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 skills add woodfishhhh/EZ_math_model --skill plotting-agentgit clone --depth 1 https://github.com/woodfishhhh/EZ_math_modelWrote 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/woodfishhhh/ez_math_model/plotting-agent)<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/plotting-agent"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/plotting-agent/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/plotting-agent"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/plotting-agent.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00102 | $0.01961 |
| Opus 5.5 | $0.00041 | $0.00784 |
| Sonnet 5.5 | $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 1mo 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
100% identical to plotting-agent — 0 lines 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.
- 1mo 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 (43 stars, last pushed 2mo ago), licensed MIT. It adds 102 tokens to every session and 1,961 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to plotting-agent, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
mathmodel-skill
An end-to-end workflow for mathematical-modeling competitions, including CUMCM, MCM/ICM, and the Electrical Cup. It guides teams from choosing a problem through modeling, solving, checking, writing, rule compliance, and final review.
math-modeling-solver
A Chinese-language guide for solving mathematical modeling competition problems. It covers China’s CUMCM and America’s MCM/ICM, where teams use mathematics and code to answer real-world problem statements.
math-modeling-paper
A Chinese-language guide for writing papers for mathematical modelling competitions, where teams use mathematics and data to study a real-world problem.
interpret-modeling-problems
A method for turning a mathematical modelling contest problem and its attachments into a checked plan for solving and documenting it.
mcm-model-typeset
A set of writing and formatting rules for the model-building and solution section of a Chinese mathematical modelling paper. It defines section structure, equation explanations, constraints, and model summaries without changing the supplied numbers or formulas.
math-modeling-finalizer
A finalization guide for mathematical-modeling projects after the main results are largely fixed.