Agent skill

Business Overview Analysis

by zj-unicom-ai in zj-unicom-ai/UniEmployee

Produces a full business health analysis from sales, finance, inventory and customer CSV files: core KPIs, monthly trends, drill-downs and recommendations.

MITAuto-check passedBusiness, Finance & HR

SKILL.md written in Chinese; this summary is our English description.

Install Business Overview Analysis

skills CLI

$ npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install zj-unicom-ai/UniEmployee business-overview --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .claude/skills && cp -r skills-src/backend/skills/business-overview .claude/skills/business-overview && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "business-overview" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/business-overview into .claude/skills/business-overview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "business-overview", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/business-overview

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install zj-unicom-ai/UniEmployee business-overview --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .agents/skills && cp -r skills-src/backend/skills/business-overview .agents/skills/business-overview && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "business-overview" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/business-overview into .agents/skills/business-overview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "business-overview", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install zj-unicom-ai/UniEmployee business-overview --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/backend/skills/business-overview .cursor/skills/business-overview && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "business-overview" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/business-overview into .cursor/skills/business-overview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "business-overview", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/zj-unicom-ai/UniEmployee.git --path backend/skills/business-overview

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install zj-unicom-ai/UniEmployee business-overview --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/backend/skills/business-overview .gemini/skills/business-overview && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "business-overview" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/business-overview into .gemini/skills/business-overview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "business-overview", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install zj-unicom-ai/UniEmployee business-overview

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .github/skills && cp -r skills-src/backend/skills/business-overview .github/skills/business-overview && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "business-overview" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/business-overview into .github/skills/business-overview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "business-overview", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install zj-unicom-ai/UniEmployee business-overview --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/zj-unicom-ai/UniEmployee.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/backend/skills/business-overview .opencode/skills/business-overview && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "business-overview" agent skill from https://github.com/zj-unicom-ai/UniEmployee/tree/main/backend/skills/business-overview into .opencode/skills/business-overview/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "business-overview", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: business-overview
GitHub stars: 360
Token cost: ~662 tokens
SKILL.md length: 92 words
Files: 1
Skills in repo: 22
Repo updated: Oct 8, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Produces a full business health analysis from sales, finance, inventory and customer CSV files: core KPIs, monthly trends, drill-downs and recommendations.

Works in 5 steps: 现状 — 营收/利润/现金流健康状况，与预期偏差 → 结构 — 哪个地区/产品/渠道贡献最大、哪个最弱 → 趋势 — 月度走势，是上升/下降/震荡，拐点在哪个月 → …
Getting an overall picture of revenue, profit and cash flow
SKILL.md covers 数据来源, 执行步骤, 图表与看板 and 约束
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

In this skill the agent acts as a business analysis adviser. It reads four datasets from workspace/data/ (sales detail, daily finance and cash flow, weekly inventory, and customer KPIs) and runs Python to compute headline indicators and a monthly table of revenue, profit, order volume and transaction counts. Drill-downs by region, channel or product line run one dimension at a time, showing revenue, profit, margin and share in sorted order.

Conclusions follow a fixed frame of current position, structure, trend, risk and recommendation. Every figure must come from actual script output, with no estimates or invented numbers, and monthly-style reports must show both month-over-month change and share. Anomalies are flagged for root-cause analysis, charts are saved with matplotlib, an HTML dashboard can be written to a file, and the user's analysis preferences are recorded in AGENTS.md.

When your agent uses it

Getting an overall picture of revenue, profit and cash flow
Comparing month-over-month trends in sales and margin
Finding which region, channel or product line contributes most or least
Spotting margin squeeze, inventory buildup or tight cash flow early

Example prompts

“Give me a full business overview of this year with core KPIs and monthly trends.”
“Break the profit down by sales channel and show each channel's share.”
“Build an HTML dashboard of monthly revenue and profit and tell me where to find it.”

Requirements

The four CSV files in workspace/data/
A Python execution tool (run_python) with pandas and matplotlib

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit c38a00a. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are python).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Business Overview Analysis loads about 662 tokens when it runs. Until then it costs about 16 tokens; SKILL.md has 92 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~16
When it runs · the whole SKILL.md, loaded when a task matches: ~662

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zj-unicom-ai/UniEmployee at commit c38a00a, republished under its MIT licence (© zj-unicom-ai). 92 words, ~662 tokens.

Download SKILL.mdSave it as .claude/skills/business-overview/SKILL.md (or your agent's skills folder).

name: business-overview
description: 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。

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KPI	date, 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：

现状 — 营收/利润/现金流健康状况，与预期偏差
结构 — 哪个地区/产品/渠道贡献最大、哪个最弱
趋势 — 月度走势，是上升/下降/震荡，拐点在哪个月
风险 — 利润率是否被压缩、库存是否积压、现金流是否紧张
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。

Files

Just SKILL.md in backend/skills/business-overview of zj-unicom-ai/UniEmployee.

Open the folder on GitHubat commit c38a00a

Compare with similar skills

Business Overview Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Business Overview Analysis compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Business Overview Analysis this skillzj-unicom-ai/UniEmployee	360	—	~662	Automated safety check: Pass	MIT	3 days ago
Kpi Definition Helperjeremylongshore/tons-of-skills-marketplace	2.8k	—	~568	Automated safety check: Pass	MIT	yesterday
Python Executorcortega26/chile-hub	113	2 repos	~1.5k	Automated safety check: Pass	MIT	today
GeoPandas Spatial Analysisdavila7/claude-code-templates	33k	10 repos	~1.8k	Automated safety check: Pass	MIT	today
Analytics Data AnalysisMindrally/skills	271	—	~1.6k	Automated safety check: Pass	Apache-2.0	2 days ago
Earnings Flashdaloopa/investing	489	—	~1.9k	Automated safety check: Pass	Apache-2.0	3 days ago

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Works with

Questions about Business Overview Analysis

What does Business Overview Analysis do?

Produces a full business health analysis from sales, finance, inventory and customer CSV files: core KPIs, monthly trends, drill-downs and recommendations. In this skill the agent acts as a business analysis adviser. It reads four datasets from workspace/data/ (sales detail, daily finance and cash flow, weekly inventory, and customer KPIs) and runs Python to compute headline indicators and a monthly table of revenue, profit, order volume and transaction counts.

When should I use Business Overview Analysis?

Business Overview Analysis fits situations like: getting an overall picture of revenue, profit and cash flow; comparing month-over-month trends in sales and margin; finding which region, channel or product line contributes most or least; spotting margin squeeze, inventory buildup or tight cash flow early.

How do I install Business Overview Analysis in Claude Code?

Run `npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a claude-code`. Or copy the skill folder (backend/skills/business-overview in zj-unicom-ai/UniEmployee) into .claude/skills/business-overview in your project. Claude Code loads it when a task matches its description.

How do I install Business Overview Analysis in Codex?

Run `npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a codex`. Or copy the skill folder (backend/skills/business-overview in zj-unicom-ai/UniEmployee) into .agents/skills/business-overview in your project. Codex loads it when a task matches its description.

Can I use Business Overview Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zj-unicom-ai/UniEmployee --skill business-overview -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/business-overview, .gemini/skills/business-overview, .github/skills/business-overview and .opencode/skills/business-overview in your project.

What does Business Overview Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Business Overview Analysis is instructions for the agent only. Our summary lists: The four CSV files in workspace/data/; A Python execution tool (run_python) with pandas and matplotlib.

Does Business Overview Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Business Overview Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Business Overview Analysis use?

Business Overview Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Business Overview Analysis use?

About 662 tokens (SKILL.md is roughly 2.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Business Overview Analysis?

Skills that share tags, products or a category with Business Overview Analysis: Kpi Definition Helper (jeremylongshore/tons-of-skills-marketplace, 2.8k stars), Python Executor (cortega26/chile-hub, 113 stars), GeoPandas Spatial Analysis (davila7/claude-code-templates, 33k stars) and Analytics Data Analysis (Mindrally/skills, 271 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Business Overview Analysis?

zj-unicom-ai (a GitHub organization) maintains it in zj-unicom-ai/UniEmployee, which has 360 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.

Source: zj-unicom-ai/UniEmployee on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Business Overview Analysis

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

经营全景分析

数据来源

执行步骤

步骤1：核心KPI总览

步骤2：趋势分析（月度）

步骤3：维度下钻

步骤4：归因与判断

图表与看板

约束

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