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 agentmods add skills/malue-ai/dazee-small/chart-imagenpx skills add malue-ai/dazee-small --skill chart-imagegit clone --depth 1 https://github.com/malue-ai/dazee-smallWrote 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/malue-ai/dazee-small/chart-image)<a href="https://agentmods.dev/skills/malue-ai/dazee-small/chart-image"><img src="https://agentmods.dev/badge/skills/malue-ai/dazee-small/chart-image.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 | $0.00025 | $0.00663 |
| Opus 5 | $0.00013 | $0.00331 |
| Sonnet 5 | $0.00005 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
chart-image 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 5d 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
数据图表生成
从数据生成高质量图表图片，支持柱状图、折线图、饼图、散点图等常见类型。
使用场景
- 用户说「把这些数据做成图表」「画一个柱状图」
- 用户提供了 Excel/CSV 数据，需要可视化
- 与 excel-analyzer 配合，分析后自动生成图表
- 用户说「做一个销售趋势图」「画个饼图看看占比」
执行方式
基本用法
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
# 中文字体支持
plt.rcParams['font.sans-serif'] = ['PingFang SC', 'Microsoft YaHei', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
fig, ax = plt.subplots(figsize=(10, 6))
# 示例：柱状图
categories = ['一月', '二月', '三月', '四月']
values = [120, 150, 180, 200]
ax.bar(categories, values, color='#4A90D9')
ax.set_title('月度销售额', fontsize=16, fontweight='bold')
ax.set_ylabel('金额（万元）')
plt.tight_layout()
plt.savefig('/tmp/chart.png', dpi=150, bbox_inches='tight')
plt.close()
支持的图表类型
| 类型 | 方法 | 适用场景 |
|---|---|---|
| 柱状图 | ax.bar() |
分类对比 |
| 折线图 | ax.plot() |
趋势变化 |
| 饼图 | ax.pie() |
占比分析 |
| 散点图 | ax.scatter() |
相关性分析 |
| 水平柱状图 | ax.barh() |
排名对比 |
| 堆叠图 | ax.bar(bottom=) |
组成分析 |
样式规范
- 默认配色：
#4A90D9（蓝）、#E85D75（红）、#50C878（绿）、#F5A623（橙） - 分辨率：150 DPI（屏幕查看）或 300 DPI（打印）
- 中文标题和标签必须设置中文字体
- 图例放在不遮挡数据的位置
输出规范
- 图表保存为 PNG 文件到临时目录
- 返回文件路径供用户查看或嵌入报告
- 自动选择最合适的图表类型（用户未指定时）
- 数据量大时自动截断或聚合，避免图表拥挤
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.
- 5d ago First seen · 77 lines · 25 tokens per session scan A fa56fab80345
chart-image is a skill published in the GitHub repository malue-ai/dazee-small (36 stars, last pushed 5mo ago), licensed MIT. It adds 25 tokens to every session and 663 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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