Agent skill

Retail Clerk Performance Analysis

by LeoYeAI in LeoYeAI/openclaw-master-skills

导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据). An agent skill from LeoYeAI/openclaw-master-skills.

MITAuto-check passed

Install Retail Clerk Performance Analysis

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a claude-code

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

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills retail-clerk-performance-analysis --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/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/retail-clerk-performance-analysis .claude/skills/retail-clerk-performance-analysis && 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/

Facts

Skill name
retail-clerk-performance-analysis
GitHub stars
2.2k
Token cost
~3.1k tokens
SKILL.md length
536 words
Files
4
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据). An agent skill from LeoYeAI/openclaw-master-skills.

  • Works in 10 steps: 核心业绩指标分析 → 雷达图能力对比 → 商品特征分析 → …
  • SKILL.md covers 技能名称, 功能描述, 快速开始 and 数据来源, plus 5 more sections
  • Runs Python scripts from its folder

What it does

Retail Clerk Performance Analysis is an agent skill from LeoYeAI/openclaw-master-skills. 导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据)。 输出导购个人详细诊断报告,包含: 1. 核心业绩指标(销售额、排名、业绩占比) 2. 雷达图能力对比(6维能力 vs 门店平均) 3. 商品特征分析(品类/价格带/包型/颜色/新品偏好) 4. Top5 SKU爆品分析(门店贡献率、SPU集中度、上市时间) 5. 订单结构分析(折扣/连带/会员结构) 6. AIoT高试用低转化分析(仅AIoT门店) 7. AIoT客户漏斗分析(仅AIoT门店) 8. 14天销售趋势分析 9. 综合诊断与行动建议 触发条件: - 用户询问导购业绩(如"李翠业绩怎么样") - 用户分析导购能力(如"导购销售能力如何") - 用户需要导购诊断(如"导购有什么问题")

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `USAGE.md`, `_meta.json` and `analyze.py`).

The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

Example prompts

  • “李翠业绩怎么样”
  • “导购销售能力如何”
  • “导购有什么问题”
  • “/retail-clerk-performance-analysis”

Requirements

  • Python 3

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. 核心业绩指标分析
  2. 雷达图能力对比
  3. 商品特征分析
  4. 爆品分析(Top5 SKU)
  5. 订单结构分析
  6. AIoT高试用低转化分析(仅AIoT门店)
  7. AIoT客户漏斗分析(仅AIoT门店)
  8. 销售额Top5与高试用低转化Top5对比(仅AIoT门店且customer-funnel正常)
  9. 14天销售趋势分析
  10. 综合诊断

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. 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

    Ships script files (Python), which the agent can run.

    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

Retail Clerk Performance Analysis loads about 3.1k tokens when it runs. Until then it costs about 96 tokens; SKILL.md has 536 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~96
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

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 LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 536 words, ~3,118 tokens.

Download SKILL.mdSave it as .claude/skills/retail-clerk-performance-analysis/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
retail-clerk-performance-analysis
description
导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据)。 输出导购个人详细诊断报告,包含: 1. 核心业绩指标(销售额、排名、业绩占比) 2. 雷达图能力对比(6维能力 vs 门店平均) 3. 商品特征分析(品类/价格带/包型/颜色/新品偏好) 4. Top5 SKU爆品分析(门店贡献率、SPU集中度、上市时间) 5. 订单结构分析(折扣/连带/会员结构) 6. AIoT高试用低转化分析(仅AIoT门店) 7. AIoT客户漏斗分析(仅AIoT门店) 8. 14天销售趋势分析 9. 综合诊断与行动建议 触发条件: - 用户询问导购业绩(如"李翠业绩怎么样") - 用户分析导购能力(如"导购销售能力如何") - 用户需要导购诊断(如"导购有什么问题")

导购个人业绩分析 Skill

技能名称

clerk-performance-analysis

功能描述

对单个导购进行深度业绩分析,支持两种门店类型:

  1. 普通门店(POS数据)- 所有门店可用
  2. AIoT门店(POS + AIoT数据)- 仅巽融智能体门店可用

输出导购个人的详细诊断报告,包含业绩指标、商品特征、订单结构、AIoT转化分析等多维度分析。

快速开始

详见 USAGE.md 获取完整使用指南。

简单示例
python
import sys
sys.path.insert(0, '~/.openclaw/skills/clerk-performance-analysis')
from analyze import analyze

# 执行分析(Skill自动判断门店类型)
result = analyze(
    store_id="416759_1714379448487",
    guide_name="李翠",
    from_date="2026-03-01",
    to_date="2026-03-25"
)

# 查看结果
print(f"销售额: ¥{result['core_metrics']['sales']['amount']:,.0f}")
print(f"排名: #{result['core_metrics']['sales']['rank']}")

数据来源

API 1: 导购详细数据
GET /api/v1/guide/detail?guideName={name}&storeId={id}&fromDate={from}&toDate={to}

返回数据结构:

  • guideOverallPerformance - 导购整体表现(含排名、雷达图)
  • featureDistribution - 商品特征分布(品类/价格带/包型/颜色/上市日期)
  • skuRanking - SKU销售排行
API 2: 订单分析数据
GET /api/v1/guide/order-analysis?storeId={id}&fromDate={from}&toDate={to}&guideName={name}

返回数据结构:

  • OrderDiscounts - 订单折扣分布
  • OrderAttachs - 订单连带分布
  • OrderMembers - 订单会员结构
API 3: AIoT高试用低转化分析(仅AIoT门店)
GET /api/v1/guide/high-trial-low-conversion?guideName={name}&storeId={id}&fromDate={from}&toDate={to}

返回数据结构:

  • highTrialLowConversion - 高试用低转化商品列表
    • goodsName - 商品名称
    • goodsModelCode - 款号
    • trialCount - 试用次数
    • dealCount - 成交件数
    • conversionRate - 转化率
    • standardPrice - 标准价
API 4: AIoT客户漏斗分析(仅AIoT门店)
GET /api/v1/guide/customer-funnel?guideName={name}&storeId={id}&fromDate={from}&toDate={to}

返回数据结构:

  • customerFunnel - 客户分层漏斗数据
    • 有效客户 - 有交互行为的客户总数
    • 普通客户 - 无试用行为的客户
    • 潜在客户 - 有普通试用的客户
    • 意向客户 - 有深度试用的客户
    • 成交客户 - 有成交的客户
  • 环比数据(与上期对比)

核心能力

1. 核心业绩指标分析
  • 销售额、排名、业绩占比
  • 订单数、客单价、连带率
  • 新客数、人效值、有订单天数
2. 雷达图能力对比

6维能力对比(导购 vs 门店平均):

  • 销售额
  • 订单数
  • 新客数
  • 客单价
  • 件单价
  • 连带率
3. 商品特征分析
  • 品类偏好: 主营品类及占比
  • 价格带分布: 主销价格带、高客单占比(≥800元)
  • 包型偏好: 主销包型分析
  • 颜色偏好: 主销颜色分析
  • 新品销售: 2026年新品销售占比
4. 爆品分析(Top5 SKU)
  • Top5 SKU列表: 销售额排名前五的商品明细
  • 销量分析: 各SKU销量、销售额、成交均价
  • 门店贡献率: 导购该SKU销售额占门店该SKU总销售的比例(反映导购对爆款的主导程度)
  • 价格分析: 标准价 vs 实际成交均价(折扣情况)
  • SPU集中度: 按商品名称聚合,分析款式集中度
  • 上市时间偏好: 分析导购对新品/老品的销售偏好
  • 新品识别: 成交时上市时间≤3个月的商品(可为负值=预售)
5. 订单结构分析
  • 折扣结构: 主销折扣区间、低折扣订单占比(6折以下)
  • 连带结构: 1件单占比、多连带占比(≥3件)、平均连带
  • 会员结构: 老会员/新会员/非会员销售占比
6. AIoT高试用低转化分析(仅AIoT门店)
  • 高试用低转化商品识别: 试用次数多但成交为0的商品
  • 转化能力评估: 分析导购的试用后跟进转化能力
  • 重点商品关注: Top3高试用低转化商品详情
7. AIoT客户漏斗分析(仅AIoT门店)
  • 客户分层漏斗: 有效客户→普通→潜在→意向→成交的转化路径
  • 环比趋势分析: 与上期对比的客户量变化
  • 工牌佩戴检测: 识别导购是否佩戴电子工牌(影响数据采集完整性)
  • 转化效率评估: 各层级的转化率分析

重要说明:

  1. 未佩戴电子工牌:只能记录成交客户(通过订单系统关联),无法获取试用行为数据。此时所有有效客户都显示为"成交客户",这是正常情况。
  2. 导购信息未维护:如果客户漏斗数据全为0,说明导购信息未在AIoT系统维护,此时high-trial-low-conversion返回的是门店整体数据,非个人数据。

边界情况处理:

情况现象处理方式
未佩戴工牌所有客户显示为成交客户正常,提示"未佩戴电子工牌"
信息未维护客户漏斗全为0警告"AIoT信息未维护",高试用低转化数据为门店整体
8. 销售额Top5与高试用低转化Top5对比(仅AIoT门店且customer-funnel正常)

前提条件:

  • 仅AIoT门店可用
  • customer-funnel 数据正常(不全为0)
  • 用于分析导购个人销售新品的情况

分析维度:

  • 重叠商品分析: 同时在高销售和高试用的商品
  • 销售-only商品: 销售好但试用少的商品
  • 试用-only商品: 试用多但未成交的商品
  • 新品转化分析: 高试用低转化中的新品识别(重点)
  • SPU集中度对比: 销售额Top5与高试用低转化Top5的SPU分布

触发培训建议:

  • 当高试用低转化商品中新品≥2个时,触发"新品转化专项提升"建议
9. 14天销售趋势分析
  • 日度销售追踪: 近14天每日销售额、订单数、客单价、业绩占比
  • 销售连续性: 有销售天数 vs 无销售天数
  • 峰值识别: 销售最高/最低的日期
  • 近期趋势: 近7天详细数据
10. 综合诊断
  • 业绩排名诊断
  • 能力短板识别
  • 品类集中度风险
  • 高客单销售能力评估
  • 连带销售能力评估
  • 议价能力评估
  • 销售连续性评估

使用方法

python
import sys
sys.path.insert(0, '~/.openclaw/skills/clerk-performance-analysis')
from analyze import analyze, batch_analyze

# 分析单个导购
result = analyze(
    store_id="416759_1714379448487",
    guide_name="李翠",
    from_date="2026-03-01",
    to_date="2026-03-25"
)

# 批量分析多个导购
results = batch_analyze(
    store_id="416759_1714379448487",
    guide_names=["李翠", "杨丽", "赵泽瑞"],
    from_date="2026-03-01",
    to_date="2026-03-25"
)

输入参数

参数类型必填说明
store_idstr是门店ID
guide_namestr是导购姓名
from_datestr是分析开始日期 (YYYY-MM-DD)
to_datestr是分析结束日期 (YYYY-MM-DD)

输出结构

json
{
  "status": "ok",
  "store_id": "416759_1714379448487",
  "guide_name": "李翠",
  "analysis_period": {"from": "2026-03-01", "to": "2026-03-25"},
  "core_metrics": {
    "sales": {"amount": 64559, "rank": 2, "share": 20.6},
    "orders": {"count": 96, "rank": 2},
    "atv": {"value": 672.49, "rank": 3},
    "attach": {"qty_ratio": 1.48, "sku_ratio": 1.43},
    "new_customers": {"count": 29, "rank": 1},
    "efficiency": {"value": 3398, "rank": 1, "order_days": 19}
  },
  "radar_analysis": {
    "salesAmount": {"guide": 64559, "store_avg": 43391, "ratio": 1.49, "gap": 21168},
    "effectiveOrderCount": {"guide": 96, "store_avg": 70, "ratio": 1.37, "gap": 26},
    "newCustomerCount": {"guide": 29, "store_avg": 16, "ratio": 1.81, "gap": 13},
    "customerUnitPrice": {"guide": 672, "store_avg": 655, "ratio": 1.03, "gap": 17},
    "qtyUnitPrice": {"guide": 455, "store_avg": 450, "ratio": 1.01, "gap": 5},
    "attachQtyRatio": {"guide": 1.48, "store_avg": 1.48, "ratio": 1.0, "gap": 0}
  },
  "feature_analysis": {
    "category": {"top": "女包", "top_percentage": 78, "distribution": [...]},
    "price_range": {"top": "1000-1200", "top_percentage": 30, "high_price_ratio": 65},
    "shape": {"top": "方包", "top_percentage": 30},
    "color": {"top": "黑色系", "top_percentage": 37},
    "new_items": {"ratio": 30}
  },
  "sku_analysis": {
    "top5_skus": [
      {
        "rank": 1,
        "name": "心遥",
        "code": "H51556659",
        "sales": 3510.1,
        "qty": 3,
        "avg_price": 1170.03,
        "contribute_rate": 100,
        "standard_price": 1299,
        "launch_date": "2025/7/1",
        "launch_year": 2025,
        "launch_month": 7,
        "is_new": false,
        "months_diff": 8
      }
    ],
    "total_skus": 5,
    "top5_total_sales": 13161.13,
    "top5_total_qty": 12,
    "spu_concentration": {
      "total_spu": 4,
      "spu_list": [
        {"name": "心遥", "sku_count": 2, "sales": 5444.12, "qty": 5},
        {"name": "舒珀", "sku_count": 1, "sales": 3409.05, "qty": 3}
      ]
    },
    "launch_date_analysis": {
      "total_items": 5,
      "new_items_count": 1,
      "new_items_ratio": 20.0,
      "launch_dates": [...]
    },
    "new_items": [
      {
        "name": "鞣迹",
        "code": "H51457102",
        "launch_date": "2025/12/1",
        "months_diff": 3,
        "is_new": true
      }
    ]
  },
  "order_analysis": {
    "discount": {
      "main_range": "8-9折",
      "main_share": 55.2,
      "low_discount_ratio": 27.6,
      "total_orders": 98
    },
    "attach": {
      "single_item_ratio": 65.3,
      "multi_item_ratio": 12.2,
      "avg_attach": 1.50,
      "total_orders": 98
    },
    "member": {
      "old_member_ratio": 64.2,
      "new_member_ratio": 33.3,
      "non_member_ratio": 2.3,
      "total_customers": 89
    }
  },
  "aiot_analysis": {
    "is_aiot_store": true,
    "item_count": 5,
    "total_trials": 14,
    "items": [
      {
        "name": "心遥(2)",
        "code": "H51757021",
        "trial_count": 3,
        "deal_count": 0,
        "conversion_rate": 0,
        "price": 1299
      }
    ]
  },
  "funnel_analysis": {
    "is_aiot_store": true,
    "effective_customers": 42,
    "deal_customers": 42,
    "conversion_rate": 100.0,
    "badge_not_worn": true,
    "note": "导购未佩戴电子工牌,仅记录成交客户",
    "funnel": {
      "有效客户": {"value": 42, "percentage": 100, "trend": "down", "link_relative_rate": 28.0},
      "普通客户": {"value": 0, "percentage": 0},
      "潜在客户": {"value": 0, "percentage": 0},
      "意向客户": {"value": 0, "percentage": 0},
      "成交客户": {"value": 42, "percentage": 100, "trend": "down", "link_relative_rate": 22.0}
    }
  },
  "findings": [...],
  "recommendations": [...]
}

核心指标解释

业绩指标
指标字段说明
销售额sales.amount销售总额
业绩排名sales.rank门店内排名
业绩占比sales.share占门店总业绩比例
订单数orders.count有效订单数
客单价atv.value平均客单价
连带率attach.qty_ratio数量连带率
新客数new_customers.count新增会员数
人效值efficiency.value日均业绩贡献
有订单天数efficiency.order_days统计周期内有成交的天数
雷达图指标
指标说明
ratio导购值 / 门店平均值,>1表示优于平均
gap导购与门店平均的绝对差距
商品特征指标
指标说明
top占比最高的品类/价格带/包型/颜色
top_percentage占比百分比
high_price_ratio800元以上商品销售占比
new_items.ratio2026年新品销售占比
订单结构指标
指标字段说明
主销折扣order_analysis.discount.main_range占比最高的折扣区间
低折扣占比order_analysis.discount.low_discount_ratio6折以下订单占比
1件单占比order_analysis.attach.single_item_ratio单件订单占比
多连带占比order_analysis.attach.multi_item_ratio≥3件订单占比
平均连带order_analysis.attach.avg_attach平均连带件数
老会员占比order_analysis.member.old_member_ratio老会员销售占比
新会员占比order_analysis.member.new_member_ratio新会员销售占比
AIoT指标(仅AIoT门店)
指标字段说明
是否AIoT门店aiot_analysis.is_aiot_store是否有AIoT数据
高试用低转化商品数aiot_analysis.item_count试用>0但成交=0的商品数
总试用次数aiot_analysis.total_trials这些商品的总试用次数
商品名称aiot_analysis.items[].name高试用低转化商品名称
试用次数aiot_analysis.items[].trial_count该商品被试用次数
成交件数aiot_analysis.items[].deal_count该商品成交件数
转化率aiot_analysis.items[].conversion_rate试用转化率
Show full SKILL.md (204 more words)Show less

诊断规则

问题判断条件严重程度
业绩排名靠后排名 > 3🟡 中
新客获取能力不足新客数 < 门店平均70%🟡 中
新客获取能力突出新客数 > 门店平均150%🟢 低(优势)
客单价偏低客单价 < 门店平均85%🟡 中
品类过于集中单一品类占比 > 70%🟡 中
高客单销售弱800元以上占比 < 30%🟡 中
连带销售能力不足1件单占比 > 60%🟡 中
低折扣订单占比过高6折以下订单 > 20%🟡 中
导购AIoT信息未维护客户漏斗全为0🟡 中(仅AIoT)
高试用低转化商品多≥3个高试用低转化商品🟡 中(仅AIoT)
新品试用转化差高试用低转化中新品≥2个🟡 中(仅AIoT)
有效客户大幅下降环比下降>20%🔴 高(仅AIoT)
无销售天数过多14天中≥5天无销售🟡 中
日均销售额偏低日均<¥1000🟡 中

分析示例

李翠(3月1日-25日)

核心业绩:

  • 销售额 ¥64,559,排名#2,占比20.6%
  • 订单数 96单,排名#2
  • 新客数 29人,排名#1
  • 人效值 3398,排名#1

雷达图对比:

  • 销售额:149%(优于平均49%)
  • 订单数:137%(优于平均37%)
  • 新客数:181%(优于平均81%)⭐
  • 客单价:103%(与平均持平)
  • 连带率:100%(与平均持平)

商品特征:

  • 主营品类:女包(78%)
  • 主销价格带:1000-1200元(30%)
  • 高客单占比:65%
  • 主销包型:方包(30%)
  • 新品销售占比:30%

订单结构:

  • 主销折扣:8-9折(55.2%)
  • 低折扣订单:27.6%(6折以下)
  • 1件单占比:65.3%
  • 多连带占比:12.2%
  • 老会员:64.2%,新会员:33.3%

爆品分析(Top5 SKU):

排名款号名称销量销售额均价门店贡献率上市时间新品
#1H51556659心遥3件¥3,510¥1,170100%2025/7
#2H51456675舒珀3件¥3,409¥1,136100%2025/7
#3H51457102鞣迹2件¥2,174¥1,08769%2025/12✓
#4H60101090爱旅2件¥2,134¥1,067100%2025/7
#5H51556656心遥2件¥1,934¥96734%2025/7

Top5合计: 销量12件, 销售额¥13,161(占总业绩20.4%)

门店贡献率 = 导购该SKU销售额 / 门店该SKU总销售额

SPU集中度:

  • 共涉及4个SPU(商品名称)
  • 心遥: 2个SKU, 销售额¥5,444(41.4%)
  • 舒珀: 1个SKU, 销售额¥3,409(25.9%)
  • 鞣迹: 1个SKU, 销售额¥2,174(16.5%)
  • 爱旅: 1个SKU, 销售额¥2,134(16.2%)

上市时间偏好:

  • 新品占比: 1/5 (20.0%)
  • 新品: 鞣迹 (2025/12/1, +3月)

AIoT高试用低转化分析(仅AIoT门店):

  • 发现 5 个高试用低转化商品
  • 总试用次数:14 次
  • Top3:心遥(2)3次、迁屿3次、合和3次(均0成交)

AIoT客户漏斗分析(仅AIoT门店):

  • 有效客户:42人,成交客户:42人,转化率100%
  • ℹ️ 导购未佩戴电子工牌,仅记录成交客户,无法获取试用行为数据
  • 有效客户环比下降28%(需关注)

诊断发现:

  • 🟢 业绩表现良好(排名#2)
  • 🟢 新客获取能力突出(门店第一)
  • 🟡 品类销售过于集中(女包78%)
  • 🟡 连带销售能力不足(1件单65.3%)
  • 🟡 低折扣订单占比过高(27.6%)
  • 🟡 存在5个高试用低转化商品(AIoT)
  • 🔴 有效客户环比下降28%(AIoT)

行动建议:

  1. 🔴 连带销售强化训练 - 练习二拍一话术、搭配推荐
  2. 🔴 试用跟进转化专项 - 针对心遥(2)等试用未成交商品,学习跟进话术
  3. 🔴 客流提升专项 - 加强邀约回店、社群运营(应对有效客户下降)
  4. 🟡 议价能力提升 - 减少不必要的折扣让步
  5. 🟡 拓展品类销售 - 加强其他品类学习

版本

v3.1.0 - 导购个人业绩深度分析(新增销售额Top5与高试用低转化Top5对比分析)

相关文档

  • USAGE.md - 详细使用指南和API说明

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files in skills/retail-clerk-performance-analysis of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • USAGE.md
  • _meta.json
  • analyze.py

Open the folder on GitHubat commit e5199b5

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Clerk Observabilityjeremylongshore/tons-of-skills-marketplace2.8k—~2.1kAutomated safety check: PassMIT

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Questions about Retail Clerk Performance Analysis

What does Retail Clerk Performance Analysis do?

导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据). An agent skill from LeoYeAI/openclaw-master-skills. Retail Clerk Performance Analysis is an agent skill from LeoYeAI/openclaw-master-skills. 导购个人业绩深度分析工具。支持普通门店(POS数据)和AIoT门店(POS+AIoT数据)。 输出导购个人详细诊断报告,包含: 1.

How do I install Retail Clerk Performance Analysis in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a claude-code`. Or copy the skill folder (skills/retail-clerk-performance-analysis in LeoYeAI/openclaw-master-skills) into .claude/skills/retail-clerk-performance-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Retail Clerk Performance Analysis in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a codex`. Or copy the skill folder (skills/retail-clerk-performance-analysis in LeoYeAI/openclaw-master-skills) into .agents/skills/retail-clerk-performance-analysis in your project. Codex loads it when a task matches its description.

Can I use Retail Clerk Performance 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 LeoYeAI/openclaw-master-skills --skill retail-clerk-performance-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/retail-clerk-performance-analysis, .gemini/skills/retail-clerk-performance-analysis, .github/skills/retail-clerk-performance-analysis and .opencode/skills/retail-clerk-performance-analysis in your project.

What does Retail Clerk Performance Analysis need to run?

Going by SKILL.md and its folder, Retail Clerk Performance Analysis needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Retail Clerk Performance 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 Retail Clerk Performance 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 Retail Clerk Performance Analysis use?

Retail Clerk Performance 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 Retail Clerk Performance Analysis use?

About 3.1k tokens (SKILL.md is roughly 12k 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 Retail Clerk Performance Analysis?

Skills that share tags, products or a category with Retail Clerk Performance Analysis: Clerk Auth (davila7/claude-code-templates, 33k stars), Clerk Auth (sickn33/agentic-awesome-skills, 47k stars), Clerk (Anil-matcha/awesome-muse-connectors, 1.3k stars) and Retail Product Search Agent (google/adk-recipes, 10k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Retail Clerk Performance Analysis?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.