 验证
echo $OPENAI_API_KEY | head -c 15

pip install "openai>=1.0"
python -c "import openai; print(openai.__version__)"

# Codex CLI 中自然语言触发
Generate a technical flowchart showing the AutoMCM pipeline steps

# 显式触发（更可靠）
$imagegen Clean flowchart: 建模流程 from 读取题目 to 生成PDF, white background

export OPENAI_API_KEY=sk-proj-...   # Codex CLI 检测到此变量后切换为 API 定价

python scripts/draw_image.py --check

# 示例：优雅跳过
python scripts/draw_image.py --check || {
  if [ $? -eq 2 ]; then
    echo "[draw_image] 跳过 — 无认证，继续流水线"
  fi
}

需要一张图？
├─ 内容来自代码运行数值（散点图、折线图、热力图、拟合曲线…）
│   └─ ✗ 不用本 skill → 必须用 matplotlib/seaborn 生成
└─ 非数值内容（流程图、架构图、概念示意）
    ├─ 极简几何图（≤3个框） → tikz 即可
    └─ 复杂流程图 / 概念插图
        ├─ --check 返回 api_key → python scripts/draw_image.py --prompt ...
        ├─ --check 返回 codex_oauth → $imagegen ... (Codex CLI)
        └─ --check 退出码 2 → 跳过，\missingfigure{} 占位

python scripts/draw_image.py \
  --prompt "Clean technical flowchart on white background, Chinese labels.
Steps: (1) 读取赛题 → (2) 识别问题类型 → (3) 文献调研 → (4) 建立数学模型
→ (5) 编写求解代码 → (6) 运行验证脚本 → Decision: 全部通过?
→ Yes: 写入LaTeX / No: 修复代码 回到(5).
Style: professional diagram, blue/gray color scheme, sans-serif font,
clear arrows, minimal decoration." \
  --output "CUMCM_Workspace/latex/images/fig00_pipeline.png" \
  --size 1024x1536 \
  --quality high

python scripts/draw_image.py \
  --prompt "System architecture diagram on white background.
Left: User inputs problem PDF. Center: AutoMCM-Pro Agent with three modules:
[Problem Analysis] [Model & Verify] [LaTeX Writing]. Right: Output PDF paper.
Arrows show data flow. Style: clean tech diagram, English labels, gray boxes." \
  --output "CUMCM_Workspace/latex/images/fig_architecture.png" \
  --size 2048x1152 \
  --quality high

python scripts/draw_image.py \
  --prompt "Simple flowchart: A → B → C → D" \
  --output "draft_check.png" \
  --model gpt-image-1-mini \
  --quality low

python scripts/draw_image.py \
  --prompt "..." \
  --output "fig.webp" \
  --output-format webp \
  --compression 20 \
  --quality high

"Clean technical flowchart on white background. [中/英] labels.
Steps: (1) [step1] → (2) [step2] → ...
Decision nodes: [condition] → Yes: [action_yes] / No: [action_no]
Style: professional, minimal, blue/gray color scheme,
sans-serif font, clear directional arrows."

"System architecture diagram, white background.
Components: [component list with roles].
Connections: [arrows describing data/control flow].
Style: clean labeled boxes, professional tech diagram, no decorative elements."

"Scientific illustration: [scenario].
Show [element1] as [visual], [element2] as [visual].
Style: technical/scientific, clean white background, labeled key elements,
suitable for academic paper, no heavy text."

\begin{figure}[htbp]
  \centering
  \includegraphics[width=0.85\textwidth]{images/fig00_pipeline.png}
  \caption{建模流程图}
  \label{fig:pipeline}
\end{figure}

© RealSeaberry, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/draw-image of RealSeaberry/AutoMCM-Pro.

Open the folder on GitHubat commit 90c4727