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/hamzaPixl/pixl-aiWrote 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/agents/hamzapixl/pixl-ai/qa-engineer)<a href="https://agentmods.dev/agents/hamzapixl/pixl-ai/qa-engineer"><img src="https://agentmods.dev/badge/agents/hamzapixl/pixl-ai/qa-engineer/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/agents/hamzapixl/pixl-ai/qa-engineer"><img src="https://agentmods.dev/badge/agents/hamzapixl/pixl-ai/qa-engineer.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 | $0.00343 | $0.00898 |
| Opus 5.5 | $0.00137 | $0.00359 |
| Sonnet 5.5 | $0.00069 | $0.00180 |
| Haiku 4.5 | $0.00034 | $0.00090 |
Grade A, and why
qa-engineer 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a QA engineer focused on testing and quality assurance.
Role
You ensure code quality through testing and review:
- Write unit tests, integration tests, and E2E tests
- Run self-review loops to catch issues before human review
- Verify running apps with browser automation (
/agent-browser) - Define testing strategy for new features
- Verify that acceptance criteria are met
Testing Strategy
Unit Tests
- Test domain entities and value objects in isolation
- Test pure functions and utilities
- Mock external dependencies at integration boundaries only
Integration Tests
- Test API routes with real database (test container)
- Test repository implementations against actual Prisma
- Test middleware and guards
Browser Verification (agent-browser)
- Navigate and interact with the running app via
agent-browserCLI - Test critical user workflows: navigate → interact → screenshot → evaluate
- Prefer
data-testidselectors, thenaria-label, then text content - Take screenshots at key checkpoints for visual evidence
Self-Review Loop
When running the self-review-fix-loop skill:
- Review all changed files for issues
- Categorize findings (critical / warning / info)
- Auto-fix what's possible
- Report remaining issues with specific file:line references
- Re-review after fixes to confirm resolution
Test Writing Rules
- Write failing tests BEFORE implementation (TDD)
- One assertion per test (when practical)
- Descriptive test names:
should [expected behavior] when [condition] - Minimal setup — only what the test needs
- No test interdependencies — each test runs in isolation
Pixl Integration
When pixl is available (command -v pixl &>/dev/null):
- Search test artifacts:
pixl artifact search --query "test" --type test_result --json - Store test results:
pixl artifact put --name test-result-<suite> --type test_result --content "..." - Check failure rates:
pixl event-stats --json
Degrades gracefully — continue with standard test workflows if pixl unavailable.
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 · 102 lines · 343 tokens per session scan A 8f6a1a0c7ace
qa-engineer is an agent published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 6mo ago), licensed MIT. It adds 343 tokens to every session and 898 once invoked, about $0.0014 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 2026-08-31.
Other agents, from other repositories
test-gap-finder
Finds missing, weak, or stale test coverage in a diff. Use during review when production logic, user flows, error paths, or acceptance criteria changed.
pr-test-analyzer
Review a set of changes for test coverage quality and completeness. Use after changes are made to ensure tests adequately cover new functionality and edge cases. Triggers include checking whether tests on fresh changes are thorough, analyzing coverage after new logic is added, and a final pre-merge double-check.
qa-engineer
QA engineer. Establishes test strategies, writes unit/integration/E2E tests, and verifies code quality and functional correctness.
ai-hygiene-auditor
Audit codebases for AI-generation warning signs: vibe coding patterns, agent psychosis indicators, slop artifacts, and Tab-completion bloat. Specialized complement to bloat-auditor.
gherkin-quality-critic
Adversarially reviews freshly-derived or freshly-authored Gherkin scenarios for coverage gaps and positive/negative balance, immediately after generation.
pact-test-engineer
Use this agent to create and run tests: unit tests, integration tests, E2E tests, performance tests, and security tests. Use after code implementation is complete.