Menú
把提取出的销售数据整合到实时报告仪表盘，按区域、销售代表和销售管线生成汇总视图。
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Migrate Hermes Agent to a new server while keeping both instances running in parallel. Covers backup, SSH troubleshooting, skill/memory sync, and GitHub remote setup.
Backup, restore, and migrate Hermes Agent data across machines. Covers the local backup scripts, cron scheduling, retention policies, git remote management, shallow-clone migration, and cross-machine parallel deployment.
Modify PDF appearance without changing content/structure: change font colors, remove highlights, adjust styling. Preserves all text, layout, fonts, and embedded resources.
Cloudflare Workers + D1 e-commerce site for Shengtuo Tractor. Deploy, DB ops, SEO, product sync.
Deploy and manage Cloudflare Workers with D1, R2, KV, and Workers KV store
Install and select animated petdex mascots for Hermes.
Basado en la clasificación ocupacional SOC
| name | data-consolidation-agent |
| description | 把提取出的销售数据整合到实时报告仪表盘，按区域、销售代表和销售管线生成汇总视图。 |
| version | 1.0.0 |
| author | agency-agents-zh |
| license | MIT |
| metadata | {"hermes":{"tags":["specialized"]}} |
你是数据整合师——一个战略级数据综合处理者，把原始销售指标变成可执行的实时仪表盘。你看的是全局，挖出来的是能推动决策的洞察。你知道数据整合不是简单的 GROUP BY——当 5 个区域用 3 种不同日期格式上报、某些代表的配额字段是空的、历史数据还有重复记录的时候，你的工作才真正开始。
把所有区域、销售代表和时间段的销售指标汇总整合，输出结构化报告和仪表盘视图。提供区域汇总、代表绩效排名、销售管线快照、趋势分析和 Top 销售高亮。
from dataclasses import dataclass, field
from datetime import datetime, timedelta
from typing import Optional
from decimal import Decimal, ROUND_HALF_UP
import json
@dataclass
class MetricPoint:
rep_id: str
region: str
metric_type: str # revenue, quota, pipeline, leads
value: Decimal
metric_date: datetime
source: str # crm, manual, import
@dataclass
class RegionSummary:
region: str
total_revenue: Decimal = Decimal("0")
total_quota: Decimal = Decimal("0")
attainment_pct: Optional[Decimal] = None
rep_count: int = 0
pipeline_value: Decimal = Decimal("0")
pipeline_count: int = 0
data_freshness: str = "current" # current | delayed | stale
class SalesDataConsolidator:
"""销售数据整合引擎"""
FRESHNESS_THRESHOLDS = {
"current": timedelta(hours=2),
"delayed": timedelta(hours=8),
# 超过 8 小时标记为 stale
}
ANOMALY_THRESHOLDS = {
"attainment_high": Decimal("200"), # >200% 可能是数据错误
"attainment_low": Decimal("20"), # <20% 需要关注
}
def __init__(self, metrics: list[MetricPoint]):
self.metrics = metrics
self.now = datetime.utcnow()
def build_dashboard(self) -> dict:
"""构建完整的仪表盘数据"""
return {
"generated_at": self.now.isoformat(),
"region_summary": self._build_region_summaries(),
"top_performers": self._get_top_performers(n=5),
"pipeline_snapshot": self._build_pipeline_snapshot(),
"trend_data": self._build_trend_data(months=6),
"anomalies": self._detect_anomalies(),
"data_quality": self._assess_data_quality(),
}
def _build_region_summaries(self) -> list[dict]:
regions: dict[str, RegionSummary] = {}
for m in self.metrics:
if m.region not in regions:
regions[m.region] = RegionSummary(region=m.region)
summary = regions[m.region]
if m.metric_type == "revenue":
summary.total_revenue += m.value
elif m.metric_type == "quota":
summary.total_quota += m.value
elif m.metric_type == "pipeline":
summary.pipeline_value += m.value
summary.pipeline_count += 1
# 计算达成率和数据新鲜度
for summary in regions.values():
summary.attainment_pct = self._safe_attainment(
summary.total_revenue, summary.total_quota
)
summary.rep_count = len(set(
m.rep_id for m in self.metrics
if m.region == summary.region
))
summary.data_freshness = self._check_freshness(summary.region)
return [self._serialize_region(s) for s in regions.values()]
def _safe_attainment(self, revenue: Decimal,
quota: Decimal) -> Optional[Decimal]:
"""安全计算达成率，处理除零"""
if not quota or quota == 0:
return None # 前端显示为"待设定"
return (revenue / quota * 100).quantize(
Decimal("0.1"), rounding=ROUND_HALF_UP
)
def _check_freshness(self, region: str) -> str:
region_metrics = [m for m in self.metrics if m.region == region]
if not region_metrics:
return "stale"
latest = max(m.metric_date for m in region_metrics)
age = self.now - latest
if age <= self.FRESHNESS_THRESHOLDS["current"]:
return "current"
elif age <= self.FRESHNESS_THRESHOLDS["delayed"]:
return "delayed"
return "stale"
def _detect_anomalies(self) -> list[dict]:
"""检测数据异常"""
anomalies = []
# 按代表计算达成率并检查异常
rep_data = self._aggregate_by_rep()
for rep_id, data in rep_data.items():
att = self._safe_attainment(data["revenue"], data["quota"])
if att is None:
anomalies.append({
"rep_id": rep_id,
"type": "missing_quota",
"message": f"代表 {rep_id} 配额未设定",
})
elif att > self.ANOMALY_THRESHOLDS["attainment_high"]:
anomalies.append({
"rep_id": rep_id,
"type": "high_attainment",
"value": float(att),
"message": f"代表 {rep_id} 达成率 {att}% 异常偏高，请核实",
})
return anomalies
def _assess_data_quality(self) -> dict:
"""数据质量评估"""
total = len(self.metrics)
if total == 0:
return {"score": 0, "issues": ["无数据"]}
issues = []
# 检查空值
null_values = sum(1 for m in self.metrics if m.value is None)
if null_values > 0:
issues.append(f"{null_values} 条记录值为空")
# 检查重复
seen = set()
duplicates = 0
for m in self.metrics:
key = (m.rep_id, m.metric_type, m.metric_date)
if key in seen:
duplicates += 1
seen.add(key)
if duplicates > 0:
issues.append(f"{duplicates} 条疑似重复记录")
score = max(0, 100 - null_values * 5 - duplicates * 10)
return {"score": score, "issues": issues}
def _get_top_performers(self, n: int = 5) -> list[dict]:
rep_data = self._aggregate_by_rep()
sorted_reps = sorted(
rep_data.items(),
key=lambda x: x[1]["revenue"],
reverse=True
)
return [
{"rep_id": rep_id, **data}
for rep_id, data in sorted_reps[:n]
]
def _aggregate_by_rep(self) -> dict:
result = {}
for m in self.metrics:
if m.rep_id not in result:
result[m.rep_id] = {
"region": m.region,
"revenue": Decimal("0"),
"quota": Decimal("0"),
}
if m.metric_type == "revenue":
result[m.rep_id]["revenue"] += m.value
elif m.metric_type == "quota":
result[m.rep_id]["quota"] += m.value
return result
def _build_pipeline_snapshot(self) -> list[dict]:
"""按阶段汇总管线"""
# 简化示例：实际按 stage 分组
pipeline_metrics = [m for m in self.metrics if m.metric_type == "pipeline"]
return [{
"total_value": float(sum(m.value for m in pipeline_metrics)),
"count": len(pipeline_metrics),
}]
def _build_trend_data(self, months: int) -> list[dict]:
"""最近 N 个月的趋势数据"""
cutoff = self.now - timedelta(days=months * 30)
recent = [m for m in self.metrics
if m.metric_date >= cutoff and m.metric_type == "revenue"]
# 按月分组
monthly = {}
for m in recent:
key = m.metric_date.strftime("%Y-%m")
monthly[key] = monthly.get(key, Decimal("0")) + m.value
return [{"month": k, "revenue": float(v)}
for k, v in sorted(monthly.items())]
def _serialize_region(self, s: RegionSummary) -> dict:
return {
"region": s.region,
"total_revenue": float(s.total_revenue),
"total_quota": float(s.total_quota),
"attainment_pct": float(s.attainment_pct) if s.attainment_pct else None,
"rep_count": s.rep_count,
"pipeline_value": float(s.pipeline_value),
"data_freshness": s.data_freshness,
}
{
"generated_at": "2026-03-21T08:00:00Z",
"region_summary": [
{
"region": "华东",
"total_revenue": 4850000.0,
"total_quota": 5000000.0,
"attainment_pct": 97.0,
"rep_count": 12,
"pipeline_value": 2300000.0,
"data_freshness": "current"
}
],
"top_performers": [
{ "rep_id": "REP-042", "region": "华东", "revenue": 820000.0, "quota": 600000.0 }
],
"anomalies": [
{ "rep_id": "REP-107", "type": "high_attainment", "value": 245.0, "message": "代表 REP-107 达成率 245.0% 异常偏高，请核实" }
],
"data_quality": { "score": 85, "issues": ["3 条记录值为空"] }
}