Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/huangwb8/skills/auto-draw-plotnpx skills add huangwb8/skills --skill auto-draw-plotgit clone --depth 1 https://github.com/huangwb8/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/huangwb8/skills/auto-draw-plot)<a href="https://agentmods.dev/skills/huangwb8/skills/auto-draw-plot"><img src="https://agentmods.dev/badge/skills/huangwb8/skills/auto-draw-plot.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00087 | $0.04790 |
| Opus 5 | $0.00044 | $0.02395 |
| Sonnet 5 | $0.00017 | $0.00958 |
| Haiku 4.5 | $0.00009 | $0.00479 |
Grade B, and why
auto-draw-plot scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Reads agent configuration directoriesmediumAgent snooping
.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.
2. 默认优先读取本地 Codex 配置:从 `~/.codex/config.toml` 获取 BenszAPI base URL,从 `~/.codex/auth.json` 获取 `OPENAI_API_KEY | OPENAI_API`,再使用 `gpt-image-2`;环境变量与 `remote.env` 只作为缺失字段的兜底来源。Windows 同时兼容 `%USERPROFILE%`、`%HOMEDRIVE%%HOMEP How it starts
The opening of the file, as written. The whole thing — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Auto Draw Plot
目标
根据用户描述生成高质量绘图 prompt,并按通用、roadmap、schematic 模式通过 BenszAPI 直接完成 gpt-image-2 或 Nano Banana/Gemini 出图、编辑和多轮迭代;这是自包含的图片生成工作流,选中后不得调用或依赖 imagegen,除非用户明确要求同时使用 imagegen。
流程
输入
定位
- 以用户需求为起点,由宿主 AI 进行语义规划,再构造适用于当前图片 provider 的 prompt;脚本默认不调用额外 Gemini 文本接口。
- 默认模式是
general;用户明确要技术路线图/roadmap/flowchart 时使用roadmap,明确要原理图/机制图/架构图时使用schematic。后续新增类型应作为config.yaml:modes.presets扩展,不改主流程。 - 默认通过
scripts/run_draw_plot.py在独立隐藏工作区里完成“parallel-vibe 规划留痕 → prompt → 出图 → 视觉评估 → 继续/停止”的闭环;parallel-vibe是必选工作流的一部分,不是可选增强。 - 默认工作区是当前目录下的
.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/auto-draw-plot/{yyyy-mm-dd-hh-mm}/;所有中间文件必须留在隐藏目录里。宿主 AI 在正式检查 API、初始化工作区或开始出图前,必须先向用户明确声明本次任务.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/auto-draw-plot根目录的绝对路径,方便用户实时监督。轻量测试目录固定为./tests/draw-plot。
输入
user_need(必需):自然语言描述的图像需求、输出用途、必要的视觉语义与格式要求。mode(可选):general/roadmap/schematic;默认general。模式只改变 prompt preset、默认画布和评估口径,不引入 legacy draw.io 渲染器。api_config(可选):指向~/.bensz-skills/config/remote.env的路径;默认auto只在运行前按优先级选择连接与鉴权检查通过的 provider,真实生成资格以 Images submit 响应为准。image_provider(可选):用户明确指定的图片模型/provider,如gpt-image-2或nano_banana。显式指定后必须只用该 provider,失败时暂停并报告原因,不得切换到其他模型。allow_provider_fallback(可选):只有用户明确说“失败可以换模型/可以回退到另一个 provider”时才为 true;该授权仅覆盖已确认未创建任务的 provider 故障,不覆盖订阅、余额、权限、overage、计费服务错误或 submit 空/非 JSON 等任务创建状态不确定的协议错误。max_rounds(可选):最大优化轮数,默认 3;若用户另有指定,以用户为准。visual_constraints(可选):比例、期望布局、色调、字体等硬约束。尺寸只作为 provider 原生尺寸选择参考,不承诺最终导出像素。quality/provider_size/output_format/output_compression(可选):gpt-image-2显式 provider 参数;默认分别为low、1024x1024、jpeg、85,均执行白名单或范围校验。reference_images(可选):用于 prompt 引导的风格/布局图;第 2 轮起上一轮output.jpg会自动作为第一参考图,用户参考图排在其后。workspace_base(可选):用户显式指定的隐藏工作区根目录;未指定时使用当前目录.bensz-api/task-{yyyymmdd-hhmm}-{简短描述}/auto-draw-plot/。
执行步骤
技能边界
auto-draw-plot独立负责需求拆解、prompt 生成、图片 generation/edit、多轮保真微调、评估与交付;图片请求由本 skill 的脚本通过 BenszAPI 提交,不调用也不依赖imagegenskill。- 用户要求用
auto-draw-plot画图、改图或基于上一轮继续微调时,只使用本 skill 完成完整链路。不得把它表述或编排为“先生成 prompt,再交给imagegen出图/编辑”的前处理步骤;这种说法会错误暗示依赖关系,并可能造成重复生图与重复计费。 - 只有用户明确点名同时使用
imagegen,或明确要求其独有能力时,才允许额外调用imagegen;调用前说明两者各自独立的职责,且不得将imagegen伪装成auto-draw-plot的内部依赖。 - 默认用户可见说明应写明:
本次使用 auto-draw-plot,由其经 BenszAPI 完成 prompt、出图和后续编辑。不得默认承诺后续会调用imagegen。
What ships with it
17 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- CHANGELOG.md 9.4 KB
- config.yaml 9.1 KB
- README.md 16 KB
- references/parallel-plan.md 1.5 KB
- references/prompt-guidelines.md 3.3 KB
- scripts/build_parallel_plan.py 2.8 KB runs code
- scripts/common.py 4.5 KB runs code
- scripts/env_utils.py 3.0 KB runs code
- scripts/evaluate_image.py 9.6 KB runs code
- scripts/generate_image.py 9.7 KB runs code
- scripts/image_provider_client.py 78 KB runs code
- scripts/init_workspace.py 7.3 KB runs code
- scripts/modes.py 3.9 KB runs code
- scripts/nano_banana_check.py 1.0 KB runs code
- scripts/nano_banana_client.py 17 KB runs code
- scripts/parallel_round_worker.py 2.3 KB runs code
- scripts/run_draw_plot.py 25 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed · +30 lines 094267b79bd5
- 6d ago First seen · 120 lines · 87 tokens per session scan B 8f6feef871fe
auto-draw-plot is a skill published in the GitHub repository huangwb8/skills (48 stars, last pushed today), licensed MIT. It adds 87 tokens to every session and 4,790 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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