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 Bwkyd/wps-skills --skill wps-data-vizgit clone --depth 1 https://github.com/Bwkyd/wps-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/bwkyd/wps-skills/wps-data-viz)<a href="https://agentmods.dev/skills/bwkyd/wps-skills/wps-data-viz"><img src="https://agentmods.dev/badge/skills/bwkyd/wps-skills/wps-data-viz/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/bwkyd/wps-skills/wps-data-viz"><img src="https://agentmods.dev/badge/skills/bwkyd/wps-skills/wps-data-viz.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00094 | $0.01926 |
| Opus 5 | $0.00047 | $0.00963 |
| Sonnet 5 | $0.00019 | $0.00385 |
| Haiku 4.5 | $0.00009 | $0.00193 |
Grade A, and why
wps-data-viz 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.
How it starts
The opening of the file, as written. The whole thing — 203 lines — stays where its author put it; the contents beside it link to each section on GitHub.
数据可视化仪表盘
数据 → KPI卡片 + 趋势图 + 占比图 → 一页式数据概览。
不用Power BI,Excel也能做出专业仪表盘。
When to Use
- 制作数据概览/仪表盘
- 月度/季度数据一页展示
- KPI指标可视化
- 用户说"做个数据仪表盘""数据大屏"
When NOT to Use
- 单个图表 → 使用
wps-chart - 数据透视分析 → 使用
wps-pivot
仪表盘布局
┌─────────┬─────────┬─────────┬─────────┐
│ KPI 1 │ KPI 2 │ KPI 3 │ KPI 4 │
│ 总收入 │ 订单数 │ 客单价 │ 转化率 │
│ ¥125万 │ 3,456 │ ¥362 │ 4.2% │
│ ↑12.5% │ ↑8.3% │ ↓2.1% │ ↑0.5% │
├─────────┴─────────┼─────────┴─────────┤
│ │ │
│ 收入趋势折线图 │ 品类占比饼图 │
│ (12个月) │ │
│ │ │
├────────────────────┼────────────────────┤
│ │ │
│ TOP10产品柱状图 │ 地区分布表格 │
│ │ │
└────────────────────┴────────────────────┘
工作流程
Step 1: 确认仪表盘要素
- KPI指标:需要展示哪些关键数字
- 趋势图:哪些数据看趋势
- 占比图:哪些数据看构成
- 排名表:哪些数据看排名
- 数据源:数据在哪里
Step 2: 生成仪表盘
from openpyxl import Workbook
from openpyxl.chart import BarChart, LineChart, PieChart, Reference
from openpyxl.chart.label import DataLabelList
from openpyxl.styles import Font, Alignment, PatternFill, Border, Side
from openpyxl.utils import get_column_letter
import os
def create_dashboard(kpis, charts_data, output_path=None):
"""
kpis = [
{'name': '总收入', 'value': '¥125万', 'change': '+12.5%', 'trend': 'up'},
{'name': '订单数', 'value': '3,456', 'change': '+8.3%', 'trend': 'up'},
]
charts_data = {
'trend': {'labels': [...], 'values': [...]},
'pie': {'labels': [...], 'values': [...]},
'bar': {'labels': [...], 'values': [...]},
}
"""
wb = Workbook()
ws = wb.active
ws.title = "数据仪表盘"
ws.sheet_properties.tabColor = '2C3E50'
# 隐藏网格线
ws.sheet_view.showGridLines = False
# KPI卡片
kpi_colors = ['3498DB', '2ECC71', 'E74C3C', 'F39C12']
for i, kpi in enumerate(kpis[:4]):
col_start = i * 4 + 1
ws.merge_cells(start_row=2, start_column=col_start,
end_row=2, end_column=col_start + 3)
ws.merge_cells(start_row=3, start_column=col_start,
end_row=3, end_column=col_start + 3)
ws.merge_cells(start_row=4, start_column=col_start,
end_row=4, end_column=col_start + 3)
color = kpi_colors[i % len(kpi_colors)]
fill = PatternFill('solid', fgColor=color)
# 指标名
cell = ws.cell(row=2, column=col_start, value=kpi['name'])
cell.font = Font(name='微软雅黑', size=11, color='FFFFFF')
cell.fill = fill
cell.alignment = Alignment(horizontal='center')
# 指标值
cell = ws.cell(row=3, column=col_start, value=kpi['value'])
cell.font = Font(name='微软雅黑', size=22, bold=True, color='FFFFFF')
cell.fill = fill
cell.alignment = Alignment(horizontal='center')
# 变化
arrow = '↑' if kpi.get('trend') == 'up' else '↓'
cell = ws.cell(row=4, column=col_start,
value=f'{arrow} {kpi["change"]}')
cell.font = Font(name='微软雅黑', size=10, color='FFFFFF')
cell.fill = fill
cell.alignment = Alignment(horizontal='center')
# 数据区域(隐藏,供图表引用)
data_start_row = 30
if 'trend' in charts_data:
td = charts_data['trend']
for i, label in enumerate(td['labels']):
ws.cell(row=data_start_row + i, column=1, value=label)
ws.cell(row=data_start_row + i, column=2, value=td['values'][i])
chart = LineChart()
chart.title = "趋势"
chart.width = 20
chart.height = 12
chart.style = 10
data = Reference(ws, min_col=2, min_row=data_start_row,
max_row=data_start_row + len(td['labels']) - 1)
cats = Reference(ws, min_col=1, min_row=data_start_row,
max_row=data_start_row + len(td['labels']) - 1)
chart.add_data(data)
chart.set_categories(cats)
ws.add_chart(chart, 'A6')
if 'pie' in charts_data:
pd = charts_data['pie']
for i, label in enumerate(pd['labels']):
ws.cell(row=data_start_row + i, column=4, value=label)
ws.cell(row=data_start_row + i, column=5, value=pd['values'][i])
chart = PieChart()
chart.title = "占比"
chart.width = 14
chart.height = 12
data = Reference(ws, min_col=5, min_row=data_start_row,
max_row=data_start_row + len(pd['labels']) - 1)
cats = Reference(ws, min_col=4, min_row=data_start_row,
max_row=data_start_row + len(pd['labels']) - 1)
chart.add_data(data)
chart.set_categories(cats)
chart.dataLabels = DataLabelList()
chart.dataLabels.showPercent = True
ws.add_chart(chart, 'I6')
if not output_path:
output_path = '数据仪表盘.xlsx'
wb.save(output_path)
return os.path.abspath(output_path)
What ships with it
1 file 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.
- 12d ago First seen · 203 lines · 94 tokens per session scan A e367f179fd0c
wps-data-viz is a skill published in the GitHub repository Bwkyd/wps-skills (8 stars, last pushed 4mo ago), licensed MIT. It adds 94 tokens to every session and 1,926 once invoked, about $0.0005 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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