SenseNova-Skills is a collection of modular skills that extend SenseNova models with office-assistant capabilities such as image generation, presentation creation, spreadsheet analysis, and research. The skills are designed for use in agent runtimes and can be combined into productivity workflows; the catalogue entries are individual skills and agents from this collection.
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 OpenSenseNova/SenseNova-Skills --skill threshold-filteringgit clone --depth 1 https://github.com/OpenSenseNova/SenseNova-SkillsWrote 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/opensensenova/sensenova-skills/threshold-filtering)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/threshold-filtering"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/threshold-filtering/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/opensensenova/sensenova-skills/threshold-filtering"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/threshold-filtering.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 | $0.00049 | $0.00511 |
| Opus 5 | $0.00024 | $0.00255 |
| Sonnet 5 | $0.00010 | $0.00102 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
excel-threshold-analysis-and-styling 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 10d 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
Excel Threshold Analysis and Styling
Note: This sub-skill covers one step of the Excel analysis workflow. For the full pipeline (file reading, row counting, large-file optimization, export), see the parent workflow SKILL.md.
Step1 读取 Excel 文件中所有工作表的行数并汇总，用于评估数据规模。
import pandas as pd
file_path = 'input_file.xlsx'
# 读取所有 sheet 名称并统计总行数
xls = pd.ExcelFile(file_path)
sheet_names = xls.sheet_names
total_rows = 0
for sheet in sheet_names:
# header=None 用于快速统计包含表头的总行数
df_tmp = pd.read_excel(file_path, sheet_name=sheet, header=None)
rows = len(df_tmp)
total_rows += rows
print(f"Sheet '{sheet}': {rows} 行")
print(f"\n总行数汇总: {total_rows}")
Step2 对目标数据表进行清洗，将指定列的非数值内容转换为缺失值并剔除，确保数据类型为数值型。
target_sheet = 'Sheet1'
target_col = '数量' # 待处理的目标列名
header_idx = 1 # 表头所在行索引（0开始计数）
df = pd.read_excel(file_path, sheet_name=target_sheet, header=header_idx)
# 强制转换数值类型，无法转换的内容变为 NaN 并删除
df[target_col] = pd.to_numeric(df[target_col], errors='coerce')
df_cleaned = df.dropna(subset=[target_col])
print(f"清洗完成，有效数据行数: {len(df_cleaned)}")
Step3 筛选符合特定数值条件的记录并进行统计。
filter_threshold = 10
df_filtered = df_cleaned[df_cleaned[target_col] > filter_threshold]
print(f"{target_col} 大于 {filter_threshold} 的记录共有 {len(df_filtered)} 条")
Step4 使用 openpyxl 对原始文件中
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.
- 10d ago First seen · 56 lines · 49 tokens per session scan A 91dfaf4f9852
excel-threshold-analysis-and-styling is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,476 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 511 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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