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 xlsxgit 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/xlsx)<a href="https://agentmods.dev/skills/woodfishhhh/ez_math_model/xlsx"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/xlsx/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/xlsx"><img src="https://agentmods.dev/badge/skills/woodfishhhh/ez_math_model/xlsx.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 | $0.00034 | $0.00516 |
| Opus 5 | $0.00017 | $0.00258 |
| Sonnet 5 | $0.00007 | $0.00103 |
| Haiku 4.5 | $0.00003 | $0.00052 |
Grade A, and why
xlsx 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 9d 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
xlsx — 表格数据读写
何时使用
- 读取
.xlsx、.xls、.csv附件。 - 写出
results/qN_summary.csv或.xlsx。 - 生成
intake.json.attachments[i].preview。 - 大 CSV 需要分块处理。
预览契约
输出到 intake preview：
{
"shape": [1024, 8],
"columns": ["timestamp", "value"],
"head_3": [{"timestamp": "2024-01-01", "value": 12.3}],
"missing_per_col": {"value": 5}
}
优先链路
pandas读写和清洗。openpyxl写公式或保留 Excel 格式。- 宿主
xlsxskill 处理公式刷新、LibreOffice 重算等高级场景。
pandas 模板
import pandas as pd
df = pd.read_excel("attachments/data.xlsx")
preview = {
"shape": list(df.shape),
"columns": df.columns.tolist(),
"head_3": df.head(3).to_dict("records"),
"missing_per_col": {c: int(n) for c, n in df.isna().sum().items() if n > 0},
}
df.to_csv("results/q1_summary.csv", index=False, encoding="utf-8")
CSV 编码尝试顺序：utf-8 → gbk → gb2312 → latin-1。
大文件协议
pd.read_csv(path, chunksize=200_000)。- 提前指定
dtype。 - 必要时抽样 5% 做 EDA。
- 处理完及时释放分块 DataFrame。
失败诊断
| 情况 | 处理 |
|---|---|
| 编码全失败 | 写诊断，建议用户提供 utf-8 版 |
| 列名含非法字符 | 生成 ASCII 安全别名并保留 column_alias.json |
| 文件超过 1GB | chunksize 分块处理 |
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.
- 9d ago First seen · 65 lines · 34 tokens per session scan A ebb291a1a9e5
xlsx is a skill published in the GitHub repository woodfishhhh/EZ_math_model (41 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 516 once invoked, about $0.0002 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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