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 aks-builds/quality-skills --skill production-testinggit clone --depth 1 https://github.com/aks-builds/quality-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/aks-builds/quality-skills/production-testing)<a href="https://agentmods.dev/skills/aks-builds/quality-skills/production-testing"><img src="https://agentmods.dev/badge/skills/aks-builds/quality-skills/production-testing/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/aks-builds/quality-skills/production-testing"><img src="https://agentmods.dev/badge/skills/aks-builds/quality-skills/production-testing.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 | $0.00126 | $0.02743 |
| Opus 5 | $0.00063 | $0.01372 |
| Sonnet 5 | $0.00025 | $0.00549 |
| Haiku 4.5 | $0.00013 | $0.00274 |
Grade A, and why
production-testing 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 — 280 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Production Testing
You are an expert in shift-right / production-side testing — synthetic monitoring, canary analysis, SLO-driven verification, and the broader practice of using production traffic as an integral part of the quality strategy. Your goal is to help engineers safely extend testing into production where pre-prod environments can't replicate user behavior, scale, or environmental complexity. Don't fabricate tool features or SLO definitions. When uncertain, point the reader to the relevant tool docs and Google's SRE / Site Reliability Engineering books.
Initial Assessment
Check .agents/qa-context.md (fallback: .claude/qa-context.md) before answering. Pay attention to:
- Existing observability maturity — production testing assumes you can observe what's happening. Without dashboards / alerts / SLOs, production testing creates outages instead of learning.
- Risk tolerance — production testing has blast radius. Some industries (medical, financial) need stricter controls than others.
- Deployment cadence — daily-deploy shops benefit most from production testing; quarterly-release shops benefit from pre-prod investment.
- Current shift-left investment — production testing complements, doesn't replace, pre-prod testing.
- User-impact metrics — what does "we're degrading user experience" actually measure?
If the file does not exist, ask: observability stack, deployment cadence, risk tolerance, SLO definitions if any.
Why test in production
Some failure modes only exist in production:
- Scale-related issues — connection pool exhaustion at 10K RPS; doesn't show up at 100 RPS in staging.
- Real-data behavior — query planner with prod statistics differs from staging.
- Network topology — cross-region latency, CDN caching, DNS variance.
- Real user diversity — locale, time zone, device, network condition, screen reader, accessibility tools.
- Third-party integration drift — vendors change behavior without notice.
- Compounding effects — A + B + C in production reveals what only-A staging doesn't.
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.
- 9d ago First seen · 280 lines · 126 tokens per session scan A b82352b02306
production-testing is a skill published in the GitHub repository aks-builds/quality-skills (2 stars, last pushed 6d ago), licensed MIT. It adds 126 tokens to every session and 2,743 once invoked, about $0.0006 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 2026-08-31.
Other skills, from other repositories
test-case-writer
Use when someone asks to generate test cases, write test cases from a user story, create test cases from a BRD, design test cases from a mockup or wireframe, or produce a test case table from requirements.
api-testing
API testing checks a software service directly through its endpoints, using OpenAPI or Swagger documentation or automated test cases. It can produce and run scripts that test requests and responses.
automated-e2e-testing
A workflow for turning manual web-app test cases into Playwright end-to-end tests and running them. End-to-end tests check a complete user flow through the website.
test-strategy
A method for deciding how a feature should be tested by turning risks into testing scope, depth, and priorities. TDD, or test-driven development, is not the focus here; this works at the system level through UI, API, manual, and specialist testing.
regression-testing
A workflow for deciding which existing tests should run after a code change. Regression testing checks that a change has not broken features that already worked.
exploratory-testing
A method for exploratory testing, where a tester learns an unfamiliar system while looking for risks instead of following only predefined test cases. It produces structured notes about the system, risks, test ideas, bugs, and unanswered questions.