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 MigoXLab/webqa-agent --skill button-checkgit clone --depth 1 https://github.com/MigoXLab/webqa-agentWrote 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/migoxlab/webqa-agent/button-check)<a href="https://agentmods.dev/skills/migoxlab/webqa-agent/button-check"><img src="https://agentmods.dev/badge/skills/migoxlab/webqa-agent/button-check/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/migoxlab/webqa-agent/button-check"><img src="https://agentmods.dev/badge/skills/migoxlab/webqa-agent/button-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Memory Poisoning · line 4 Skill attempts to fill the context window with filler content, displacing legitimate instructions and safety constraints. This can degrade agent performance or bypass safety boundaries.Fix: Implement context-window management that detects and rejects padding or stuffing attempts. Prioritize system instructions over user-injected content.
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 | $0.00015 | $0.02185 |
| Opus 5.5 | $0.00006 | $0.00874 |
| Sonnet 5.5 | $0.00003 | $0.00437 |
| Haiku 4.5 | $0.00002 | $0.00218 |
Grade A, and why
button-check 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 1mo 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 — 189 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Button Check Skill
Systematically test every interactive element on the page:
- Clickables (button / link / tab / checkbox / radio / switch / menuitem) — click and verify
- Inputs (textbox / searchbox / combobox / slider) — fill with sample data and verify
After each test, return to the baseline page so the next element is tested from the same starting state.
Important: Before each tool call, output a short one-line description of what you are about to do (e.g. "Filling the search box with 'hello'"). This helps generate readable step-by-step reports.
When to Use
- Task mentions "遍历所有按钮/链接/输入", "comprehensive UI testing", or "regression testing"
- You need to verify no broken links / dead buttons / unresponsive inputs exist
- Smoke testing a page before a release
Skip for single-element tests — use click or fill directly instead.
Phase 1: Collect Interactive Elements
Call take_snapshot to get the accessibility tree. Group elements into two buckets by role:
Clickable bucket — driven by click:
button,link,tabcheckbox,radio,switchmenuitem
Input bucket — driven by fill (or type_text / press_key for keyboard-only widgets):
textbox,searchboxcombobox(treat as click-to-open if no editable affordance is visible, then fill if a textbox appears)slider(fill with a numeric mid-range value)
Each element has a uid (e.g. uid=1_5), a role, and a label. Record {uid, role, label, bucket} for every element.
Cap at 50 elements total. If more exist, prioritize:
- Inputs (form fields) — these often gate downstream behavior
buttonandlink(core interactive)tab,menuitem(navigation)- Other ARIA roles
Snapshot misses some elements? Icon-only buttons without ARIA attributes are invisible to take_snapshot. Mention in the report instead of erroring.
Phase 2: Baseline
Record the baseline state before testing:
- Call
take_screenshotwithfullPage: trueto capture the entire page - Note the current URL — this is the baseline URL you MUST return to after every test that navigates away
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.
- 1mo ago First seen · 189 lines · 15 tokens per session scan A b4fbc02a05b4
button-check is a skill published in the GitHub repository MigoXLab/webqa-agent (234 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 2,185 once invoked, about $0.0001 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 2026-08-30.
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