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.
git clone --depth 1 https://github.com/pnakhat/qa-ai-repoWrote 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/plugins/pnakhat/qa-ai-repo/qa-strategy)<a href="https://agentmods.dev/plugins/pnakhat/qa-ai-repo/qa-strategy"><img src="https://agentmods.dev/badge/plugins/pnakhat/qa-ai-repo/qa-strategy/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/plugins/pnakhat/qa-ai-repo/qa-strategy"><img src="https://agentmods.dev/badge/plugins/pnakhat/qa-ai-repo/qa-strategy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>Grade A, and why
qa-strategy 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.
What it actually says
{
"name": "qa-strategy",
"displayName": "QA Strategy",
"version": "0.10.0",
"description": "Pre-fill what the codebase shows, then ask only the decision-relevant intake questions (product, stack, team size, release cadence) as multiple-choice option sets, then generate a tailored, risk-based QA strategy with an automation plan, quality gates, tooling, and a phased rollout. Scores features by likelihood × impact to focus coverage where it matters, and defines metrics (escape rate, DORA lead time / change fail rate / recovery time, flake rate, mutation score) with precise formulas and enforcing CI gates. Enforces guardrails against invented inputs, big-bang rewrites, one-size-fits-all strategies, testing theater, and vanity metrics with no gate — every recommendation must trace to a stated intake input and land as a measurable gate.",
"author": {
"name": "pnakhat"
},
"homepage": "https://github.com/pnakhat/qa-ai-repo/tree/main/qa-strategy",
"repository": "https://github.com/pnakhat/qa-ai-repo",
"license": "MIT",
"keywords": [
"qa",
"testing",
"qa-strategy"
]
}
What it installs
The manifest is a name and a version. 1 skill, 1 agent travel with it, and installing the plugin installs all of them — 237 tokens a session between them. Each is measured on its own page, and each can be installed alone.
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 Changed cff0680544d6
- 11d ago First seen · 18 lines scan A 93ed7265170b
qa-strategy is a plugin published in the GitHub repository pnakhat/qa-ai-repo (2 stars, last pushed 10d ago), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 2026-10-06.
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