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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
実行スコア 未検証
インストール方法を見るインストールする前に、エージェントに与えられる指示の中身を確認できます。
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.まだレビューはありません。使ってみた感想をお寄せください。
ログインなしで書き込めます。使ってみた感想、質問、動かなかった環境などを気軽にどうぞ。
まだコメントはありません。最初のコメントを書いてみませんか。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。