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/woodfishhhh/ez_math_model/plotting-agentnpx skills add woodfishhhh/EZ_math_model --skill plotting-agentgit clone --depth 1 https://github.com/woodfishhhh/EZ_math_modelWhat 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 | $0.00102 | $0.01961 |
| Opus 5 | $0.00051 | $0.00981 |
| Sonnet 5 | $0.00020 | $0.00392 |
| Haiku 4.5 | $0.00010 | $0.00196 |
Grade A, and why
plotting-agent scanned grade A with 0 findings 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 3d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
97% identical to plotting-agent — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plotting Agent (Step 2)
Faithful implementation of the Plotting Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 2 and App. F.1 p.45).
Cost: ~20–30 LLM calls. The paper uses PaperBanana (Zhu et al., 2026) as the default backbone with a closed-loop VLM-critique refinement. This skill expresses that loop in host-agent terms: you (the host agent) generate matplotlib code with your own LLM, render via your Bash/Python tool, optionally critique the rendered PNG with your vision model, redraw, and finally caption.
Inputs
workspace/outline.json— specifically theplotting_planarrayworkspace/inputs/idea.mdandworkspace/inputs/experimental_log.md— the source dataworkspace/inputs/figures/— optional pre-existing figures (PlotOnmode)
Outputs
workspace/figures/<figure_id>.png— one PNG perplotting_planentry (300 DPI, sized to the requested aspect ratio)workspace/figures/captions.json—{figure_id: caption_text}map
Workflow
Per figure (executed independently per figure_id)
-
Read the figure spec from
outline.json:{ "figure_id": "fig_main_results", "title": "Main Results on Dataset X", "plot_type": "plot", "data_source": "experimental_log.md", "objective": "Visual summary (Grouped Bar Chart) demonstrating ...", "aspect_ratio": "5:4" } -
Few-shot retrieval (visual planning): pick the matching pattern from
references/chart-patterns.md(forplot_type=="plot") orreferences/diagram-patterns.md(forplot_type=="diagram"). -
Extract data: parse
idea.mdand/orexperimental_log.md(data_sourcefield tells you which) to obtain the numeric values or conceptual entities the figure needs. Forexperimental_log.md, the## 2. Raw Numeric Datasection contains markdown tables. -
Render:
If
PAPERBANANA_PATHis set — use the PaperBanana backbone (Zhu et al., 2026). It runs a Retriever → Planner → Stylist → Visualizer → Critic loop and is especially good forplot_type == "diagram". Seereferences/paperbanana-cookbook.mdfor setup (needs a Gemini API key).
What ships with it
9 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.
- references/aspect-ratios.md 2.0 KB
- references/caption-prompt.md 2.7 KB
- references/chart-patterns.md 6.6 KB
- references/diagram-patterns.md 3.9 KB
- references/paperbanana-cookbook.md 6.0 KB
- references/plotting-pipeline.md 5.8 KB
- scripts/paperbanana_render.py 13 KB runs code
- scripts/render_diagram.py 4.1 KB runs code
- scripts/render_matplotlib.py 7.1 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.
- 3d ago First seen · 168 lines · 102 tokens per session scan A 57bd1e7e18ab
plotting-agent is a skill published in the GitHub repository woodfishhhh/EZ_math_model (39 stars, last pushed 1mo ago), licensed MIT. It adds 102 tokens to every session and 1,961 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to plotting-agent, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
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mathmodel-skill
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math-modeling-paper
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interpret-modeling-problems
根据原始赛题和附件,生成可回查证据的国赛及类似数学建模赛题解读,建立小问输入输出、约束与歧义、附件审计、模型蓝图、验证方案和论文交付接口。适用于选题比较、正式建模前的完整解读和已有解读复核;不用于在缺少原题时臆测题意,也不把候选模型伪装成已经验证的数值求解。.
math-modeling-finalizer
数学建模项目收口与终审 Skill。用于正式结果已基本冻结后的参赛者尺度代码简化与行为回归、最终完整复审、AI 工具使用详情 DOCX、Word/PDF 排版检查、支撑材料 ZIP 与提交前卫生检查。典型请求包括“结果已经定了,把代码改得更像学生项目”“最后完整审一遍”“检查最终 Word/PDF”“生成 AI 使用详情和支撑材料”。若需要重新建模、修改 evaluator 或正式结果,路由回 math-modeling-solver;正文、摘要、引用语义或公式写作问题路由回 math-modeling-paper;提交后的项目复盘/教学交给 math-modeling-growth。.
math-modeling-growth
把真实项目的阶段结果、决策、证据、失败记录和当前进度,转化为通俗、循序渐进、可执行的学习解释与赛后成长报告。用户要求复盘项目、理解自己学到了什么、解释某次决策、生成赛后报告、从项目进度推进学习,或需要按接受度逐步教学时使用;优先读取 Solver、Paper、Finalizer 等上游阶段产物和当前冻结结果。.