导购对比分析

导购对比分析工具。基于clerk-performance-analysis的扩展，提供导购业绩的多维度对比分析。 核心能力： 1. 时间维度对比（培训前后、活动前后、月度环比） 2. 横向对比（多人排名、标杆学习、差距分析） 3. 高频场景（晨会对比、周会报告、绩效对标） 4. 导购能力雷达图对比 5. 业绩贡献...

Security Scan

The skill's code and instructions are consistent with a retail-sales comparison tool that depends on a local 'clerk-performance-analysis' module; it does not request credentials or install external code itself.

Purpose & Capability

The name/description describe multi-dimensional clerk performance comparisons and the included compare.py implements compare_guide_over_time, compare_guides and compare_with_benchmark which match that purpose. The skill explicitly depends on a local clerk-performance-analysis module and inserts that path; this is coherent with the declared functionality.

Instruction Scope

SKILL.md and compare.py limit actions to calling analyze(...) from clerk-performance-analysis, aggregating metrics, and producing recommendations and prints. Example code references helper functions like send_morning_meeting_report() and generate_performance_table() that are not provided by the skill — these are examples of integration points and imply external push/reporting but are not implemented here. You should review the referenced clerk-performance-analysis 'analyze' implementation to know what data sources it accesses and whether it transmits data externally.

Install Mechanism

No install spec or external downloads are present; the skill is instruction/code-only and does not pull remote archives or add binaries. This is low install risk.

Credentials

The skill declares no required environment variables or credentials. However, examples mention pushing reports to enterprise WeChat (send_morning_meeting_report) which would require external credentials if implemented; those are not requested here — integrations must be supplied separately. Verify any integrating code or the analyze dependency for hidden credential requests.

Persistence & Privilege

always is false and the skill does not request system-wide modifications. It does modify sys.path at runtime to import the local dependency (~/.openclaw/skills/clerk-performance-analysis), which is expected for a dependency but should be noted as reading from the user's skill directory.

Assessment

This skill appears to be what it says: a local comparison/reporting module that relies on a separate clerk-performance-analysis module. Before installing or using it: 1) Inspect the clerk-performance-analysis 'analyze' implementation to confirm where it reads data (databases, APIs, files) and whether it makes network calls or requires credentials. 2) If you plan to auto-push reports (WeChat, email, etc.), implement and secure those integrations yourself — this skill does not include those credentials. 3) Confirm the expected location (~/.openclaw/skills/clerk-performance-analysis) exists and is trustworthy; a malicious or compromised analyze module would drive data access. 4) Run the skill in a test environment first to observe its data access and outputs. If you want, provide the full analyze.py (or the rest of compare.py if truncated) and I can check those for additional concerns.

导购对比分析 Skill

技能名称

clerk-comparison-analysis

功能描述

基于 clerk-performance-analysis 的扩展，提供导购业绩的对比分析功能：

• 时间维度对比：培训前后、活动前后、月度环比等
• 横向对比：多人排名、标杆学习、差距分析
• 高频场景：晨会对比、周会报告、绩效对标

依赖关系

本Skill依赖 clerk-performance-analysis：

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

核心功能

1. 导购自身时间维度对比

函数： compare_guide_over_time()

使用场景：

场景	period_a	period_b	comparison_label
培训效果评估	培训前7天	培训后7天	"新品销售培训前后"
活动效果复盘	活动前7天	活动期间	"315大促活动期间"
月度环比	上月同期	本月	"3月vs2月环比"
波动归因	低谷期	高峰期	"低谷vs高峰对比"

示例：

from compare import compare_guide_over_time

result = compare_guide_over_time(
    store_id="416759_1714379448487",
    guide_name="李翠",
    period_a_from="2026-03-01",
    period_a_to="2026-03-07",
    period_b_from="2026-03-16",
    period_b_to="2026-03-22",
    comparison_label="新品销售培训前后"
)

输出：

{
  "status": "ok",
  "comparison_label": "新品销售培训前后",
  "period_a": {"from": "...", "to": "...", "metrics": {...}},
  "period_b": {"from": "...", "to": "...", "metrics": {...}},
  "changes": {
    "sales": {"before": 28456, "after": 36103, "change": 7647, "change_pct": 26.9, "trend": "up"},
    "orders": {...},
    "atv": {...},
    "new_customers": {...}
  },
  "key_findings": [...],
  "recommendations": [...]
}

2. 多人导购横向对比

函数： compare_guides()

使用场景：

• 晨会快速对比昨日表现
• 周会排名和差距分析
• 月度绩效对标
• 标杆学习和经验分享

示例：

from compare import compare_guides

result = compare_guides(
    store_id="416759_1714379448487",
    guide_names=["李翠", "杨丽", "赵泽瑞", "陈二妹"],
    from_date="2026-03-25",
    to_date="2026-03-25"
)

输出：

{
  "status": "ok",
  "total_guides": 4,
  "rankings": {
    "by_sales": [...],
    "by_new_customers": [...],
    "by_atv": [...]
  },
  "top_performer": {...},
  "bottom_performer": {...},
  "gap_analysis": [...],
  "needs_attention": [...],
  "quick_insights": [...]
}

3. 标杆对比（找差距）

函数： compare_with_benchmark()

使用场景：

• 待提升导购与标杆对比
• 生成个性化改进计划
• 一对一辅导准备

示例：

from compare import compare_with_benchmark

result = compare_with_benchmark(
    store_id="416759_1714379448487",
    guide_name="陈二妹",           # 待提升导购
    benchmark_guide_name="李翠",   # 标杆导购
    from_date="2026-03-01",
    to_date="2026-03-25"
)

输出：

{
  "status": "ok",
  "guide": {"name": "陈二妹", "metrics": {...}},
  "benchmark": {"name": "李翠", "metrics": {...}},
  "gaps": {
    "sales": {"guide_value": 31922, "benchmark_value": 64559, "gap": 32637, "gap_pct": 50.6},
    "new_customers": {...}
  },
  "learning_points": [...],
  "action_plan": [...]
}

高频使用场景

场景1：晨会快速对比（每日）

# 店长每日晨会前自动生成
report = compare_guides(
    store_id="...",
    guide_names=["李翠", "杨丽", "赵泽瑞", "陈二妹"],
    from_date=yesterday,
    to_date=yesterday
)

# 推送到店长企业微信
send_morning_meeting_report(report)

输出示例：

═══════════════════════════════════════════════════════
导购横向对比 - 正义路60号店
2026-03-25（昨日）
═══════════════════════════════════════════════════════

【销售额排名】
#1  活动导购  ¥3,529  (25%)  7单  ¥504  ✅
#2  李翠      ¥3,111  (22%)  4单  ¥778  
#3  杨丽      ¥2,115  (15%)  3单  ¥705  
#4  陈二妹    ¥0      (0%)   0单  ¥0    ⚠️ 无销售

【需要关注】
⚠️ 陈二妹: 昨日无销售，近14天有4天无销售

【今日建议】
1. 店长重点关注陈二妹
2. 李翠分享高客单价经验

场景2：培训效果评估（培训后7天）

result = compare_guide_over_time(
    store_id="...",
    guide_name="李翠",
    period_a_from="2026-03-01",  # 培训前
    period_a_to="2026-03-07",
    period_b_from="2026-03-16",  # 培训后
    period_b_to="2026-03-22",
    comparison_label="新品销售培训前后"
)

if result['changes']['sales']['change_pct'] > 20:
    print("✅ 培训效果显著，销售额提升20%+")
else:
    print("⚠️ 培训效果不明显，需要复盘")

场景3：月度绩效对标（每月底）

# 生成本月完整对比报告
report = compare_guides(
    store_id="...",
    guide_names=all_guides_in_store,
    from_date="2026-03-01",
    to_date="2026-03-31"
)

# 生成绩效考核表
generate_performance_table(report)

场景4：标杆学习（按需）

# 找出差距最大的导购与标杆对比
result = compare_with_benchmark(
    store_id="...",
    guide_name="陈二妹",
    benchmark_guide_name="李翠",
    from_date="...",
    to_date="..."
)

# 生成一对一辅导计划
coaching_plan = result['action_plan']

输出指标说明

时间对比指标

指标	说明	用途
change	绝对变化值	量化改进幅度
change_pct	变化百分比	评估改进比例
trend	变化趋势	up/down/stable

横向对比指标

指标	说明	用途
gap_to_top	与标杆的销售额差距	量化提升空间
gap_pct	差距百分比	评估追赶难度
potential	达到标杆的潜在收益	激励改进

诊断规则

时间对比诊断

发现类型	判断条件	建议
显著提升	销售额提升>30%	固化成功经验
显著下滑	销售额下滑>30%	分析原因并干预
问题改善	发现数量减少	肯定改进效果

横向对比诊断

发现类型	判断条件	建议
业绩集中	TOP3贡献>70%	关注尾部导购
新客差异	最大/最小>3倍	推广标杆经验
高风险	有high severity finding	立即干预

版本

v1.0.0 - 导购对比分析Skill（支持时间对比和横向对比）