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 jaktestowac/awesome-copilot-for-testers --skill designing-test-datagit clone --depth 1 https://github.com/jaktestowac/awesome-copilot-for-testersWrote 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/jaktestowac/awesome-copilot-for-testers/designing-test-data)<a href="https://agentmods.dev/skills/jaktestowac/awesome-copilot-for-testers/designing-test-data"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/designing-test-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/jaktestowac/awesome-copilot-for-testers/designing-test-data"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/designing-test-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.00878 |
| Opus 5 | $0.00028 | $0.00439 |
| Sonnet 5 | $0.00011 | $0.00176 |
| Haiku 4.5 | $0.00006 | $0.00088 |
Grade A, and why
designing-test-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.
How it starts
The opening of the file, as written. The whole thing — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Designing Test Data
Use this skill when the team needs better inputs, not just more tests. It helps turn vague "use some sample data" habits into deliberate datasets that exercise happy paths, failures, boundaries, and real workflow states.
When to Use
- prepare test data before manual execution starts
- create stable fixtures for automation work
- design edge-case inputs from field constraints
- generate role-aware or state-aware data combinations
- replace improvised sample values with a reusable data catalog
Data Design Rules
- Derive from constraints, not vibes - lengths, formats, ranges, relationships, and states drive good data.
- Happy path is not enough - pair realistic valid data with deliberate invalid and awkward values.
- Synthetic first - use safe, non-sensitive values unless the user explicitly provides approved test data.
- Dependencies must be visible - if a dataset requires setup, state it clearly.
- Time and state matter - dates, expirations, roles, and lifecycle states often create the real edge cases.
Workflow
Phase 0: Frame the target
Clarify the data goal:
- manual execution support
- automation fixture design
- boundary testing
- negative validation
- role or state combinations
Identify the core entities involved.
Phase 1: Map the constraints
For each entity or field, capture what matters:
- required vs optional
- type and format
- minimum, maximum, and off-by-one limits
- allowed values or enums
- uniqueness or relationship rules
- lifecycle state or permission dependencies
If constraints are not fully known, list assumptions instead of fabricating precision.
Phase 2: Build the data categories
Generate values across these buckets where relevant:
- Typical valid data - realistic and reusable
- Boundary values - min, max, zero, empty, whitespace, off-by-one
- Invalid format data - malformed, wrong type, disallowed characters
- Role and state combinations - guest, user, admin, active, suspended, expired, pending
- Temporal data - past, future, leap day, expiry edge, timezone-sensitive values
- Stress or awkward data - long strings, Unicode, duplicate keys, near-collision values
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 115 lines · 56 tokens per session scan A e212fe17cd10
designing-test-data is a skill published in the GitHub repository jaktestowac/awesome-copilot-for-testers (113 stars, last pushed 14d ago), licensed MIT. It adds 56 tokens to every session and 878 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 2026-08-30.
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