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 OpenRaiser/PaperFit --skill float-optimizergit clone --depth 1 https://github.com/OpenRaiser/PaperFitWrote 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/openraiser/paperfit/float-optimizer)<a href="https://agentmods.dev/skills/openraiser/paperfit/float-optimizer"><img src="https://agentmods.dev/badge/skills/openraiser/paperfit/float-optimizer/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/openraiser/paperfit/float-optimizer"><img src="https://agentmods.dev/badge/skills/openraiser/paperfit/float-optimizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00000 | $0.03873 |
| Opus 5.5 | $0.00000 | $0.01549 |
| Sonnet 5.5 | $0.00000 | $0.00775 |
| Haiku 4.5 | $0.00000 | $0.00387 |
Grade A, and why
float-optimizer 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 21d 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 — 324 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Float Optimizer Skill
概述
本技能专门处理 Category B:浮动体缺陷,包括:
- B1:浮动体远离首次引用(Float-Reference Distance)
- B2:浮动体大小不适配栏宽(Float Width Mismatch)
- B3:连续多个浮动体堆叠无正文间隔(Float Clustering)
- B4:浮动体跨页分裂(Float Page Orphaning)
该技能由 code-surgeon-agent 调用,通过对浮动体位置参数、尺寸设置和周围正文结构的精确调整,实现图表与正文的和谐共处。浮动体优化是视觉排版中最具挑战性的环节之一,必须结合页图反馈迭代验证。
适用场景
当 layout-detective-agent 报告中出现以下缺陷 ID 时,路由至本技能:
| 缺陷 ID | 描述 | 优先级 |
|---|---|---|
| B1 | 浮动体远离首次引用 | High |
| B2 | 浮动体大小不适配栏宽 | High |
| B3 | 浮动体连续堆叠 | Medium |
| B4 | 浮动体跨页分裂 | Medium |
输入规范
| 输入项 | 来源 | 说明 |
|---|---|---|
主 .tex 文件路径 |
项目上下文 | 需修改的源文件 |
| 排版侦探报告 | layout-detective-agent 输出 |
包含缺陷对象(图表标签)、页码、描述 |
| 源码交叉引用报告 | extract_crossrefs.py 输出 (data/crossrefs.json) |
提供每个图表的首次引用行号、定义行号、行距离、节距离 |
| 当前 PDF 页图 | visual-inspector 输出 |
用于验证修复后视觉效果 |
| 模板类型 | 用户上下文或 templates.yaml |
单栏/双栏,影响浮动体宽度策略 |
输入使用说明:
- 从
crossrefs.json的distances[]数组中提取ref_line和figure_line,计算源码距离 - 若源码距离近(
line_distance < 50且section_distance = 0)但视觉距离远 → 典型 LaTeX 浮动体放置问题,使用本技能的浮动体参数调整策略 - 若源码距离本身远 → 优先建议移动源码位置(策略 3),而非调整浮动体参数
输出规范
修改完成后,必须返回以下信息:
{
"skill": "float-optimizer",
"status": "success | partial | failed",
"modified_files": ["main.tex"],
"changes": [
{
"defect_id": "B1",
"object": "Figure 3",
"action": "调整浮动体位置参数为 [ht]",
"before": "\\begin{figure}[t]",
"after": "\\begin{figure}[ht]"
},
{
"defect_id": "B2",
"object": "Table 1",
"action": "将 tabular 宽度设为 \\linewidth",
"before": "\\begin{tabular}{|l|c|c|}",
"after": "\\begin{tabularx}{\\linewidth}{|l|X|X|}"
}
],
"unresolved": []
}
修复策略
通用原则
- 浮动体是“浮动”的:LaTeX 的浮动体放置算法具有不确定性,同一份源码在不同编译中可能产生不同输出。因此,修复后必须多次编译以验证稳定性。
- 最小侵入:优先调整位置参数
[ht],其次调整尺寸,最后才考虑移动浮动体在源码中的位置。 - Endmatter 硬约束:正文浮动体不得进入
Acknowledgements、References、Bibliography所在页;若发生,按失败处理,并优先在 endmatter 前插入\FloatBarrier。 - 视觉验证必须:浮动体修复的效果必须在页图上肉眼确认,不能仅凭日志判断。
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.
- 21d ago Changed · +14 lines c260da288e4b
- 1mo ago First seen · 310 lines · 0 tokens per session scan A 1306e7c1d15f
float-optimizer is a skill published in the GitHub repository OpenRaiser/PaperFit (334 stars, last pushed 21d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,873 tokens. 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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