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 handling-sensitive-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/handling-sensitive-test-data)<a href="https://agentmods.dev/skills/jaktestowac/awesome-copilot-for-testers/handling-sensitive-test-data"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/handling-sensitive-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/handling-sensitive-test-data"><img src="https://agentmods.dev/badge/skills/jaktestowac/awesome-copilot-for-testers/handling-sensitive-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 | $0.00090 | $0.02533 |
| Opus 5 | $0.00045 | $0.01267 |
| Sonnet 5 | $0.00018 | $0.00507 |
| Haiku 4.5 | $0.00009 | $0.00253 |
Grade A, and why
handling-sensitive-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 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 — 183 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Handling Sensitive Test Data
Use this skill when the data a test suite touches would matter if it leaked.
Test environments are where personal data goes to be forgotten about. A production dump copied to staging "just for this migration" outlives the migration by years; a HAR fixture recorded from a real session carries a real session token into the repository; a failing test uploads a screenshot of a customer's address to a CI artifact store with public read access. None of these are exotic. All of them are the ordinary result of nobody having decided anything.
This skill is about doing testing work safely. It is not legal advice, and where a regime's specific obligations are in play, the data protection owner decides, not the tester.
When to Use
- a test environment is seeded from a production dump
- fixtures contain real names, emails, phone numbers, or addresses
- HAR files, traces, videos, or screenshots are committed or uploaded to CI
- secrets are needed for local runs and nobody knows where they should live
- a data protection review, audit, or certification is coming
- a test suite is being set up for a product handling health, financial, or identity data
Operating Principles
- Synthetic first. Generated data that satisfies the same constraints is safer than any transformation of real data, and usually easier to reason about.
- Anonymization is a claim that must survive re-identification. Replacing a name while keeping a date of birth, postcode, and purchase history re-identifies most people. Assess the combination, not the field.
- Minimize before you protect. The safest record is the one you did not copy. Take the smallest slice that makes the test work.
- Artifacts leak. Traces, HARs, videos, screenshots, and logs all capture whatever was on screen or on the wire. They need the same treatment as fixtures.
- Secrets never enter the repository, the trace, or the log. Not in a fixture, not in a URL, not in a committed
.env. - Retention is a decision, not a default. Data with no deletion date accumulates until an incident finds it.
What ships with it
4 files 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.
- 9d ago First seen · 183 lines · 90 tokens per session scan A 7587ce2c7e31
handling-sensitive-test-data is a skill published in the GitHub repository jaktestowac/awesome-copilot-for-testers (113 stars, last pushed 13d ago), licensed MIT. It adds 90 tokens to every session and 2,533 once invoked, about $0.0005 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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