trend-analysis
基于多维度数据进行分级评估与趋势预测，通过设定差异化增长率计算预测值，并生成对比可视化图表，适用于绩效评估、目标设定等场景。
npx skills add OpenSenseNova/SenseNova-Skills --skill trend-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
Step1 加载数据并配置环境，设置中文字体以确保可视化图表正常显示。 ```python import pandas as pd import numpy as np import matplotlib.pyplot as plt import warnings warnings.filterwarnings('ignore') # 设置中文字体，优先使用 WenQuanYi Zen Hei，备选 SimHei 和 DejaVu Sans plt.rcParams['font.sans-serif'] = ['WenQuanYi Zen Hei', 'SimHei', 'DejaVu Sans'] plt.rcParams['axes.unicode_minus'] = False # 加载数据文件 file_path = 'your_data.xlsx' df = pd.read_excel(file_path) print(f"数据形状: {df.shape}") df.head() ``` Step2 基于数据表现划分等级并设定差异化增长率，计算预测结果。 ```python # 定义通用列名 group_col = '分组列名' # 示例：'部门'、'产品线' target_col = '目标数值列名' # 示例：'销售额'、'产量' # 计算各维度的总值并排序 performance_data = df.groupby(group_col, as_index=False)[target_col].sum().sort_values(by=target_col, ascending=False) # 划分等级（前30%为高，后30%为低，其余为中等） n = len(performance_data) high_perf_threshold = int(0.3 * n) low_perf_threshold = int(0.7 * n) performance_data['等级'] = '中等' performance_data.loc[:high_perf_threshold-1, '等级'] = '高' performance_data.loc[low_perf_threshold:, '等级'] = '低' # 设定预测增长率映射字典 growth_rate_map = { '高': 0.10, # 10% 增长率 '中等': 0.08, # 8% 增长率 '低': 0.15 # 15% 增长率 } performance_data['预测增长率'] = performance_data['等级'].map(growth_rate_map) # 计算预测值 = 当前值 × (1 + 增长率)，保留两位小数 performance_data['
What does the trend-analysis skill do?
基于多维度数据进行分级评估与趋势预测，通过设定差异化增长率计算预测值，并生成对比可视化图表，适用于绩效评估、目标设定等场景。
How do I install it?
Run `npx skills add OpenSenseNova/SenseNova-Skills --skill trend-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From OpenSenseNova/SenseNova-Skills, a repository with 4,911 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.