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 rules/homenshum/nodebenchai/gemini_qa_loopgit 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 | $0.00000 | $0.00889 |
| Opus 5 | $0.00000 | $0.00445 |
| Sonnet 5 | $0.00000 | $0.00178 |
| Haiku 4.5 | $0.00000 | $0.00089 |
Grade A, and why
gemini_qa_loop 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini QA Loop — Automated UI/UX Quality Gate
Continuous dogfood loop using Gemini 3 Flash vision to score and fix UI/UX issues automatically.
Pipeline Steps (in order)
# 1. Build production bundle
npx vite build
# 2. Ensure preview server is running (port 4173)
npx vite preview --host 127.0.0.1 --port 4173 &
# 3. Capture screenshots via e2e test
BASE_URL=http://127.0.0.1:4173 npx playwright test tests/e2e/full-ui-dogfood.spec.ts --project=chromium --workers=1
# 4. Publish screenshots to public/dogfood/
npm run dogfood:publish
# 5. Record walkthrough video
node scripts/ui/recordDogfoodWalkthrough.mjs --baseURL http://127.0.0.1:4173 --publish static
# 6. Run Gemini QA (sends screenshots + video to Gemini 3 Flash for scoring)
BASE_URL=http://127.0.0.1:4173 node scripts/ui/runDogfoodGeminiQa.mjs
# 7. Read results
# Score + summary printed to stdout
# Full JSON: .tmp/dogfood-gemini-qa/screens-qa.json and video-qa.json
# History: public/dogfood/qa-results.json
Scoring Formula
Score = 100 - (P1_count × 6) - (P2_count × 2) - (P3_count × 1)
- P1: Major polish (low contrast, missing focus state, misleading UI) — 6 pts each
- P2: Minor polish (spacing, inconsistent styling, empty state copy) — 2 pts each
- P3: Nit (alignment, minor label wording) — 1 pt each
Fix Strategy Per Severity
P1 Fixes (highest ROI — each fix recovers 6 points)
- Low contrast text: Check dark mode. Use
dark:text-gray-300minimum. - Missing focus styling: Add
focus-visible:ring-2 focus-visible:ring-blue-500. - Missing visual hierarchy: Add left border accent, font weight differentiation, size stepping.
- Poor empty states: Add icon + descriptive copy + CTA button.
- Misleading labels: Show exact price/data, add tooltips.
P2 Fixes (each recovers 2 points)
- Spacing: Standardize gaps. Date formats: Use
month: 'short'. Icon contrast:dark:bg-indigo-500/25min.
Loop Protocol
while score < target:
1. Read .tmp/dogfood-gemini-qa/screens-qa.json and video-qa.json
2. Fix all P1s first (highest ROI), then easy P2s
3. npx vite build && run e2e + publish + record + Gemini QA
4. Read new score
5. If 3 consecutive rounds without improvement → change strategy
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 · 86 lines · 0 tokens per session scan A 05b86c9342ba
gemini_qa_loop is a cursor rule published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 18d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 889 tokens. 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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