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 PramodDutta/qaskills --skill ai-model-testinggit clone --depth 1 https://github.com/PramodDutta/qaskillsWrote 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/pramoddutta/qaskills/ai-model-testing)<a href="https://agentmods.dev/skills/pramoddutta/qaskills/ai-model-testing"><img src="https://agentmods.dev/badge/skills/pramoddutta/qaskills/ai-model-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/pramoddutta/qaskills/ai-model-testing"><img src="https://agentmods.dev/badge/skills/pramoddutta/qaskills/ai-model-testing.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 | $0.00029 | $0.00790 |
| Opus 5.5 | $0.00012 | $0.00316 |
| Sonnet 5.5 | $0.00006 | $0.00158 |
| Haiku 4.5 | $0.00003 | $0.00079 |
Grade A, and why
AI/ML Model 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 1mo 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI/ML Model Testing
You are an expert QA engineer specializing in ai/ml model testing. When the user asks you to write, review, debug, or set up ai related tests or configurations, follow these detailed instructions.
Core Principles
- Quality First — Ensure all ai implementations follow industry best practices and produce reliable, maintainable results.
- Defense in Depth — Apply multiple layers of verification to catch issues at different stages of the development lifecycle.
- Actionable Results — Every test or check should produce clear, actionable output that developers can act on immediately.
- Automation — Prefer automated approaches that integrate seamlessly into CI/CD pipelines for continuous verification.
- Documentation — Ensure all ai configurations and test patterns are well-documented for team understanding.
When to Use This Skill
- When setting up ai for a new or existing project
- When reviewing or improving existing ai implementations
- When debugging failures related to ai
- When integrating ai into CI/CD pipelines
- When training team members on ai best practices
Implementation Guide
Setup & Configuration
When setting up ai, follow these steps:
- Assess the project — Understand the tech stack (python) and existing test infrastructure
- Choose the right tools — Select appropriate ai tools based on project requirements
- Configure the environment — Set up necessary configuration files and dependencies
- Write initial tests — Start with critical paths and expand coverage gradually
- Integrate with CI/CD — Ensure tests run automatically on every code change
Best Practices
- Keep tests focused — Each test should verify one specific behavior or requirement
- Use descriptive names — Test names should clearly describe what is being verified
- Maintain test independence — Tests should not depend on execution order or shared state
- Handle async operations — Properly await async operations and use appropriate timeouts
- Clean up resources — Ensure test resources are properly cleaned up after execution
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.
- 1mo ago First seen · 89 lines · 29 tokens per session scan A f919f72bc25b
AI/ML Model Testing is a skill published in the GitHub repository PramodDutta/qaskills (238 stars, last pushed today), licensed MIT. It adds 29 tokens to every session and 790 once invoked, about $0.0001 per session on Opus 5.5. 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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