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/lancegui/causal-powers/figure-craftnpx skills add lancegui/causal-powers --skill figure-craftgit clone --depth 1 https://github.com/lancegui/causal-powersWrote 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/lancegui/causal-powers/figure-craft)<a href="https://agentmods.dev/skills/lancegui/causal-powers/figure-craft"><img src="https://agentmods.dev/badge/skills/lancegui/causal-powers/figure-craft.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 | $0.00248 | $0.06619 |
| Opus 5 | $0.00124 | $0.03309 |
| Sonnet 5 | $0.00050 | $0.01324 |
| Haiku 4.5 | $0.00025 | $0.00662 |
Grade A, and why
figure-craft 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.
How it starts
The opening of the file, as written. The whole thing — 277 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Figure Craft
Overview
The discipline skills keep you from being wrong; this skill keeps the figure readable at a glance from the back of the room. A figure is a communication artifact, not a debug plot — it will be projected on a screen, embedded in a paper, or dropped into a slide deck, and the audience will give it three seconds. It may also be printed in black and white. Every choice below serves those constraints.
Core principle: the minimum ink that conveys the idea, at a size the back row can read, with nothing clipped or overlapping — legible in color and in grayscale. Restraint, not decoration.
This guide applies to EVERY figure, in every language. The house style is defined by its principles (clean theme, colorblind-safe palette, large fonts, no y-axis label, concise labels, right geom, B&W-safe, visual self-check); the implementation differs by language. The R implementation — theme_hc() + scale_color_ptol() from ggthemes — is the reference because it's the most direct, but the same principles map to Python and Julia. Don't skip the style because you're not in R; translate it.
This skill fires after the data is right — after question-framing (what each mark encodes, the unit, the joins), after descriptive-evidence or causal-identification (the number is real), and after result-verification (it reproduces). It governs only the rendering — the bridge from a verified result to a presentation-ready exhibit. If the data hasn't been validated yet, route there first (result-verification itself runs only if the user asks for it); a beautiful figure of a wrong number is still a wrong number.
Where this sits
This is the last step of the analysis chain, not the first. The figure renders a result that has already been earned:
question-framing→ what each mark encodes, the unit, the joins — frame before you plot.descriptive-evidenceorcausal-identification→ the number is real and the composition / identification is checked.result-verification→ the number reproduces from a clean seed.figure-craft→ render the verified result as a presentation-ready exhibit.project-organization→ save the figure toresults/figures/with a conventional name (fig_<what_it_shows>.pdf); the figure is a deliverable, not scratch.
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 · 277 lines · 248 tokens per session scan A afd9d2fc1031
figure-craft is a skill published in the GitHub repository lancegui/causal-powers (2 stars, last pushed 10d ago), licensed MIT. It adds 248 tokens to every session and 6,619 once invoked, about $0.0012 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 2026-08-31.
Other skills, from other repositories
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fin-paper-writing
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fin-viz-launch
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fin-data-acquisition
根据REFINEDDESIGN.md中的变量定义，自动获取所需数据并生成可执行的回归分析脚本（Python/Stata）。.
fin-lit-review
经济金融领域的系统性文献综述。整合 Semantic Scholar + ArXiv + OpenAlex + NBER 构建引文网络，识别研究缺口，生成结构化文献地图。.
fin-submit-check
根据目标期刊要求，对论文进行投稿前全面检查，涵盖格式、引用、图表、数据可用性等所有投稿必需项。.