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 skills add sjarmak/coding-agent-workflows --skill browser-qagit clone --depth 1 https://github.com/sjarmak/coding-agent-workflowsWrote 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/skills/sjarmak/coding-agent-workflows/browser-qa)<a href="https://agentmods.dev/skills/sjarmak/coding-agent-workflows/browser-qa"><img src="https://agentmods.dev/badge/skills/sjarmak/coding-agent-workflows/browser-qa/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sjarmak/coding-agent-workflows/browser-qa"><img src="https://agentmods.dev/badge/skills/sjarmak/coding-agent-workflows/browser-qa.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00045 | $0.00697 |
| Opus 5.5 | $0.00018 | $0.00279 |
| Sonnet 5.5 | $0.00009 | $0.00139 |
| Haiku 4.5 | $0.00005 | $0.00070 |
Grade A, and why
browser-qa 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 26d 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Browser QA — Automated Visual Testing & Interaction
When to Use
- After deploying a feature to staging/preview
- When you need to verify UI behavior across pages
- Before shipping — confirm layouts, forms, interactions actually work
- When reviewing PRs that touch frontend code
- Accessibility audits and responsive testing
How It Works
Use an available browser automation tool and record the browser, viewport,
revision, data state, and environment. Choose critical journeys by risk.
Use semantic locators and condition-based waits; apply e2e-testing when
adding durable regression coverage.
Phase 1: Smoke Test
1. Navigate to target URL
2. Inspect console errors and classify product, environment, and third-party failures
3. Inspect failed requests and distinguish expected error scenarios from defects
4. Screenshot above-the-fold on desktop + mobile viewport
5. Measure available performance signals against the project budgets; state lab-measurement limits
Phase 2: Interaction Test
1. Click every nav link — verify no dead links
2. Submit forms with valid data — verify success state
3. Submit forms with invalid data — verify error state
4. Test auth flow: login → protected page → logout
5. Test critical user journeys (checkout, onboarding, search)
Phase 3: Visual Regression
1. Screenshot key pages at representative narrow, medium, and wide viewports
2. Compare against baseline screenshots (if stored)
3. Investigate layout shifts, missing elements, and overflow against intended behavior
4. Check dark mode if applicable
Phase 4: Accessibility
1. Run axe-core or equivalent on each page
2. Flag WCAG AA violations (contrast, labels, focus order)
3. Verify keyboard navigation works end-to-end
4. Check screen reader landmarks
Output Format
## QA Report — [URL] — [timestamp]
### Smoke Test
- Console errors: 0 critical, 2 warnings (analytics noise)
- Network: all 200/304, no failures
- Core Web Vitals: LCP 1.2s ✓, CLS 0.02 ✓, INP 89ms ✓
### Interactions
- [✓] Nav links: 12/12 working
- [✗] Contact form: missing error state for invalid email
- [✓] Auth flow: login/logout working
### Visual
- [✗] Hero section overflows on 375px viewport
- [✓] Dark mode: all pages consistent
### Accessibility
- 2 AA violations: missing alt text on hero image, low contrast on footer links
### Verdict: SHIP WITH FIXES (2 issues, 0 blockers)
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.
- 26d ago First seen · 89 lines · 45 tokens per session scan A 68fd603f069f
browser-qa is a skill published in the GitHub repository sjarmak/coding-agent-workflows (7 stars, last pushed 3d ago), licensed MIT. It adds 45 tokens to every session and 697 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-10-02.
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