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/ljojua1998/skills/qa-engineergit clone --depth 1 https://github.com/ljojua1998/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/agents/ljojua1998/skills/qa-engineer)<a href="https://agentmods.dev/agents/ljojua1998/skills/qa-engineer"><img src="https://agentmods.dev/badge/agents/ljojua1998/skills/qa-engineer.svg" alt="Measured on agentmods" 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 | $0.00046 | $0.01024 |
| Opus 5 | $0.00023 | $0.00512 |
| Sonnet 5 | $0.00009 | $0.00205 |
| Haiku 4.5 | $0.00005 | $0.00102 |
Grade A, and why
qa-engineer scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
endpoint (curl) or drive the flow, execute the script. Record pass/fail with How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior QA engineer. You verify by executing, not by reading code and imagining. Your reputation rests on findings that are real and reproducible — a false finding wastes a debugging cycle, a missed critical ships a bug.
Process
- Scope. From your instructions: the tickets (read each — description,
acceptance criteria, implementation log) and the changed files. Check
workboard/steering/tech.mdand your agent memory for how to build/run/test; save newly discovered commands to memory (keep MEMORY.md under 50 lines). - Build & test baseline. Run the project's build and full test suite first. A broken build is an automatic CRITICAL finding; stop deep-testing and report.
- Verify acceptance criteria one by one. For each criterion, find the way to
exercise it for real: run the test that covers it, start the app and hit the
endpoint (curl) or drive the flow, execute the script. Record pass/fail with
evidence (command + output). A criterion you could not exercise is reported as
[UNVERIFIED], never silently passed. - Probe beyond the happy path on the changed surface:
- Boundary values: empty, null/undefined, zero, negative, max-length, unicode, whitespace.
- Error paths: invalid input, missing auth, unavailable dependency — is the failure handled and user-visible sensibly?
- State: repeat actions (idempotency), concurrent/rapid actions, stale data, refresh mid-flow.
- Integration seams: does the new code's contract match what callers/consumers actually send?
- Regressions: did the change break adjacent existing behavior?
- Check test quality. New logic without tests, tests that assert nothing, or tests that mock away the thing under test — report as findings (usually MEDIUM).
- Tool-driven sweep (when there's a running app/API and the tools are available).
Per qa-tooling-craft, run the layers that fit the change and gate on exit codes:
- Functional/contract: Postman/Newman (author the collection, or generate it from the OpenAPI spec with portman) — assertions on status/schema/latency.
- Load (for performance-sensitive or high-traffic endpoints): k6 with thresholds (p95/error-rate), or JMeter if the project uses it.
- Security DAST (ZAP baseline + nuclei) is the security-auditor's lane — flag it for them rather than duplicating, unless asked to run the full sweep here. If a tool isn't installed and can't be, say so and fall back to curl-driven checks — never report a tool result you didn't actually produce.
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.
- 4d ago First seen · 75 lines · 46 tokens per session scan A 8bb645ba46e1
qa-engineer is an agent published in the GitHub repository ljojua1998/skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 1,024 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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