Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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Use when verifying complete user flows end-to-end with the qe-browser skill (Vibium), recording session evidence, and asserting state at each step. For product verification with real browser automation.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Product verification skill that drives user flows with the qe-browser fleet skill (Vibium engine), asserts state at each step, and records evidence as a ZIP of screenshots + DOM snapshots.
/e2e-flow-verifier [flow-name]
This skill uses .claude/skills/qe-browser/ for all browser automation. The vibium binary is installed automatically by aqe init. See qe-browser/SKILL.md for the full command reference.
Define the flow as a JSON batch plan and drive it with the qe-browser batch runner:
# flows/{{flow-name}}.json
cat > /tmp/{{flow-name}}.json <<'EOF'
[
{"action": "go", "url": "{{base_url}}"},
{"action": "wait_load"},
{"action": "fill", "selector": "[data-testid=email]", "text": "{{test_user}}"},
{"action": "fill", "selector": "[data-testid=password]", "text": "{{test_password}}"},
{"action": "click", "selector": "[data-testid=login-btn]"},
{"action": "wait_url","pattern": "/dashboard"},
{"action": "assert", "checks": [
{"kind": "url_contains", "text": "/dashboard"},
{"kind": "selector_visible","selector": "[data-testid=dashboard]"},
{"kind": "no_console_errors"},
{"kind": "no_failed_requests"}
]},
{"action": "click", "selector": "[data-testid={{action_element}}]"},
{"action": "assert", "checks": [
{"kind": "text_visible", "text": "{{expected_text}}"}
]}
]
EOF
# Record evidence AND drive the flow
vibium record start --screenshots --snapshots --name "{{flow-name}}"
node .claude/skills/qe-browser/scripts/batch.js --steps "@/tmp/{{flow-name}}.json"
FLOW_EXIT=$?
vibium record stop -o "test-results/{{flow-name}}/evidence.zip"
exit $FLOW_EXIT
The batch runner exits non-zero if any step fails, so CI gates work with a plain $? check.
After each flow verification, vibium record produces a ZIP containing:
Use vibium har-export for a separate HAR 1.2 network log if needed.
The qe-browser assert.js check kinds include network assertions that capture backend calls made from the page:
node .claude/skills/qe-browser/scripts/assert.js --checks '[
{"kind": "response_status", "url": "/api/{{resource}}", "status": 200},
{"kind": "request_url_seen", "url": "/api/{{resource}}"}
]'
For API calls that don't originate from the page, run the API check in a separate step (curl + jq, or a dedicated API test skill).
| Flow | Steps | Critical Assertions |
|---|---|---|
| Sign-up | Register → Verify email → Login | url_contains /dashboard, no_failed_requests |
| Purchase | Browse → Add to cart → Checkout → Pay | response_status /api/orders 201, text_visible "Thank you" |
| Profile | Login → Edit profile → Save | value_equals on the reloaded form, no_console_errors |
| Search | Enter query → Filter → Select result | element_count .result >= 1, text_visible <query> |
# Once: log in and save state
vibium go "{{base_url}}/login"
vibium fill "[data-testid=email]" "$USERNAME"
vibium fill "[data-testid=password]" "$PASSWORD"
vibium click "[data-testid=login-btn]"
vibium wait url "/dashboard"
vibium storage -o test-results/auth/state.json
# Every subsequent run
vibium storage restore test-results/auth/state.json
vibium go "{{base_url}}/dashboard"
@e1 refs are invalidated by navigation; use vibium diff map to see what changed and re-map if needed.data-testid over CSS classes.storage snapshots.@playwright/test. If you need Playwright's network interception (page.route), use it in a separate skill and call it as a step; don't reintroduce the dependency here.还没有评价。欢迎分享你的使用感受。
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
中文简介正在准备中。当前显示原文说明。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
中文简介正在准备中。当前显示原文说明。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
中文简介正在准备中。当前显示原文说明。
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
中文简介正在准备中。当前显示原文说明。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
中文简介正在准备中。当前显示原文说明。
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.
中文简介正在准备中。当前显示原文说明。