跳到正文
cccskills
免费在 GitHub 公开

e2e-flow-verifier

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.

中文简介正在准备中。当前显示原文说明。

proffesor-for-testing/agentic-qe星标 515(GitHub 515 + cccskills 0)
集成工具Playwright

实测评分 未实测

查看安装方法

包含的文件(1)

  • SKILL.md4.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

E2E Flow Verifier

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.

Activation

/e2e-flow-verifier [flow-name]

Dependency

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.

Flow Verification Pattern (qe-browser + batch + assert)

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.

Evidence Collection

After each flow verification, vibium record produces a ZIP containing:

  1. Screenshots — one per action + annotated failure shots
  2. DOM snapshots — before/after each interaction
  3. Timeline — ordered event log with URLs and timings
  4. Console/network logs — captured inline

Use vibium har-export for a separate HAR 1.2 network log if needed.

Asserting backend state alongside UI

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).

Common Flows to Verify

FlowStepsCritical Assertions
Sign-upRegister → Verify email → Loginurl_contains /dashboard, no_failed_requests
PurchaseBrowse → Add to cart → Checkout → Payresponse_status /api/orders 201, text_visible "Thank you"
ProfileLogin → Edit profile → Savevalue_equals on the reloaded form, no_console_errors
SearchEnter query → Filter → Select resultelement_count .result >= 1, text_visible <query>

Reusing auth state across runs

# 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"

Gotchas

  • Re-map after DOM changes — Vibium @e1 refs are invalidated by navigation; use vibium diff map to see what changed and re-map if needed.
  • Selectors break on deployment — prefer data-testid over CSS classes.
  • Auth tokens expire — use fresh login per run, or rotate storage snapshots.
  • Evidence ZIPs grow — keep failure runs only in CI, gc after N days.
  • No raw Playwright — this skill used to use @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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

a11y-ally

免费

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。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

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。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

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。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

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。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

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。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

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。

proffesor-for-testing/agentic-qe5152026年10月26日 更新

查看 proffesor-for-testing 的全部技能

报告此技能的问题