القائمة
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/itgoyo/hermes-skills --skill data-consolidation-agentيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
用 browser-harness 抓取币安广场 (Binance Square) 热点话题、高讨论帖子、热搜币种，并生成带可点击跳转链接的 HTML 报告。
Direct browser control via CDP. Use when the user wants to automate, scrape, test, or interact with web pages. Connects to the user's already-running Chrome.
Large-scale GitHub repository discovery and data collection using agent-browser + execute_code loops. Use when building curated lists, awesome-X repos, competitive analysis, or ecosystem maps. Covers multi-keyword search, pagination, deduplication, bulk description fetching, and structured output.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| 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
: Decimal(),
}
():
.metrics = metrics
.now = datetime.utcnow()
() -> :
{
: .now.isoformat(),
: ._build_region_summaries(),
: ._get_top_performers(n=),
: ._build_pipeline_snapshot(),
: ._build_trend_data(months=),
: ._detect_anomalies(),
: ._assess_data_quality(),
}
() -> []:
regions: [, RegionSummary] = {}
m .metrics:
m.region regions:
regions[m.region] = RegionSummary(region=m.region)
summary = regions[m.region]
m.metric_type == :
summary.total_revenue += m.value
m.metric_type == :
summary.total_quota += m.value
m.metric_type == :
summary.pipeline_value += m.value
summary.pipeline_count +=
summary regions.values():
summary.attainment_pct = ._safe_attainment(
summary.total_revenue, summary.total_quota
)
summary.rep_count = ((
m.rep_id m .metrics
m.region == summary.region
))
summary.data_freshness = ._check_freshness(summary.region)
[._serialize_region(s) s regions.values()]
() -> [Decimal]:
quota quota == :
(revenue / quota * ).quantize(
Decimal(), rounding=ROUND_HALF_UP
)
() -> :
region_metrics = [m m .metrics m.region == region]
region_metrics:
latest = (m.metric_date m region_metrics)
age = .now - latest
age <= .FRESHNESS_THRESHOLDS[]:
age <= .FRESHNESS_THRESHOLDS[]:
() -> []:
anomalies = []
rep_data = ._aggregate_by_rep()
rep_id, data rep_data.items():
att = ._safe_attainment(data[], data[])
att :
anomalies.append({
: rep_id,
: ,
: ,
})
att > .ANOMALY_THRESHOLDS[]:
anomalies.append({
: rep_id,
: ,
: (att),
: ,
})
anomalies
() -> :
total = (.metrics)
total == :
{: , : []}
issues = []
null_values = ( m .metrics m.value )
null_values > :
issues.append()
seen = ()
duplicates =
m .metrics:
key = (m.rep_id, m.metric_type, m.metric_date)
key seen:
duplicates +=
seen.add(key)
duplicates > :
issues.append()
score = (, - null_values * - duplicates * )
{: score, : issues}
() -> []:
rep_data = ._aggregate_by_rep()
sorted_reps = (
rep_data.items(),
key= x: x[][],
reverse=
)
[
{: rep_id, **data}
rep_id, data sorted_reps[:n]
]
() -> :
result = {}
m .metrics:
m.rep_id result:
result[m.rep_id] = {
: m.region,
: Decimal(),
: Decimal(),
}
m.metric_type == :
result[m.rep_id][] += m.value
m.metric_type == :
result[m.rep_id][] += m.value
result
() -> []:
pipeline_metrics = [m m .metrics m.metric_type == ]
[{
: ((m.value m pipeline_metrics)),
: (pipeline_metrics),
}]
() -> []:
cutoff = .now - timedelta(days=months * )
recent = [m m .metrics
m.metric_date >= cutoff m.metric_type == ]
monthly = {}
m recent:
key = m.metric_date.strftime()
monthly[key] = monthly.get(key, Decimal()) + m.value
[{: k, : (v)}
k, v (monthly.items())]
() -> :
{
: s.region,
: (s.total_revenue),
: (s.total_quota),
: (s.attainment_pct) s.attainment_pct ,
: s.rep_count,
: (s.pipeline_value),
: 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