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 MrSGSA/math-modeling-skill-dify --skill xlsxgit clone --depth 1 https://github.com/MrSGSA/math-modeling-skill-difyWrote 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/mrsgsa/math-modeling-skill-dify/xlsx)<a href="https://agentmods.dev/skills/mrsgsa/math-modeling-skill-dify/xlsx"><img src="https://agentmods.dev/badge/skills/mrsgsa/math-modeling-skill-dify/xlsx.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 | $0.00024 | $0.00940 |
| Opus 5 | $0.00012 | $0.00470 |
| Sonnet 5 | $0.00005 | $0.00188 |
| Haiku 4.5 | $0.00002 | $0.00094 |
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 7d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Excel 工具
许可:专有;完整条款见 LICENSE.txt。
原则
- 输入表格只读,输出写入
PROJECT_ROOT。 - 普通结果汇总优先 CSV。
- 题目指定 XLSX、需保留公式、多工作表或模板结构时才使用 XLSX。
- 不覆盖题目原始附件和 Skill 文件。
读取与写入
import pandas as pd
# 第一行就是数据时必须显式使用 header=None。
data = pd.read_excel("data/input.xlsx", sheet_name=0, header=None)
# 从题目数据合同、附件说明或独立原始计数填写;未知时保留 None 并先完成核验。
expected_rows = None
if expected_rows is None:
raise ValueError("请先从当前题目建立 expected_rows,不能沿用其他题数值")
if len(data) != expected_rows:
raise ValueError(f"行数异常: 实际 {len(data)},预期 {expected_rows}")
def write_result_table(names, values, output_path):
"""names/values 必须来自真实运行结果,output_path 必须位于 PROJECT_ROOT。"""
if len(names) != len(values):
raise ValueError("方案名与目标值数量不一致")
result = pd.DataFrame({"方案": names, "目标值": values})
result.to_excel(output_path, index=False)
禁止依赖 pandas.read_excel() 默认的 header=0 猜测表头,否则第一行数据会被当成列名。未知表头结构或必须精确保留行时使用无隐式推断的读取工具:
from scripts.read_rows import read_excel_rows
expected_rows = None # 从当前题目合同填写
if expected_rows is None:
raise ValueError("请先建立当前题目的预期行数")
rows = read_excel_rows(
"data/input.xlsx",
header=False,
expected_rows=expected_rows,
)
# 与原始附件独立抽取的首末行哨兵核对,不能在 Skill 中硬编码某道题的数值。
expected_first_row = None
expected_last_row = None
if expected_first_row is None or expected_last_row is None:
raise ValueError("请先记录当前附件的首末行哨兵")
if rows[0] != expected_first_row or rows[-1] != expected_last_row:
raise ValueError("首行或末行哨兵与原始附件不一致")
读取后必须核对首行、末行、原始工作表有效行数和题目声明的记录数;行数不符时立即报错,不得静默继续建模。
使用模板时用 openpyxl.load_workbook() 打开,写入指定单元格后另存新文件。不要删除未知工作表、命名区域、公式或验证规则。
公式重算
openpyxl 不计算公式。修改含公式的工作簿后使用 LibreOffice 隔离重算:
python scripts/recalc.py "<PROJECT_ROOT>/results/结果.xlsx" 60
工具会:
- 使用唯一临时 LibreOffice 配置;
- 把重算结果写入临时目录;
- 检查常见 Excel 错误;
- 仅在成功后原子替换目标文件;
- 超时、失败或未生成输出时保留原文件。
DOCX 与 XLSX 共用的 OOXML/LibreOffice 基础代码位于 ../docx/scripts/office/,本工具不再维护重复副本。
检查
- 公式引用和范围正确。
- 工作表名、列名、数据类型和单位正确。
- 已显式声明是否有表头,首行、末行和预期数据行数均已核对。
- 无
#VALUE!、#DIV/0!、#REF!、#NAME?、#NULL!、#NUM!、#N/A。 - 结果与代码终端输出和论文表格一致。
What ships with it
4 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.
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.
- 7d ago First seen · 90 lines · 24 tokens per session scan A f98ad1359d61
xlsx is a skill published in the GitHub repository MrSGSA/math-modeling-skill-dify (4 stars, last pushed 1mo ago), licensed MIT. It adds 24 tokens to every session and 940 once invoked, about $0.0001 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.
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