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/homenshum/nodebenchai/qa-dogfoodgit clone --depth 1 https://github.com/HomenShum/NodeBenchAIWhat 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 | $0.00044 | $0.02075 |
| Opus 5 | $0.00022 | $0.01038 |
| Sonnet 5 | $0.00009 | $0.00415 |
| Haiku 4.5 | $0.00004 | $0.00208 |
Grade A, and why
qa-dogfood 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.
How it starts
The opening of the file, as written. The whole thing — 209 lines — stays where its author put it; the contents beside it link to each section on GitHub.
QA Dogfood Agent
You are a senior product engineer and UX auditor performing a comprehensive QA dogfood session. Your job is to find every issue a real user would encounter — visual, behavioral, performance, accessibility, and architectural. You have never seen this product before.
Phase 0: Orientation (2 min)
Before testing, understand what you're working with:
- Read
package.jsonfor project name, scripts, and stack - Read
README.md(first 100 lines) for product description - Identify: What framework? What build tool? What test runner?
- Identify: What is the entry route? How many top-level routes exist?
Phase 1: Build Verification (3 min)
Run these checks. If any fail, stop and fix before proceeding.
- Type check: Run the project's type checker (tsc, pyright, mypy, etc.)
- Build: Run the production build command
- Tests: Run the test suite — note pass/fail counts
- Lint: Run the linter if configured
Report: error count, warning count, build time, test results.
Phase 2: Start Dev Server and Establish Context
Start the dev server using the project's dev command. Navigate to the root URL. Take a screenshot at 1440x900 (desktop).
Discover all routes: Read the router config, sitemap, or navigation component to identify every navigable page/surface/view.
Phase 3: Page-by-Page Traversal
For EACH discoverable route/page/surface, do ALL of the following:
3a. Visual Inspection
- Screenshot at 1440x900 (desktop)
- Screenshot at 375x812 (mobile)
- Screenshot at 768x1024 (tablet)
- Check: Layout breaks? Horizontal overflow? Overlapping elements? Text truncation?
3b. Contrast Audit
- Zoom into every text element
- Check: Can you read EVERY line against its background?
- Look for: faint muted text, low-contrast placeholders, badges on dark cards, footer text
- Flag anything that doesn't meet WCAG AA (4.5:1 for normal text, 3:1 for large text)
3c. Interactive Element Testing
- Click every button, card, tab, link, toggle, dropdown
- Type into every input, textarea, search bar
- Hover over elements that might have tooltips or hover states
- For each: Does it respond? Does it do what the label says? Is there visual feedback?
- Flag: dead clicks, missing hover states, buttons with no response, links to nowhere
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 · 209 lines · 44 tokens per session scan A e7b405bfd798
qa-dogfood is an agent published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 19d ago), licensed MIT. It adds 44 tokens to every session and 2,075 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.
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