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Order Analytics

订单数据业务分析仪表盘生成器。当用户提供订单Excel/CSV数据（含日期、金额、用户ID、场地/商品、时段等字段），需要生成业务分析报告、经营诊断、用户留存/频次分析、时段热力图、趋势对比、运营方案时，使用本技能。适用场景：场馆运营（羽毛球/网球/篮球/游泳）、餐饮门店、零售订单、服务业预约等任何有订单记录的业务场景。支持自定义配色（上传Logo图片或提供HEX/RGB色值）或选择预设主题。触发词：订单分析、经营诊断、用户分析、场次分析、留存分析、趋势分析、运营仪表盘、业务报告、数据报告。 Skill: Order Analytics Owner: minibeanai Summary: 订单数据业务分析仪表盘生成器。当用户提供订单Excel/CSV数据（含日期、金额、用户ID、场地/商品、时段等字段），需要生成业务分析报告、经营诊断、用户留存/频次分析、时段热力图、趋势对比、运营方案时，使用本技能。适用场景：场馆运营（羽毛球/网球/篮球/游泳）、餐饮门店、零售订单、服务业预约等任何有订单记录的业务场景。支持自定义配色（上传Logo图片或提供HEX/RGB色值）或选择预设主题。触发词：订单分析、经营诊断、用户分析、场次分析、留存分析、趋势分析、运营仪表盘、业务报告、数据报告。 Tags: latest:0.1.0 Version history: v0.1.0 | 2026-07-25T16:26:48.892Z | auto order-analytics v0.1.0 — Initial Release - C

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Downloads

3.0k

Updated

Oct 9, 2026

Version

0.1.0

Source

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About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 3K downloads reported by the source. Last updated 10/9/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 9, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 9, 2026
Adoption signal: 3K downloadsadoption · observed Oct 9, 2026
Latest release: 0.1.0release · observed Jul 25, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17b01j3yq2xecvwp3zcr41adh83nyep:business-data-analysis

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-minibeanai-business-data-analysis/snapshot"

Documentation

CLAWHUB

45,292 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: order-analytics
description: 订单数据业务分析仪表盘生成器。当用户提供订单Excel/CSV数据（含日期、金额、用户ID、场地/商品、时段等字段），需要生成业务分析报告、经营诊断、用户留存/频次分析、时段热力图、趋势对比、运营方案时，使用本技能。适用场景：场馆运营（羽毛球/网球/篮球/游泳）、餐饮门店、零售订单、服务业预约等任何有订单记录的业务场景。支持自定义配色（上传Logo图片或提供HEX/RGB色值）或选择预设主题。触发词：订单分析、经营诊断、用户分析、场次分析、留存分析、趋势分析、运营仪表盘、业务报告、数据报告。
---

# 订单业务分析仪表盘技能

## 概述

将原始订单数据转化为**交互式HTML分析报告**，6个Tab完整覆盖趋势→对比→时段→用户→流量→运营方案，支持桌面和手机端。**全程使用整月日均口径**，不做半月/等长截取对比。

> 📎 完整案例：`references/example-badminton.html` — 星辰羽毛球馆，紫色主题，6Tab

---

## 第一步：确认输入与配置

开始前确认以下信息（已在对话中提供则直接使用）：

### 1.1 数据文件
- 格式：`.xlsx`（用 `calamine` 引擎）/ `.csv`
- 必须字段：**订单日期、订单金额（或实收金额）、用户标识（手机号/用户ID）**
- 可选字段：场地/商品/服务类型、时段、订单状态、优惠金额

### 1.2 配色方案（四选一）
```
A. 上传Logo图片 → 运行 scripts/extract_colors.py 提取主色
B. 提供HEX色值 → 如 #7B5EA7（主色）+ #5B8DD9（辅色）
C. 提供RGB色值 → 如 rgb(123,94,167)
D. 选择预设主题（见 references/color-themes.md）：
   1. 紫蓝（精品/运动）  2. 橙绿（活力/场馆）
   3. 深蓝（科技/金融）  4. 米棕（优雅/餐饮）  5. 深色（夜间/高端）
```

### 1.3 业务场景说明（影响 Tab5 内容）
- 是否已开通大众点评商户通？
  - **已开通** → Tab5 展示点评流量分析（渠道来源、转化漏斗、团购表现、评价口碑）
  - **未开通** → Tab5 展示开通调研分析（新客来源现状、开通收益测算）
- 是否有竞品信息？→ Tab6 运营方案末尾加竞品对比表

---

## 第二步：数据处理

> 完整规范见 `references/data-processing.md`

### 2.1 读取与字段识别
```python
import pandas as pd
df = pd.read_excel(path, engine='calamine')  # 或 read_csv

FIELD_PATTERNS = {
    'date':    ['预订日期','下单时间','订单日期','日期','date','order_date'],
    'amount':  ['订单最终金额','实收金额','订单金额','金额','amount','price'],
    'user_id': ['手机号','用户ID','会员号','user_id','phone'],
    'item':    ['场地','商品','服务','项目','item','product'],
    'slot':    ['预订时段','时段','slot','time_slot'],
}
```

### 2.2 核心计算口径（⚠️ 关键原则）

**所有月度对比一律使用整月日均，不得用绝对总场次比较不同天数的月份。**

```python
# 整月日均（排除节假日锁场期，如春节）
monthly = df_clean.groupby('month').agg(
    op_days=('date', lambda x: x.dt.date.nunique()),
    total_orders=('amount','count'),
    total_rev=('amount','sum'),
    active_users=('user_id','nunique'),
)
monthly['daily_orders'] = (monthly['total_orders'] / monthly['op_days']).round(1)
monthly['daily_rev']    = (monthly['total_rev']    / monthly['op_days']).round(0)
```

**新老用户判断：**
```python
old_pool_months = sorted(df['month'].unique())[:3]  # 前3个月为老用户基准
old_pool = set(df[df['month'].isin(old_pool_months)]['user_id'])
```

**新用户次月留存：**
```python
first_order = df.groupby('user_id')['date'].min()
for mo, next_mo in zip(months[:-1], months[1:]):
    new_this = {u for u in set(df[df['month']==mo]['user_id'])
                if u not in old_pool and first_order[u] >= pd.Timestamp(f'{mo}-01')}
    retained  = new_this & set(df[df['month']==next_mo]['user_id'])
    retention = len(retained) / len(new_this)
```

**逐小时日均（整月，带 tooltip index mode）：**
```python
for mo in months:
    m = df_clean[df_clean['month']==mo]
    days = m['date'].dt.date.nunique()
    hourly[mo] = {h: round(len(m[m['hour']==h])/days, 2) for h in range(8,22)}

hchg = {h: round((hourly[last_mo][h]-hourly[ref_mo][h]) /
                  max(hourly[ref_mo][h],0.01)*100, 1)
        for h in range(8,22)}
```

---

## 第三步：配色主题

> 完整代码见 `references/color-themes.md`

### 3.1 预

README.md

# Business Data Analysis Skill

Transform raw order data into **interactive HTML analysis reports** with 6 tabs covering trends→comparison→time slots→users→traffic→operations, supporting desktop and mobile.

## Features

- 📊 **Overall Trends** - Daily average revenue line chart, orders+users dual-axis bars, frequency stack, new/old user stack, retention rate
- 🔍 **Monthly Comparison** - 5-period monthly comparison cards, daily avg orders/revenue, time slot breakdown, weekday/weekend analysis
- ⏱ **Time Analysis** - 5-period monthly line charts, heatmap, time slot bars, insight panels
- 👤 **User Structure** - New/old user stacked bars, frequency distribution, retention rate, active old user rate
- 📍 **Review Traffic** - Enabled: source pie + conversion funnel + deal table / Not enabled: status + analysis
- 🎯 **Operations Plan** - 3+3 strategy cards (P0/P1/P2) + competitor comparison

## Use Cases

- Venue operations (badminton/tennis/basketball/swimming)
- Restaurant stores
- Retail orders
- Service appointments
- Any business with order records

## Supported Data Formats

- Format: `.xlsx` / `.csv`
- Required fields: order date, order amount, user ID (phone/user ID)
- Optional fields: venue/product/service type, time slot, order status, discount amount

## Color Themes

5 preset themes available:
1. Purple-Blue (Premium/Sports)
2. Orange-Green (Vibrant/Venue)
3. Deep Blue (Tech/Finance)
4. Beige-Brown (Elegant/Restaurant)
5. Dark (Night/Luxury)

Also supports extracting main colors from logo images.

## Core Calculation Standard

All monthly comparisons use **full-month daily average** - no half-month or equal-length截取 comparison.

## Usage

1. Provide order Excel/CSV data file
2. Choose color theme (preset or custom)
3. Explain business scenario (has Dianping store or not)
4. AI automatically generates interactive analysis report

## Example

Full example: `references/example-badminton.html` — Xingchen Badminton Center, purple theme, 6 tabs

## Tech Stack

- Python (Pandas) - Data processing
- Chart.js - Data visualization
- HTML/CSS - Responsive reports

## License

MIT License

_meta.json

{
  "ownerId": "kn73gm1jmjpw7wv3xmg636vved822xw9",
  "slug": "business-data-analysis",
  "version": "0.1.0",
  "publishedAt": 1784996808892
}

references/color-themes.md

# 配色主题规范

## 1. 预设主题（直接复制使用）

### 主题1 — 紫蓝（精品/运动，技能示例主题）
此主题来自「星辰羽毛球馆」案例，参考图片：薰衣草色运动服+星空深紫背景。

```css
:root{
  --bg:#F5F3FA; --s1:#FFFFFF; --s2:#EDE8F5; --bd:#D8D0EC;
  --c1:#7B5EA7; --c2:#5B8DD9; --c3:#9B7DC7; --c4:#A390C8;
  --red:#B04848; --t:#1A1525; --mu:#7A6E90; --mu2:#C8C0DC; --tx:#2D2640;
}
```
- Hero: `background:linear-gradient(135deg,#5B3A8A,#6B4A9A,#4A3A7A); border-bottom:3px solid #3D2A6A`
- Tab:  `background:#4A3080; border-bottom:3px solid #35206A`
- Tab激活: `border-bottom-color:#CDB8F5`
- 图表配色: `C1='#7B5EA7', C2='#5B8DD9', C3='#9B7DC7', C4='#A390C8', RED='#B04848', PUR='#A040A0'`
- 新用户色: `C2+'55'`（蓝色），老用户色: `C1+'66'`（紫色）

### 主题2 — 橙绿（活力/运动场馆）
```css
:root{
  --bg:#F8F6F2; --s1:#FFFFFF; --s2:#F4F1EC; --bd:#E8E3DA;
  --c1:#4A7C59; --c2:#C97040; --c3:#D4884A; --c4:#5A8A6A;
  --red:#B04848; --t:#1A1A1A; --mu:#666666; --mu2:#CCCCCC; --tx:#333333;
}
```
- Hero: `background:linear-gradient(135deg,#B8694A,#A85C40,#985234); border-bottom:3px solid #8A4A2C`
- Tab:  `background:#4E7A62; border-bottom:3px solid #3A6550`
- Tab激活: `border-bottom-color:#C97040`
- 图表配色: `C1='#4A7C59', C2='#C97040', C3='#D4884A', C4='#5A8A6A', RED='#B04848'`

### 主题3 — 深蓝（科技/金融）
```css
:root{
  --bg:#F0F4FA; --s1:#FFFFFF; --s2:#E8EEFA; --bd:#D0DAEE;
  --c1:#2563EB; --c2:#F59E0B; --c3:#FBB040; --c4:#3B82F6;
  --red:#DC2626; --t:#0F172A; --mu:#64748B; --mu2:#CBD5E1; --tx:#1E293B;
}
```
- Hero: `background:linear-gradient(135deg,#1E3A8A,#1E40AF); border-bottom:3px solid #1A3570`
- Tab:  `background:#1E4080; border-bottom:3px solid #1A3570`

### 主题4 — 米棕（精品/餐饮/咖啡）
```css
:root{
  --bg:#FAF8F4; --s1:#FFFFFF; --s2:#F4F0E8; --bd:#E4DCCB;
  --c1:#8B6F47; --c2:#C4813D; --c3:#D4956A; --c4:#A08060;
  --red:#A84040; --t:#2C1F12; --mu:#7A6550; --mu2:#D0C4B0; --tx:#3D2E1C;
}
```
- Hero: `background:linear-gradient(135deg,#7A5A38,#5A3A20); border-bottom:3px solid #4A2A10`
- Tab:  `background:#6B5540; border-bottom:3px solid #5A4530`

### 主题5 — 深色（夜间/高端）
```css
:root{
  --bg:#0F1A0D; --s1:#162014; --s2:#1E2E1A; --bd:#2A4024;
  --c1:#4ADE80; --c2:#FB923C; --c3:#FACC15; --c4:#34D399;
  --red:#F87171; --t:#D8F0D0; --mu:#7AAA68; --mu2:#2A4024; --tx:#C8E8C0;
}
```
- Hero: `background:linear-gradient(135deg,#0D1F0A,#0F1A0D); border-bottom:3px solid #080F06`
- Tab:  `background:#1A3020; border-bottom:3px solid #0D2018`
- 热力图颜色需调整（深色背景）：使用更亮的颜色

---

## 2. 从Logo图片提取颜色

运行 `scripts/extract_colors.py`：

```bash
python3 scripts/extract_colors.py /path/to/logo.png --output css
```

输出示例：
```
提取颜色: #7B5EA7 (主), #5B8DD9 (辅)
:root {
  --c1: #7B5EA7;
  --c2: #5B8DD9;
  ...
}
```

---

## 3. 从HEX/RGB手动构建主题

给定主色 `primary` 和辅色 `accent`，套用如下规则：

```
--bg:  primary 极浅化 (lighten 90%+, desaturate)
--s1:  #FFFFFF
--s2:  primary 浅化 (lighten 85%)
--bd:  primary 浅化 (lighten 70%)
--c1:  primary  (主色，老用户/主指标/卡片左边条)
--c2:  accent   (辅色，新用户/次指标/强调)
--c3:  accent 浅一档
--c4:  primary 浅一档
--t:   非常深的同色系色（正文）
--mu:  中灰偏主色调（次要文字）

Hero bg:  primary 深化渐变（-10% → -20% → -30% lightness）
Tab bg:   primary 深化 -20%
```

P

references/data-processing.md

# 数据处理详细规范

## 1. 字段识别

### 自动映射规则（模糊匹配，优先级从高到低）

```python
FIELD_PATTERNS = {
    'date': ['预订日期', '下单时间', '订单日期', '日期', 'date', 'order_date', 'created_at'],
    'amount': ['订单最终金额', '实收金额', '实付金额', '订单金额', '金额', 'amount', 'price', 'total'],
    'order_amount': ['订单金额', '原价', 'original_price'],
    'user_id': ['手机号', '用户ID', '会员号', 'user_id', 'phone', 'member_id', 'customer_id'],
    'item': ['场地', '商品', '服务', '项目', '品类', 'item', 'product', 'service', 'court'],
    'slot': ['预订时段', '时段', '时间段', 'slot', 'time_slot', 'hour'],
    'status': ['订单状态', '状态', 'status', 'order_status'],
    'discount': ['优惠金额', '折扣', 'discount', 'coupon'],
}

def detect_fields(df):
    mapping = {}
    for std_name, patterns in FIELD_PATTERNS.items():
        for col in df.columns:
            if any(p.lower() in col.lower() for p in patterns):
                mapping[std_name] = col
                break
    return mapping
```

### 必须字段处理
- `date`：缺失则报错，提示用户指定日期列
- `amount`：缺失则尝试用 `order_amount` 代替，仍缺则只统计场次不统计收入
- `user_id`：缺失则只做订单分析，跳过用户留存分析

---

## 2. 多行拆分（场馆/多项目场景）

### 触发条件
字段值包含 `;` 分隔符，如：
- `场地`: `场地01;场地03`
- `时段`: `08:00~09:00;09:00~10:00`

### 拆分逻辑

```python
from itertools import product

def expand_row(row, item_col, slot_col):
    items = str(row[item_col]).split(';') if item_col else ['unknown']
    slots = str(row[slot_col]).split(';') if slot_col else ['unknown']
    
    # 判断展开模式
    if len(items) == 1 or len(slots) == 1:
        combos = list(product(items, slots))
    else:
        # 先尝试配对（1:1映射）
        n = max(len(items), len(slots))
        paired = [(items[min(i,len(items)-1)], slots[min(i,len(slots)-1)]) for i in range(n)]
        # 验证配对总价是否接近原始金额
        # 如不匹配则改用笛卡尔积
        combos = paired  # 默认配对
    
    return combos

def split_amount(total, std_prices, total_std):
    """按标准单价比例分摊金额"""
    if total_std == 0:
        return [total / len(std_prices)] * len(std_prices)
    return [total * p / total_std for p in std_prices]
```

---

## 3. 核心指标计算规范

### 3.1 时间处理

```python
df['date'] = pd.to_datetime(df['date'], errors='coerce', infer_datetime_format=True)
df['month'] = df['date'].dt.to_period('M').astype(str)
df['day'] = df['date'].dt.day
df['weekday'] = df['date'].dt.weekday  # 0=周一, 6=周日
df['is_weekend'] = df['weekday'] >= 5
df['hour'] = df['date'].dt.hour  # 如果时段是时间戳
# 如果时段是字符串如"08:00~09:00"，提取起始小时：
df['hour'] = df['slot'].str.extract(r'(\d+):').astype(float)
```

### 3.2 月度日均指标（推荐口径，排除天数差异）

```python
def calc_monthly(df, exclude_dates=None):
    """
    exclude_dates: list of (month, day_start, day_end) tuples for lockout periods
    e.g., [('2026-02', 15, 22)] for CNY lockout
    """
    if exclude_dates:
        for month, d1, d2 in exclude_dates:
            mask = (df['month'] == month) & df['day'].between(d1, d2)
            df = df[~mask]
    
    monthly = df.groupby('month').agg(
        total_orders=('amount', 'count'),
        total_rev=('amount', 'sum'),
        active_users=('user_id', 'nunique'),
        op_days=('date', la

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Machine-readable data

The same record, as JSON, for agents and crawlers.

{
  "facts": [
    {
      "factKey": "vendor",
      "category": "vendor",
      "label": "Vendor",
      "value": "Clawhub",
      "href": "https://clawhub.ai/minibeanai/skills/business-data-analysis",
      "sourceUrl": "https://clawhub.ai/minibeanai/skills/business-data-analysis",
      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-09T10:24:40.288Z",
      "isPublic": true
    },
    {
      "factKey": "protocols",
      "category": "compatibility",
      "label": "Protocol compatibility",
      "value": "OpenClaw",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-minibeanai-business-data-analysis/contract",
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      "sourceType": "contract",
      "confidence": "medium",
      "observedAt": "2026-10-09T10:24:40.288Z",
      "isPublic": true
    },
    {
      "factKey": "traction",
      "category": "adoption",
      "label": "Adoption signal",
      "value": "3K downloads",
      "href": "https://clawhub.ai/minibeanai/business-data-analysis",
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      "sourceType": "profile",
      "confidence": "medium",
      "observedAt": "2026-10-09T10:24:40.288Z",
      "isPublic": true
    },
    {
      "factKey": "latest_release",
      "category": "release",
      "label": "Latest release",
      "value": "0.1.0",
      "href": "https://clawhub.ai/minibeanai/business-data-analysis",
      "sourceUrl": "https://clawhub.ai/minibeanai/business-data-analysis",
      "sourceType": "release",
      "confidence": "medium",
      "observedAt": "2026-07-25T16:26:48.892Z",
      "isPublic": true
    },
    {
      "factKey": "handshake_status",
      "category": "security",
      "label": "Handshake status",
      "value": "UNKNOWN",
      "href": "https://www.xpersona.co/api/v1/agents/clawhub-minibeanai-business-data-analysis/trust",
      "sourceUrl": "https://www.xpersona.co/api/v1/agents/clawhub-minibeanai-business-data-analysis/trust",
      "sourceType": "trust",
      "confidence": "medium",
      "observedAt": null,
      "isPublic": true
    }
  ],
  "events": [
    {
      "eventType": "release",
      "title": "Release 0.1.0",
      "description": "order-analytics v0.1.0 — Initial Release - Converts raw order data (Excel/CSV) into an interactive HTML business analytics dashboard, suitable for venues, retail, F&B, and service industries. - Covers comprehensive business analysis across 6 tabs: Trends, Comparison, Time Slots, User Structure, Dianping Traffic (if applicable), and Operational Strategy. - Implements strict monthly average metrics (no partial-month or non-equivalent comparisons). - Supports custom color themes via logo extraction, HEX/RGB input, or preset themes. - Mobile-responsive by default, with all grids and charts adapting for small screens. - Provides reference example, code snippets, and verification checks for report completeness.",
      "href": "https://clawhub.ai/minibeanai/business-data-analysis",
      "sourceUrl": "https://clawhub.ai/minibeanai/business-data-analysis",
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      "observedAt": "2026-07-25T16:26:48.892Z",
      "isPublic": true
    }
  ]
}

Record generated Oct 9, 2026.

About

Avoid when

Public facts

Install and run

Documentation

Extracted files

SKILL.md

README.md

_meta.json

references/color-themes.md

references/data-processing.md

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Machine-readable data