mirror of
https://github.com/phuryn/pm-skills.git
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Pawel Huryn
83 lines
2.7 KiB
Markdown
83 lines
2.7 KiB
Markdown
---
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description: Generate realistic dummy datasets for testing — CSV, JSON, SQL inserts, or Python scripts
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argument-hint: "<description of the data you need>"
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---
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# /generate-data -- Test Data Generator
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Create realistic dummy datasets for development, testing, demos, or prototyping. Outputs as ready-to-use files in your preferred format.
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## Invocation
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```
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/generate-data 1000 users with names, emails, plan tier, signup date, and activity score
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/generate-data E-commerce orders dataset: products, customers, timestamps, amounts
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/generate-data Sample data matching this schema: [paste table definition]
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```
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## Workflow
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### Step 1: Define the Dataset
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Understand:
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- What entities? (users, orders, events, products, etc.)
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- What columns? (with data types and constraints)
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- How many rows?
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- Any relationships between tables?
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- Any specific distributions? (e.g., "80% should be on the free plan")
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- Any realistic constraints? (emails should be unique, dates should be chronological)
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### Step 2: Generate the Data
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Apply the **dummy-dataset** skill:
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- Create a Python script that generates the dataset
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- Use realistic-looking data (not random strings): proper names, valid email formats, real-seeming dates
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- Respect constraints: unique IDs, foreign key relationships, chronological ordering
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- Apply specified distributions
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- Execute the script and produce the output file
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### Step 3: Deliver
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Output in the requested format (or ask):
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- **CSV**: Most common, works everywhere
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- **JSON**: For API testing or frontend development
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- **SQL INSERT**: For populating test databases
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- **Python script**: For reproducible generation (user can tweak and re-run)
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```
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## Generated Dataset: [Description]
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**Rows**: [count]
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**Columns**: [list]
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**Format**: [CSV / JSON / SQL / Python]
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### Schema
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| Column | Type | Constraints | Distribution |
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|--------|------|-----------|-------------|
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### Sample (first 5 rows)
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[Preview of the data]
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### Files
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- [data file]
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- [generator script, if applicable]
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```
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Save data file and generator script to the user's workspace.
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### Step 4: Offer Follow-ups
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- "Want me to **add more columns** or **increase the dataset size**?"
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- "Should I **create related tables** (e.g., orders for these users)?"
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- "Want me to **write test scenarios** that use this data?"
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- "Should I **create SQL queries** to analyze this dataset?"
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## Notes
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- Always provide the generator script so the user can regenerate with different parameters
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- For demo datasets, make the data tell a story (e.g., seasonal trends, a retention problem, a power user segment)
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- Respect realistic cardinality: 1000 users don't have 1000 unique cities
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- For financial data, use realistic price distributions — not uniform random
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- Never include real personal data — all names, emails, and identifiers must be fake
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