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 top-value-coloringgit 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/top-value-coloring)<a href="https://agentmods.dev/skills/opensensenova/sensenova-skills/top-value-coloring"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/top-value-coloring/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/top-value-coloring"><img src="https://agentmods.dev/badge/skills/opensensenova/sensenova-skills/top-value-coloring.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 | $0.00045 | $0.01089 |
| Opus 5 | $0.00023 | $0.00544 |
| Sonnet 5 | $0.00009 | $0.00218 |
| Haiku 4.5 | $0.00005 | $0.00109 |
Grade A, and why
top-value-coloring 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 12d 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
Step1 提取并合并多个 Sheet 中的关键维度数据,进行数据清洗、类型转换及 Top-N 筛选。
# 示例:合并两个 Sheet 的数据
# 读取 Sheet1 并清洗
df1 = pd.read_excel(file_path, sheet_name='Sheet1', header=None)
# 假设 group_col 在第0列,value_col 在第2列
data1 = df1.iloc[20:, [0, 2]].copy()
data1.columns = ['group_col', 'value_col_1']
data1['value_col_1'] = pd.to_numeric(data1['value_col_1'], errors='coerce')
data1['group_col'] = data1['group_col'].ffill() # 处理合并单元格产生的缺失
# 读取 Sheet2 并清洗
df2 = pd.read_excel(file_path, sheet_name='Sheet2', header=None)
data2 = df2.iloc[5:, [0, 1]].copy()
data2.columns = ['value_col_2', 'value_col_3']
# 合并数据
merged_df = pd.concat([data1.reset_index(drop=True), data2.reset_index(drop=True)], axis=1)
merged_df = merged_df.dropna(subset=['value_col_1'])
# 筛选关键指标前五的数据
top_results = merged_df.nlargest(5, 'value_col_1').copy()
# 占位示例:修正特定缺失值
# top_results.loc[top_results['group_col'].isna(), 'group_col'] = 'Default_Value'
Step2 使用 openpyxl 创建格式化表格,应用条件样式(如特定列标红、最大值高亮)并设置边框与对齐方式。
from openpyxl import Workbook
from openpyxl.styles import Font, PatternFill, Alignment, Border, Side
output_path = 'analysis_report.xlsx'
# 创建工作簿
wb = Workbook()
ws = wb.active
ws.title = 'Analysis_Results'
# 定义样式
header_fill = PatternFill(start_color='4472C4', end_color='4472C4', fill_type='solid')
header_font = Font(bold=True, color='FFFFFF', size=12)
red_font = Font(color='FF0000', bold=True) # 用于高亮异常或关键值
green_fill = PatternFill(start_color='C6EFCE', end_color='C6EFCE', fill_type='solid') # 用于高亮最大值
thin_border = Border(left=Side(style='thin'), right=Side(style='thin'),
top=Side(style='thin'), bottom=Side(style='thin'))
center_align = Alignment(horizontal='center', vertical='center')
# 写入表头
headers = ['Rank'] + list(top_results.columns)
for col, header in enumerate(headers, 1):
cell = ws.cell(row=1, column=col, value=header)
cell.font = header_font
cell.fill = header_fill
cell.alignment = center_align
cell.border = thin_border
# 写入数据并应用样式
for idx, (_, row) in enumerate(top_results.iterrows(), 2):
# 写入排名
ws.cell(row=idx, column=1, value=idx-1).border = thin_border
# 写入各列数据
for col_idx, value in enumerate(row, 2):
cell = ws.cell(row=idx, column=col_idx, value=value)
cell.border = thin_border
# 逻辑高亮示例:对特定列(如第4列)应用红色字体
if col_idx == 4:
cell.font = red_font
# 逻辑高亮示例:对超过阈值的值应用绿色填充
# if isinstance(value, (int, float)) and value > threshold_val:
# cell.fill = green_fill
# 自动调整列宽
column_widths = {'A': 8, 'B': 30, 'C': 15, 'D': 15, 'E': 18}
for col, width in column_widths.items():
ws.column_dimensions[col].width = width
# 设置数字格式
for row in range(2, ws.max_row + 1):
ws.cell(row=row, column=3).number_format = '#,##0'
ws.cell(row=row, column=4).number_format = '#,##0.00'
wb.save(output_path)
print(f"Formatted file saved to: {output_path}")
Step3 生成并输出结果文件的下载链接。
# 必须使用 sandbox:/ 前缀生成下载链接
print(f"[下载分析结果]({f'sandbox:{output_path}'})")
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.
- 12d ago First seen · 100 lines · 45 tokens per session scan A 8dce83c3b9e8
top-value-coloring is a skill published in the GitHub repository OpenSenseNova/SenseNova-Skills (5,570 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 1,089 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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