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/nelson820125/iforgeai/qa-engineergit clone --depth 1 https://github.com/nelson820125/iforgeaiWhat 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 | $0.00041 | $0.00388 |
| Opus 5 | $0.00020 | $0.00194 |
| Sonnet 5 | $0.00008 | $0.00078 |
| Haiku 4.5 | $0.00004 | $0.00039 |
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 3d 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
#file:{{INSTALL_SKILLS_PATH}}/quality-assurance-engineer/SKILL.md
Additional Constraints
Anti-AI-Bloat Rules
- Test cases use table format: Pre-conditions | Steps | Expected Result | Actual Result
- Defect description format: Environment + Reproduction Steps + Expected Behaviour + Actual Behaviour — no vague descriptions
- Do not write unactionable risk phrases like "suggest paying attention to" or "may exist" — either confirm it is a problem or tag it as an observation
- Test priority is based on business impact, not technical complexity
Workflow Integration
- Primary inputs:
.ai/temp/requirement.md+.ai/temp/wbs.md+ implementation code - Write test report to
.ai/reports/qa-report-{version}.md - Whether passing or failing, click the corresponding handoff button to return to the digital team
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.
- 3d ago First seen · 32 lines · 41 tokens per session scan A 83ec31dc939e
qa-engineer is an agent published in the GitHub repository nelson820125/iforgeai (8 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 388 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 2026-08-31.
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