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.
npx skills add vaquarkhan/data-engineering-agent-skills --skill test-data-preparation-and-synthetic-datagit clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-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/skills/vaquarkhan/data-engineering-agent-skills/test-data-preparation-and-synthetic-data)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/test-data-preparation-and-synthetic-data"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/test-data-preparation-and-synthetic-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/skills/vaquarkhan/data-engineering-agent-skills/test-data-preparation-and-synthetic-data"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/test-data-preparation-and-synthetic-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 | $0.00051 | $0.00613 |
| Opus 5 | $0.00026 | $0.00307 |
| Sonnet 5 | $0.00010 | $0.00123 |
| Haiku 4.5 | $0.00005 | $0.00061 |
Grade A, and why
test-data-preparation-and-synthetic-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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Test Data Preparation And Synthetic Data
Overview
Use this skill when data work needs realistic test inputs without depending on unsafe copies of production. It helps agents decide when to use masked subsets, synthetic data, seeded fixtures, or contract-shaped test datasets.
When to Use
- building lower-environment validation datasets
- preparing integration-test or QA data
- creating representative demo or training datasets
- generating synthetic data that preserves shape and edge cases
- defining seeded fixtures for pipelines, dbt, or Spark jobs
Do not assume production copies are the default answer for testing.
Workflow
-
Define the testing objective. Clarify whether the data is needed for:
- contract validation
- business-logic testing
- performance rehearsal
- UI or dashboard testing
- demo or training use
-
Choose the right test-data source. Decide between:
- synthetic data generated from contracts
- masked or tokenized production subsets
- hand-authored fixtures for narrow edge cases
- sampled lower-environment copies with strict controls
-
Preserve the behaviors that matter. Include:
- edge cases
- null patterns
- cardinality and skew
- late or duplicate events
- partition or date-range coverage
-
Remove unsafe dependencies on production. Make sure:
- identifiers are masked or replaced
- sensitive values are not recoverable
- secrets and direct production connections are not needed to regenerate the test set
-
Version and document the dataset. Record:
- generation method
- intended use
- refresh cadence
- limitations versus production behavior
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We need real data or the tests are useless." | Many test goals are satisfied by synthetic or masked data when the right shape and edge cases are preserved. |
| "A quick production copy is faster." | Unsafe lower-environment copies often become long-lived risk surfaces. |
| "The happy path sample is enough." | Test data that omits skew, nulls, duplicates, or boundary conditions gives false confidence. |
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.
- 9d ago First seen · 82 lines · 51 tokens per session scan A 6e42bfe01dd6
test-data-preparation-and-synthetic-data is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 613 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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