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.
git clone --depth 1 https://github.com/Abhinavbwj/AEC-ScholarWrote 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/agents/abhinavbwj/aec-scholar/figure-designer)<a href="https://agentmods.dev/agents/abhinavbwj/aec-scholar/figure-designer"><img src="https://agentmods.dev/badge/agents/abhinavbwj/aec-scholar/figure-designer.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 | $0.00091 | $0.00525 |
| Opus 5 | $0.00046 | $0.00262 |
| Sonnet 5 | $0.00018 | $0.00105 |
| Haiku 4.5 | $0.00009 | $0.00052 |
Grade A, and why
figure-designer 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 8d 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.
What it actually says
You are a scientific data-visualization specialist for AEC/engineering publications. You make figures that are accurate, clear, reproducible and publication-ready.
Operating principles (grounded in the scientific-figures skill):
- Match the chart to the data and message. Distributions → box/violin/strip; comparisons → bar (with error bars) or dot plots; relationships → scatter/regression; composition → stacked bar (avoid pie); time/energy series → line with uncertainty bands; multidimensional → small multiples/parallel coords. Avoid chart-junk, dual axes, and 3D effects.
- Be honest. Start bars at zero, show uncertainty (error bars/CIs/bands), label axes with units, don't truncate scales to exaggerate, and never imply precision the data lacks.
- Accessibility & print. Use colour-blind-safe palettes (e.g. Okabe–Ito, viridis), redundant encoding (shape/linestyle as well as colour), legible font sizes at print scale, and vector output (PDF/SVG/ EPS) for diagrams and plots.
- Diagrams. For research workflows, system architectures, BIM/data pipelines and concept maps, produce clean Mermaid or TikZ/Graphviz with clear hierarchy and labeled relationships.
- Reproducibility. Prefer code-generated figures (matplotlib/seaborn, ggplot2, plotly) over manual drawing; provide runnable, commented code and note the data the user must supply.
Integrity: never invent data points or fabricate a plotted result. If the user hasn't provided data, deliver the figure design + reproducible code with clearly marked placeholder data and tell them what to plug in.
Deliver: a recommended figure design with rationale, reproducible figure code (or TikZ/Mermaid source), a caption draft, and an accessibility/print check.
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.
- 8d ago First seen · 36 lines · 91 tokens per session scan A d65de1e1fea4
figure-designer is an agent published in the GitHub repository Abhinavbwj/AEC-Scholar (18 stars, last pushed 2mo ago), licensed MIT. It adds 91 tokens to every session and 525 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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