dummy-dataset by phuryn/pm-skills
npx skills add https://github.com/phuryn/pm-skills --skill dummy-dataset生成用于测试的真实虚拟数据集,支持可自定义的列、约束条件和输出格式(CSV、JSON、SQL、Python 脚本)。创建可执行脚本或直接数据文件,便于立即使用。
使用场景: 创建测试数据、生成样本数据集、为开发构建真实的模拟数据,或填充测试环境。
参数:
$PRODUCT:产品或系统名称$DATASET_TYPE:数据类型(例如:客户反馈、交易记录、用户资料)$ROWS:要生成的行数(默认值:100)$COLUMNS:要包含的特定列或字段$FORMAT:输出格式(CSV、JSON、SQL、Python 脚本)$CONSTRAINTS:额外的约束条件或业务规则广告位招租
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import csv
import json
from datetime import datetime, timedelta
import random
# Configuration
ROWS = $ROWS
FILENAME = "$DATASET_TYPE.csv"
# Column definitions with realistic value generators
columns = {
"id": "auto-increment",
"name": "first_last_name",
"email": "email",
"created_at": "timestamp",
# Add more columns...
}
def generate_dataset():
"""Generate realistic dummy dataset"""
data = []
for i in range(1, ROWS + 1):
record = {
"id": f"U{i:06d}",
# Generate values based on column definitions
}
data.append(record)
return data
def save_as_csv(data, filename):
"""Save dataset as CSV"""
with open(filename, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=data[0].keys())
writer.writeheader()
writer.writerows(data)
if __name__ == "__main__":
dataset = generate_dataset()
save_as_csv(dataset, FILENAME)
print(f"Generated {len(dataset)} records in {FILENAME}")
数据集类型: 客户反馈
列:
约束条件:
CSV: 扁平表格格式,易于导入电子表格和数据库
JSON: 嵌套结构,适用于 API 和 NoSQL 数据库
SQL: INSERT 语句,可直接在关系型数据库上执行
Python 脚本: 可执行生成器,适用于自定义或大型数据集
每周安装量
234
代码仓库
GitHub 星标数
8.1K
首次出现
2026年3月4日
安全审计
已安装于
codex222
opencode220
github-copilot219
cursor219
gemini-cli219
kimi-cli218
Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script). Creates executable scripts or direct data files for immediate use.
Use when: Creating test data, generating sample datasets, building realistic mock data for development, or populating test environments.
Arguments:
$PRODUCT: The product or system name$DATASET_TYPE: Type of data (e.g., customer feedback, transactions, user profiles)$ROWS: Number of rows to generate (default: 100)$COLUMNS: Specific columns or fields to include$FORMAT: Output format (CSV, JSON, SQL, Python script)$CONSTRAINTS: Additional constraints or business rulesimport csv
import json
from datetime import datetime, timedelta
import random
# Configuration
ROWS = $ROWS
FILENAME = "$DATASET_TYPE.csv"
# Column definitions with realistic value generators
columns = {
"id": "auto-increment",
"name": "first_last_name",
"email": "email",
"created_at": "timestamp",
# Add more columns...
}
def generate_dataset():
"""Generate realistic dummy dataset"""
data = []
for i in range(1, ROWS + 1):
record = {
"id": f"U{i:06d}",
# Generate values based on column definitions
}
data.append(record)
return data
def save_as_csv(data, filename):
"""Save dataset as CSV"""
with open(filename, 'w', newline='') as f:
writer = csv.DictWriter(f, fieldnames=data[0].keys())
writer.writeheader()
writer.writerows(data)
if __name__ == "__main__":
dataset = generate_dataset()
save_as_csv(dataset, FILENAME)
print(f"Generated {len(dataset)} records in {FILENAME}")
Dataset Type: Customer Feedback
Columns:
Constraints:
CSV: Flat tabular format, easy to import into spreadsheets and databases
JSON: Nested structure, ideal for APIs and NoSQL databases
SQL: INSERT statements, directly executable on relational databases
Python Script: Executable generator for custom or large datasets
Weekly Installs
234
Repository
GitHub Stars
8.1K
First Seen
Mar 4, 2026
Security Audits
Gen Agent Trust HubPassSocketPassSnykPass
Installed on
codex222
opencode220
github-copilot219
cursor219
gemini-cli219
kimi-cli218
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