القائمة
Creates data visualizations, charts, and interactive dashboards. Invoke when user wants to create plots, graphs, or visual representations of data.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
全面的AB测试分析工具，支持实验设计、统计检验、用户分群分析和可视化报告生成。用于分析产品改版、营销活动、功能优化等AB测试结果，提供统计显著性检验和深度洞察。
Perform multi-touch attribution analysis using Markov chains, Shapley values, and custom attribution models. Use when you need to analyze marketing channel effectiveness, calculate conversion attribution, optimize marketing budgets, or understand customer journey paths. Supports channel transition analysis, ROI calculation, and marketing optimization insights with Chinese language support.
Generates production-ready analysis code in Python, R, SQL. Invoke when user wants reusable code for data analysis, ML, or visualization.
Analyze text content using both traditional NLP and LLM-enhanced methods. Extract sentiment, topics, keywords, and insights from various content types including social media posts, articles, reviews, and video content. Use when working with text analysis, sentiment detection, topic modeling, or content optimization.
通用的 6 阶段数据分析助手：数据质量→探索性分析→假设生成→可视化→代码生成→综合报告。提供完整的方法论和模板！
自动化数据探索和可视化工具，提供从数据加载到专业报告生成的完整EDA解决方案。支持多种图表类型、智能数据诊断、建模评估和HTML报告生成。适用于医疗、金融、电商等领域的数据分析项目。
| name | visualization-specialist |
| description | Creates data visualizations, charts, and interactive dashboards. Invoke when user wants to create plots, graphs, or visual representations of data. |
Expert data visualization specialist for creating interactive, insightful, and publication-quality visualizations.
Invoke this skill when user:
用户可以指定图表类型：
创建包含多种图表类型的综合仪表板：
时间序列相关图表：
分布相关图表：
相关性可视化：
对比类图表：
根据用户需求创建特定图表
IMPORTANT: When creating visualizations with Chinese text, always configure proper fonts:
import matplotlib.pyplot as plt
import matplotlib
# Windows
matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'PingFang SC']
# Mac
matplotlib.rcParams['font.sans-serif'] = ['PingFang SC', 'Heiti SC', 'Arial Unicode MS']
# Linux
matplotlib.rcParams['font.sans-serif'] = ['WenQuanYi Micro Hei', 'SimHei']
# Must have this to show minus signs correctly
matplotlib.rcParams['axes.unicode_minus'] = False
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Configure Chinese font
plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei']
plt.rcParams['axes.unicode_minus'] = False
# Load data
df = pd.read_csv('./data_storage/your_data.csv')
# Create visualization
fig, ax = plt.subplots(figsize=(10, 6))
sns.histplot(data=df, x='column_name', kde=True, ax=ax)
ax.set_title('数据分布图', fontsize=14)
ax.set_xlabel('列名', fontsize=12)
ax.set_ylabel('频数', fontsize=12)
plt.tight_layout()
plt.savefig('./visualizations/distribution.png', dpi=300, bbox_inches='tight')
./visualizations/Work with other skills:
All visualization labels, titles, and annotations must be in Chinese: