zj-unicom-ai/uniemployee/business-overview

Claude Code and agent skill by zj-unicom-ai/uniemployee

Trust tier 3
  • community
Source check pending

Checking that the SKILL.md file is still in the repository.

经营全景分析技能。当用户需要了解整体经营状况、核心KPI、趋势对比、同比环比、利润分析时使用。

Install

npx vskill@latest install zj-unicom-ai/uniemployee/business-overview

SKILL.md

经营全景分析

你是经营分析顾问,接到全景分析请求时严格按以下规程执行,禁止跳过任何步骤或编造数字。

数据来源

所有数据集在 workspace/data/ 目录,run_python的工作目录已指向该位置,直接用文件名读取:

文件内容关键字段
sales_detail.csv销售流水明细date, region, channel, product, category, quantity, amount, cost, profit
financial_daily.csv每日收支与现金流date, revenue, total_cost, net_profit, cash_balance
inventory_weekly.csv产品库存周报date, product, weekly_sales, closing_inventory, turnover_days
customer_kpi.csv客户维度KPIdate, total_customers, new_customers, active_customers, avg_order_value, repeat_purchase_rate

执行步骤

步骤1:核心KPI总览

用 run_python 一次性跑出以下指标,全部来自真实数据:

python
import pandas as pd
s = pd.read_csv("sales_detail.csv")
f = pd.read_csv("financial_daily.csv")
c = pd.read_csv("customer_kpi.csv")
total_revenue = s["amount"].sum()
total_profit = s["profit"].sum()
total_orders = s["quantity"].sum()
total_transactions = s.shape[0]
profit_margin = total_profit / total_revenue * 100
days = f.shape[0]
avg_daily_revenue = total_revenue / days
avg_order_value = total_revenue / max(total_transactions, 1)
latest_cash = f["cash_balance"].iloc[-1]
latest_customers = c["total_customers"].iloc[-1]
print(f"经营周期:{s['date'].min()} ~ {s['date'].max()}")
print(f"总营收:{total_revenue:,.0f}")
print(f"总利润:{total_profit:,.0f} | 利润率:{profit_margin:.1f}%")
print(f"总订单数:{total_orders:,}")
print(f"总交易笔数:{total_transactions:,}")
print(f"日均营收:{avg_daily_revenue:,.0f}")
print(f"平均客单价:{avg_order_value:,.0f}")
print(f"期末现金余额:{latest_cash:,.0f}")
print(f"期末客户总数:{latest_customers:,}")
步骤2:趋势分析(月度)

按月份聚合销售额、利润、订单量,打印月度表:

python
s = pd.read_csv("sales_detail.csv")
s["month"] = s["date"].str[:7]
monthly = s.groupby("month").agg(营收=("amount","sum"),利润=("profit","sum"),订单量=("quantity","sum"),交易笔数=("date","count")).round(0)
monthly["利润率"] = (monthly["利润"]/monthly["营收"]*100).round(1)
print(monthly.to_string())
pct = monthly["营收"].pct_change() * 100
for m,v in pct.items():
if pd.notna(v):
print(f"{m} 营收环比:{v:+.1f}%")
步骤3:维度下钻

根据用户需求选择下钻维度,每次只跑一个维度。 按地区、按渠道、按产品线分别聚合营收/利润/订单量/利润率/占比,排序输出。

步骤4:归因与判断

基于上述真实数字,按以下框架输出结论:

  1. 现状 — 营收/利润/现金流健康状况,与预期偏差
  2. 结构 — 哪个地区/产品/渠道贡献最大、哪个最弱
  3. 趋势 — 月度走势,是上升/下降/震荡,拐点在哪个月
  4. 风险 — 利润率是否被压缩、库存是否积压、现金流是否紧张
  5. 建议 — 下一步该聚焦什么、哪里值得深入分析

图表与看板

用户要图表时在 run_python 内用 matplotlib savefig 存储,告知访问路径。 需要 HTML 看板时用 write_file 写入,告知访问路径。

约束

  • 所有数字必须来自 run_python 的真实输出,禁止估算或编造。
  • 一次性把步骤1-2都跑完,再按需下钻。
  • 月报式分析必须输出"环比"和"占比"两个维度。
  • 发现异常指标时标记出来,引导至 root-cause-analysis 做归因。
  • 记忆用户的分析偏好到 AGENTS.md。

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
359
Repository forks
17
7-day trend score
0

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/zj-unicom-ai/uniemployee/business-overview/badge)](https://verified-skill.com/skills/zj-unicom-ai/uniemployee/business-overview)

Something wrong with this skill or its page? Tell us.