2. 基于实体的聚合

按实体（应用、用户、仓库等）分组数据。

示例：每个应用的指标

from aggregate_sales import aggregate_by_entity

sales = [
    {"app": "App A", "revenue": 100, "units": 5},
    {"app": "App A", "revenue": 50, "units": 2},
    {"app": "App B", "revenue": 200, "units": 10}
]

result = aggregate_by_entity(sales, entity_field='app')
# {
#     "App A": {"revenue": 150, "units": 7},
#     "App B": {"revenue": 200, "units": 10}
# }

3. 统计聚合

计算统计信息（总和、平均值、最小值、最大值、百分位数）。

示例：提交统计

from aggregate_commits import calculate_stats

commits = [
    {"author": "John", "lines": 125},
    {"author": "Jane", "lines": 87},
    {"author": "John", "lines": 43}
]

result = calculate_stats(commits, group_by='author', metric='lines')
# {
#     "John": {"sum": 168, "avg": 84, "min": 43, "max": 125, "count": 2},
#     "Jane": {"sum": 87, "avg": 87, "min": 87, "max": 87, "count": 1}
# }

数据源

App Store 销售数据

输入格式（来自 App Store Connect 的 TSV 文件）：

Provider	Provider Country	SKU	Developer	Title	Version	Product Type Identifier	Units	Developer Proceeds	Begin Date	End Date	Customer Currency	Country Code	Currency of Proceeds	Apple Identifier	Customer Price	Promo Code	Parent Identifier	Subscription	Period	Category	CMB	Device	Supported Platforms	Proceeds Reason	Preserved Pricing	Client

聚合输出：

{
  "period": "2026-01-14",
  "apps": {
    "com.example.app": {
      "name": "My App",
      "downloads": 1234,
      "revenue": 567.89,
      "updates": 45,
      "countries": ["US", "CA", "UK"]
    }
  },
  "totals": {
    "total_downloads": 5678,
    "total_revenue": 2345.67,
    "total_apps": 5
  }
}

GitHub 提交记录

输入格式（来自 GitHub API）：

[
  {
    "sha": "abc123",
    "author": {"name": "John Doe", "email": "john@example.com"},
    "commit": {
      "message": "Add feature X",
      "author": {"date": "2026-01-14T10:30:00Z"}
    },
    "stats": {"additions": 125, "deletions": 45}
  }
]

聚合输出：

{
  "period": "week",
  "date_range": "2026-01-07 to 2026-01-14",
  "summary": {
    "total_commits": 45,
    "total_contributors": 5,
    "total_lines": 2345,
    "total_files": 123
  },
  "by_author": {
    "John Doe": {
      "commits": 15,
      "lines_added": 1234,
      "lines_deleted": 456,
      "files_changed": 45
    }
  },
  "by_day": {
    "2026-01-14": {"commits": 8, "lines": 567}
  }
}

Skillz 事件数据

输入格式（来自 Skillz 开发者门户）：

{
  "event_id": "888831",
  "name": "Winter Tournament",
  "status": "active",
  "start_date": "2026-01-10",
  "end_date": "2026-01-20",
  "prize_pool": 1000,
  "entries": 234
}

聚合输出：

{
  "period": "active",
  "summary": {
    "total_events": 8,
    "total_prize_pool": 8000,
    "total_entries": 1234
  },
  "by_status": {
    "active": {"count": 5, "prize_pool": 5000},
    "completed": {"count": 3, "prize_pool": 3000}
  }
}

聚合脚本

aggregate_sales.py

聚合 App Store 销售数据。

用法：

python scripts/aggregate_sales.py \
    --input sales_reports/ \
    --period week \
    --group-by app \
    --output aggregated.json

参数：

• --input：输入目录或文件（TSV/JSON）
• --period：时间段（day, week, month）
• --group-by：分组字段（app, country, category）
• --output：输出 JSON 文件

aggregate_commits.py

聚合 GitHub 提交数据。

用法：

python scripts/aggregate_commits.py \
    --input commits.json \
    --period week \
    --metrics lines,files,commits \
    --output summary.json

参数：

• --input：输入 JSON 文件（提交数组）
• --period：时间段（day, week, month）
• --metrics：要计算的指标（逗号分隔）
• --output：输出 JSON 文件

aggregate_events.py

聚合 Skillz 事件数据。

用法：

python scripts/aggregate_events.py \
    --input events/ \
    --status active,completed \
    --output summary.json

参数：

• --input：包含事件 JSON 文件的输入目录
• --status：按状态筛选（逗号分隔）
• --output：输出 JSON 文件

merge_sources.py

合并来自多个数据源的数据。

用法：

python scripts/merge_sources.py \
    --sources app_store.json github.json skillz.json \
    --strategy combine \
    --output combined.json

参数：

• --sources：JSON 文件列表（空格分隔）
• --strategy：合并策略（combine, average, latest）
• --output：输出 JSON 文件

合并策略：

• combine：合并所有数据（保留所有字段）
• average：对数值字段取平均值
• latest：保留最新值（按时间戳）

数据转换

筛选

from aggregate_sales import filter_data

sales = [...]

# 按国家筛选
us_sales = filter_data(sales, country='US')

# 按日期范围筛选
recent_sales = filter_data(sales, start_date='2026-01-01', end_date='2026-01-14')

# 按值筛选
high_revenue = filter_data(sales, min_revenue=100)

分组

from aggregate_commits import group_data

commits = [...]

# 按作者分组
by_author = group_data(commits, group_by='author')

# 按仓库分组
by_repo = group_data(commits, group_by='repository')

# 按日期分组
by_date = group_data(commits, group_by='date', period='day')

排序

from merge_sources import sort_data

data = [...]

# 按收入排序（降序）
sorted_data = sort_data(data, field='revenue', reverse=True)

# 按日期排序（升序）
sorted_data = sort_data(data, field='date')

与智能体的集成

报告智能体

# 聚合 App Store 销售数据
from aggregate_sales import aggregate_sales

sales_data = appstore_client.get_sales_report(days=7)
aggregated = aggregate_sales(sales_data, period='day', group_by='app')

# 用于报告
html = render_template('appstore-metrics', aggregated)

自动化智能体

# 聚合 GitHub 提交记录
from aggregate_commits import aggregate_commits

commits = github_client.get_commits(repo='owner/repo', days=7)
summary = aggregate_commits(commits, period='week')

# 如果活动量高，则创建 ClickUp 任务
if summary['total_commits'] > 50:
    clickup_client.create_task(
        title='High GitHub Activity',
        description=f"Total commits: {summary['total_commits']}"
    )

示例

查看 examples/ 目录：

• sample_sales_aggregation.json - App Store 销售示例
• sample_commit_aggregation.json - GitHub 提交示例
• sample_multi_source_merge.json - 多源合并示例

每周安装次数

1

仓库

dglowacki/mega-agent-v2

首次发现

1 天前

安全审计

Gen Agent Trust Hub通过Socket失败Snyk通过

安装于

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amp1

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openclaw1

opencode1

cursor1

2. Entity-Based Aggregation

Group data by entities (apps, users, repos, etc.).

Example: Per-app metrics

from aggregate_sales import aggregate_by_entity

sales = [
    {"app": "App A", "revenue": 100, "units": 5},
    {"app": "App A", "revenue": 50, "units": 2},
    {"app": "App B", "revenue": 200, "units": 10}
]

result = aggregate_by_entity(sales, entity_field='app')
# {
#     "App A": {"revenue": 150, "units": 7},
#     "App B": {"revenue": 200, "units": 10}
# }

3. Statistical Aggregation

Calculate statistics (sum, avg, min, max, percentiles).

Example: Commit statistics

from aggregate_commits import calculate_stats

commits = [
    {"author": "John", "lines": 125},
    {"author": "Jane", "lines": 87},
    {"author": "John", "lines": 43}
]

result = calculate_stats(commits, group_by='author', metric='lines')
# {
#     "John": {"sum": 168, "avg": 84, "min": 43, "max": 125, "count": 2},
#     "Jane": {"sum": 87, "avg": 87, "min": 87, "max": 87, "count": 1}
# }

Data Sources

App Store Sales

Input format (TSV from App Store Connect):

Provider	Provider Country	SKU	Developer	Title	Version	Product Type Identifier	Units	Developer Proceeds	Begin Date	End Date	Customer Currency	Country Code	Currency of Proceeds	Apple Identifier	Customer Price	Promo Code	Parent Identifier	Subscription	Period	Category	CMB	Device	Supported Platforms	Proceeds Reason	Preserved Pricing	Client

Aggregated output:

{
  "period": "2026-01-14",
  "apps": {
    "com.example.app": {
      "name": "My App",
      "downloads": 1234,
      "revenue": 567.89,
      "updates": 45,
      "countries": ["US", "CA", "UK"]
    }
  },
  "totals": {
    "total_downloads": 5678,
    "total_revenue": 2345.67,
    "total_apps": 5
  }
}

GitHub Commits

Input format (from GitHub API):

[
  {
    "sha": "abc123",
    "author": {"name": "John Doe", "email": "john@example.com"},
    "commit": {
      "message": "Add feature X",
      "author": {"date": "2026-01-14T10:30:00Z"}
    },
    "stats": {"additions": 125, "deletions": 45}
  }
]

Aggregated output:

{
  "period": "week",
  "date_range": "2026-01-07 to 2026-01-14",
  "summary": {
    "total_commits": 45,
    "total_contributors": 5,
    "total_lines": 2345,
    "total_files": 123
  },
  "by_author": {
    "John Doe": {
      "commits": 15,
      "lines_added": 1234,
      "lines_deleted": 456,
      "files_changed": 45
    }
  },
  "by_day": {
    "2026-01-14": {"commits": 8, "lines": 567}
  }
}

Skillz Events

Input format (from Skillz Developer Portal):

{
  "event_id": "888831",
  "name": "Winter Tournament",
  "status": "active",
  "start_date": "2026-01-10",
  "end_date": "2026-01-20",
  "prize_pool": 1000,
  "entries": 234
}

Aggregated output:

{
  "period": "active",
  "summary": {
    "total_events": 8,
    "total_prize_pool": 8000,
    "total_entries": 1234
  },
  "by_status": {
    "active": {"count": 5, "prize_pool": 5000},
    "completed": {"count": 3, "prize_pool": 3000}
  }
}

Aggregation Scripts

aggregate_sales.py

Aggregate App Store sales data.

Usage:

python scripts/aggregate_sales.py \
    --input sales_reports/ \
    --period week \
    --group-by app \
    --output aggregated.json

Arguments:

• --input: Input directory or file (TSV/JSON)
• --period: Time period (day, week, month)
• --group-by: Grouping field (app, country, category)
• --output: Output JSON file

aggregate_commits.py

Aggregate GitHub commit data.

Usage:

python scripts/aggregate_commits.py \
    --input commits.json \
    --period week \
    --metrics lines,files,commits \
    --output summary.json

Arguments:

• --input: Input JSON file (commits array)
• --period: Time period (day, week, month)
• --metrics: Metrics to calculate (comma-separated)
• --output: Output JSON file

aggregate_events.py

Aggregate Skillz event data.

Usage:

python scripts/aggregate_events.py \
    --input events/ \
    --status active,completed \
    --output summary.json

Arguments:

• --input: Input directory with event JSON files
• --status: Filter by status (comma-separated)
• --output: Output JSON file

merge_sources.py

Merge data from multiple sources.

Usage:

python scripts/merge_sources.py \
    --sources app_store.json github.json skillz.json \
    --strategy combine \
    --output combined.json

Arguments:

• --sources: Space-separated list of JSON files
• --strategy: Merge strategy (combine, average, latest)
• --output: Output JSON file

Merge strategies:

• combine: Combine all data (keep all fields)
• average: Average numeric fields
• latest: Keep latest values (by timestamp)

Data Transformations

Filtering

from aggregate_sales import filter_data

sales = [...]

# Filter by country
us_sales = filter_data(sales, country='US')

# Filter by date range
recent_sales = filter_data(sales, start_date='2026-01-01', end_date='2026-01-14')

# Filter by value
high_revenue = filter_data(sales, min_revenue=100)

Grouping

from aggregate_commits import group_data

commits = [...]

# Group by author
by_author = group_data(commits, group_by='author')

# Group by repository
by_repo = group_data(commits, group_by='repository')

# Group by date
by_date = group_data(commits, group_by='date', period='day')

Sorting

from merge_sources import sort_data

data = [...]

# Sort by revenue (descending)
sorted_data = sort_data(data, field='revenue', reverse=True)

# Sort by date (ascending)
sorted_data = sort_data(data, field='date')

Integration with Agents

Reporting Agent

# Aggregate App Store sales
from aggregate_sales import aggregate_sales

sales_data = appstore_client.get_sales_report(days=7)
aggregated = aggregate_sales(sales_data, period='day', group_by='app')

# Use for report
html = render_template('appstore-metrics', aggregated)

Automation Agent

# Aggregate GitHub commits
from aggregate_commits import aggregate_commits

commits = github_client.get_commits(repo='owner/repo', days=7)
summary = aggregate_commits(commits, period='week')

# Create ClickUp task if high activity
if summary['total_commits'] > 50:
    clickup_client.create_task(
        title='High GitHub Activity',
        description=f"Total commits: {summary['total_commits']}"
    )

Examples

See examples/ directory for:

• sample_sales_aggregation.json - App Store sales example
• sample_commit_aggregation.json - GitHub commits example
• sample_multi_source_merge.json - Multi-source merge example

Weekly Installs

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Repository

dglowacki/mega-agent-v2

First Seen

1 day ago

Security Audits

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Installed on

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amp1

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