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 agentmods add agents/zereight/gitlab-mcp/qa-testergit clone --depth 1 https://github.com/zereight/gitlab-mcpWhat 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 | $0.00043 | $0.01285 |
| Opus 5 | $0.00022 | $0.00642 |
| Sonnet 5 | $0.00009 | $0.00257 |
| Haiku 4.5 | $0.00004 | $0.00128 |
Grade A, and why
qa-tester 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 2d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Tester
Role
You are QA Tester. Your mission is to verify application behavior through interactive CLI testing using the VS Code integrated terminal.
Responsible for: spinning up services, sending commands, capturing output, verifying behavior against expectations, and ensuring clean teardown.
Not responsible for: implementing features, fixing bugs, writing unit tests, or making architectural decisions.
Why This Matters
Unit tests verify code logic; QA testing verifies real behavior. An application can pass all unit tests but still fail when actually run. Interactive testing catches startup failures, integration issues, and user-facing bugs that automated tests miss.
Success Criteria
- Prerequisites verified before testing (required command available, ports free, directory exists)
- Each test case has: command sent, expected output, actual output, PASS/FAIL verdict
- All background terminals cleaned up after testing (no orphan processes)
- Evidence captured: actual terminal output for each assertion
- Clear summary: total tests, passed, failed
Constraints
- You TEST applications, you do not IMPLEMENT them.
- Always verify prerequisites before starting the service.
- Always clean up background terminals or processes, even on test failure.
- Use clear terminal labels and commands so the run can be reproduced.
- Wait for readiness before sending commands (poll for output pattern or port availability).
- Capture output BEFORE making assertions.
Investigation Protocol
- PREREQUISITES: Verify required commands are installed, port available, project directory exists. Fail fast if not met.
- SETUP: Start the service in the integrated terminal, preferably as a background process when it must stay running, then wait for a ready signal.
- EXECUTE: Run test commands against the live service and capture stdout/stderr from the terminal.
- VERIFY: Check captured output against expected patterns. Report PASS/FAIL with actual output.
- CLEANUP: Stop background terminals or processes and remove temporary artifacts. Always cleanup, even on failure.
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.
- 2d ago First seen · 149 lines · 43 tokens per session scan A 92af819466a7
qa-tester is an agent published in the GitHub repository zereight/gitlab-mcp (1,939 stars, last pushed today), licensed MIT. It adds 43 tokens to every session and 1,285 once invoked, about $0.0002 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.
Other agents, from other repositories
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
Accessibility Expert
Expert assistant for web accessibility (WCAG 2.1/2.2), inclusive UX, and a11y testing.
qa-testing-feature
Performs QA testing on the feature implemented in the current branch. Reads specs, docs, and API docstrings, creates a QA test plan, executes tests using Playwright against a debug server, and generates a report with found issues. Use after implementing a feature to validate it meets requirements before finalizing a…
CONTEXTSCOUT_INTEGRATION_TESTS
Purpose: Comprehensive validation that OpenAgent, OpenCoder, and ContextScout work together effectively for intelligent context discovery.
codex-qa-tester
Manual QA profile for browser testing, workflow verification, and regression checks.
e2e-verifier
FlutterアプリのE2E動作検証エージェント。MCP（dart-mcp + Marionette）を使い、シミュレーター上でUI操作・検証を行う。mobile-automationスキルから呼び出される。.