Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/unixcrh/phuryn-pm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/generate-data)<a href="https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/generate-data"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/generate-data/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/generate-data"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/generate-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00632 |
| Opus 5 | $0.00009 | $0.00316 |
| Sonnet 5 | $0.00003 | $0.00126 |
| Haiku 4.5 | $0.00002 | $0.00063 |
Grade A, and why
generate-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to generate-data — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/generate-data -- Test Data Generator
Create realistic dummy datasets for development, testing, demos, or prototyping. Outputs as ready-to-use files in your preferred format.
Invocation
/generate-data 1000 users with names, emails, plan tier, signup date, and activity score
/generate-data E-commerce orders dataset: products, customers, timestamps, amounts
/generate-data Sample data matching this schema: [paste table definition]
Workflow
Step 1: Define the Dataset
Understand:
- What entities? (users, orders, events, products, etc.)
- What columns? (with data types and constraints)
- How many rows?
- Any relationships between tables?
- Any specific distributions? (e.g., "80% should be on the free plan")
- Any realistic constraints? (emails should be unique, dates should be chronological)
Step 2: Generate the Data
Apply the dummy-dataset skill:
- Create a Python script that generates the dataset
- Use realistic-looking data (not random strings): proper names, valid email formats, real-seeming dates
- Respect constraints: unique IDs, foreign key relationships, chronological ordering
- Apply specified distributions
- Execute the script and produce the output file
Step 3: Deliver
Output in the requested format (or ask):
- CSV: Most common, works everywhere
- JSON: For API testing or frontend development
- SQL INSERT: For populating test databases
- Python script: For reproducible generation (user can tweak and re-run)
## Generated Dataset: [Description]
**Rows**: [count]
**Columns**: [list]
**Format**: [CSV / JSON / SQL / Python]
### Schema
| Column | Type | Constraints | Distribution |
|--------|------|-----------|-------------|
### Sample (first 5 rows)
[Preview of the data]
### Files
- [data file]
- [generator script, if applicable]
Save data file and generator script to the user's workspace.
Step 4: Offer Follow-ups
- "Want me to add more columns or increase the dataset size?"
- "Should I create related tables (e.g., orders for these users)?"
- "Want me to write test scenarios that use this data?"
- "Should I create SQL queries to analyze this dataset?"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 83 lines · 17 tokens per session scan A 48c10a51b38f
generate-data is a command published in the GitHub repository unixcrh/phuryn-pm-skills (2 stars, last pushed 6mo ago), licensed MIT. It adds 17 tokens to every session and 632 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to generate-data, differing in 0 lines, and is treated as a copy.
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check-dev
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